Resources Archives

November 11–12, 2026 | Radisson Blu Aqua | Chicago, Illinois

SAP transformation reaches far beyond the ERP system. A change to one application can affect the integrations, data, and business processes that teams rely on every day. For organizations moving toward S/4HANA, adopting clean core strategies, or exploring AI, understanding that impact is an essential part of moving forward.

That’s why we’re looking forward to joining the SAP community at SAPinsider Summit Chicago this November.

Taking place November 11–12 at the Radisson Blu Aqua, the summit centers on “SAP ERP Transformation: Moving from Clean Core to an AI-Powered Future.” It brings SAP practitioners together for two days of sessions and peer conversations focused on transformation planning, clean core execution, and the next stage of enterprise AI.

The Business Processes Behind the Transformation

For SAP teams, modernization brings practical questions. How will a change affect an existing workflow? Which integrations need validation? Can the team expand regression coverage without pulling business users into another lengthy manual testing cycle?

These questions become especially important when processes span SAP and connected applications. An order might begin in a customer portal, move through SAP, and continue into warehouse and delivery systems. Testing each application individually can leave gaps in the journey that matters to the business.

As organizations rethink their SAP landscapes, quality engineering needs to account for those connections. Teams need a clear view of dependencies, repeatable ways to validate critical processes, and evidence that changes behave as expected before release.

Where AI-Driven Quality Fits

At QyrusAI, these are the challenges we’re focused on helping enterprises address.

QyrusAI brings together capabilities across Modernize, Assure, and Operate, connecting environment discovery, automated testing, and operational insight. Its SAP testing capabilities sit alongside web, mobile, API, and data testing to support validation across connected enterprise workflows.

For the conversations in Chicago, we’re especially interested in how teams are approaching regression testing during S/4HANA transformation, maintaining automation as applications evolve, and validating processes that cross system boundaries.

AI adds another dimension to that discussion. As teams explore new ways to automate work and support decisions, they also need ways to evaluate outcomes and maintain visibility into how changes affect the business. Building quality into that journey gives teams a stronger foundation for adopting new capabilities.

Join Our Sessions on November 11

QyrusAI will be hosting two sessions on Wednesday, November 11, with Sujit Srivastava speaking at both:

Customer Case Study | 10:50–11:30 a.m.

In-Booth Session | 3:05–3:20 p.m.

Add both to your event schedule and join Sujit for a closer look at QyrusAI. We’re looking forward to sharing our perspective, hearing your questions, and connecting throughout the day.

Learning From the SAP Community

One of the reasons we’re looking forward to SAPinsider Chicago is the opportunity to hear directly from the people doing this work.

Every organization brings a different landscape, set of priorities, and stage of transformation. Some teams are planning their next major move. Others are managing ongoing releases or deciding where AI can deliver practical value. Sharing those experiences helps surface the details that shape successful projects: testing responsibilities, integration dependencies, maintenance effort, and the processes that need the most attention.

We’re looking forward to exchanging ideas with SAP leaders, architects, business process owners, and quality teams about how they’re navigating those decisions.

See You in Chicago

If you’re attending SAPinsider Summit Chicago, we’d love to connect. Join Sujit for our customer case study, stop by our in-booth session, or come share what your team is working on.

Whether you’re working through S/4HANA transformation, strengthening SAP test automation, or exploring how AI can support quality engineering, we’re looking forward to the conversation.

We’ll see you in Chicago this November.

Customer Background

A global high-tech manufacturing enterprise operating primarily through on-premise data centers faced challenges in understanding the current state of its IT infrastructure. The complexity of legacy systems, unknown interdependencies, and lack of up-to-date asset inventory made it difficult to plan for an upcoming network modernization initiative.

Problem Statement

The customer needed to:

Discover and catalog all devices across their enterprise network.

Understand the state, configuration, and interdependencies of these assets.

Understand the serviceability of the devices from the ITSM perspective.

Create a reliable foundation to plan and execute a network infrastructure upgrade.

Achieve this with minimal disruption and no dependency on outdated CMDB entries or manual discovery.

QyrusAI Solution

Automated IT Discovery Framework

QyrusAI deployed its Auto-Discovery Framework using lightweight Remote Agents installed within the customer’s segmented networks. This setup enabled deep visibility into the IT ecosystem without requiring firewall changes or intrusive scanning tools. Additionally, it correlated discovered assets with their associated incident history by integrating with the ServiceNow ITSM tool, adding a behavioral dimension to infrastructure visibility.

Technical Approach & Techniques

Technical Highlights:

No VPN or tunneling required: QyrusAI Agents communicate securely over HTTPS.

Distributed Architecture: Each Remote Agent operated independently within local segments.

Real-time Updates: Incremental and continuous synchronization kept the asset inventory up to date.

Smart Deduplication: Merged partial data from multiple sources to guarantee high accuracy.

Furthermore, QyrusAI matched discovered assets with CI references in ServiceNow (via IP, hostname, or asset tag). It classified discovered devices as Stale Assets, Noisy Assets, or Critical with High Noise Assets by applying temporal clustering to identify ticket bursts and recurring issues per asset.

Outcomes & Business Impact

Technical Outcomes

2,500+ networked assets discovered, including servers, switches, routers, storage, and virtual appliances.

Mapped 85%+ of known and unknown dependencies within the first 3 weeks.

Identified 43 orphaned or end-of-life devices impacting overall network performance.

Uncovered surface-level insights into traffic bottlenecks and misconfigured gateways.

Identified top 10 noisy devices accounting for over 40% of recurring incidents.

Recommended actionable remediation or replacement plans for unstable devices.

Enabled rapid root cause triage by linking incidents to specific dependencies.

Improved CMDB accuracy by validating discovered assets directly against ticket data.

Business Value

“With QyrusAI, we got a living blueprint of our IT ecosystem. What would have taken months manually was achieved in a few days with actionable insights.” – IT Infrastructure Head, Customer Org

Strategic Takeaways

Discovery is the first mile of IT modernization, as any transformation plan risks being flawed or incomplete without knowing what exists and how it connects.

Agent-based, real-time discovery is crucial for hybrid and legacy-heavy environments where traditional CMDBs fall short.

Business alignment is driven not just by visibility, but by contextual intelligence—knowing not just what an asset is, but what role it plays.

Conclusion

By replacing traditional, manual IT audits with QyrusAI’s automated discovery and behavioral tracking, the enterprise successfully eliminated operational blind spots and streamlined its ITSM workflows. The engagement delivered a reliable, living digital blueprint that established a solid foundation for a risk-managed, efficient network modernization journey.

Ready to Accelerate Your IT Modernization?

Discover how QyrusAI can bring total visibility, intelligence, and control to your enterprise architecture.

The Comfort of Seeing Everything

Over the years, IT teams have made real progress. There was a time when visibility itself was the problem. Systems were opaque, issues were discovered late, and teams operated with limited awareness.

That’s no longer the case. Today, most enterprises have dashboards, alerts, metrics, and monitoring tools that provide a constant stream of information. On the surface, it feels like control. You can see everything—or at least, it seems that way.

When Something Breaks, the Real Question Emerges

And then something goes wrong:

A system slows down.

An API starts failing.

A service becomes unavailable.

Alerts are triggered instantly and teams jump into action; but within minutes, the conversation shifts. Not toward the system—but toward the business, and the questions that begin to emerge are:

Is this affecting customers?

Are transactions failing?

How critical is this right now?

What is the business impact?

And that’s where things become less clear, because while the system issue is visible, the business impact is not.

The Gap Between Signals and Meaning

This is the gap most organizations don’t explicitly talk about. They have signals, data, and visibility, but they lack meaning due to lack of context.

An alert might tell you that something is wrong with a service. It rarely tells you what that service means to the business. In fact, research shows that 44% of IT incidents stem from detectable failures—technical issues that telemetry actively tracked, but teams failed to interpret or prioritize in time.

Worse yet, 47% of outages are reported by customers before internal monitoring catches them. When dashboards flash green while users struggle to check out or log in, teams are left to interpret, correlate, and guess under pressure. And in that time, critical decisions are delayed.

Why More Data Doesn’t Solve the Problem

The instinctive response to this gap is usually the same:

Add more data.

Add more tools.

Add more dashboards.

But the problem isn’t the absence of data. It’s the absence of connection. Modern IT environments are not just collections of systems; they are complex networks of dependencies.

A single business workflow may span multiple applications, microservices, cloud infrastructure, and third-party APIs. Unless those relationships are dynamically understood, no amount of raw data can provide operational clarity.

When Everything Looks Important, Nothing Is Prioritized

Without business context, every alert feels urgent and every issue looks critical. Every team believes their technical problem should be addressed first, creating a subtle but dangerous dynamic.

Efforts are spread thin, focus is diluted, and issues that truly move the needle sometimes wait longer than they should. Because teams lack a clear view of business dependencies, 70% to 80% of Mean Time to Resolution (MTTR) is consumed purely by investigation and Mean Time to Identify (MTTI)—manually hunting through logs, assembling war rooms, and figuring out which service broke what workflow.

The Cost of Misalignment

This isn’t just an operational inefficiency. It carries severe, measurable consequences:

Critical business services remain impacted longer than necessary: The median cost of enterprise downtime has escalated to $15,000 per minute ($900,000 per hour), with 91% of enterprises reporting that a single hour of downtime costs over $300,000.

Customer experience degrades without immediate visibility: The true fallout extends far beyond recovery costs. Organizations face 3x to 5x hidden costs, and 81% of technology leaders cite customer loss and churn as the most damaging consequence of an outage.

Incident severity continues to rise: Even as component reliability improves, interconnected architectures mean that 1 in 5 organizations (20%) report their most recent major outage cost over $1 million.

Trust slowly erodes along with enterprise value: When IT cannot clearly communicate business impact, cross-functional alignment fractures and leadership is left flying blind.

From Visibility to Understanding

The shift that organizations need is not from less visibility to more. What they need is to move from passive visibility to contextual understanding.

Understanding how systems support business services and how failures propagate across interdependent entities of a business process defines how effectively you respond. It transforms an ambiguous technical alert into an immediate understanding of what truly matters to the enterprise at any given moment.

Where QyrusAI Makes the Difference

QyrusAI bridges this exact gap. As an agentic platform designed to unify the enterprise application lifecycle, QyrusAI connects systems directly to business services, providing the missing layer of contextual intelligence.

At the center of this approach is QyrusAI’s Live Enterprise Knowledge Graph, which continuously discovers enterprise assets, maps dynamic dependencies, and binds technical telemetry directly to business workflows and transactions.

This continuous intelligence powers a connected lifecycle across three core pillars:

Modernize: Uncovers application dependencies, illuminates technical debt, and rationalizes complex portfolios with live architectural context.

Assure: Provides automated end-to-end testing across web, mobile, API, SAP, and data pipelines to validate changes before they reach production.

Operate: Delivers full-spectrum BizOps, AIOps, FinOps, and automated incident response, connecting technical signals to customer journeys and business outcomes.

So when something happens, teams don’t just see an isolated alert. They understand it, they know what business service it affects, and they can prioritize and resolve it with complete confidence.

Frequently Asked Questions (FAQs)

Why is component-level visibility no longer enough for modern enterprise IT?

Modern architectures are non-linear and deeply interdependent. A system or microservice can report healthy technical metrics in isolation while an end-to-end transaction (like customer checkout or loan processing) fails across the broader dependency chain.

What is the difference between observability and Business Operations Intelligence (BizOps)?

Observability focuses on capturing telemetry—metrics, events, logs, and traces—to explain technical system states. BizOps and Business Operations Intelligence connect that telemetry directly to business workflows, revenue streams, and customer journeys, translating technical health into business outcomes.

How does QyrusAI prevent alert fatigue during high-severity incidents?

Using its Live Enterprise Knowledge Graph, QyrusAI correlates disparate infrastructure alarms to their underlying business services. Instead of bombarding engineers with hundreds of isolated alerts, it pinpoints the root cause and ranks incidents by actual business and financial exposure.

How does connecting IT to business context accelerate MTTR?

Because 70% to 80% of MTTR is spent on investigation and MTTI, eliminating manual dependency tracing drastically reduces resolution time. Teams immediately see which business service is degraded, which components are responsible, and who needs to take action.

The Bottom Line

Visibility is no longer the differentiator. Most organizations already have it.

The real question is

Do you understand what you’re seeing?

Because that’s what separates mere awareness from true control.

Ready to move beyond dashboards and understand real business impact?

Discover how QyrusAI connects your systems to business outcomes—so your teams don’t just see issues, they understand what matters.

Talk to an Expert

There’s a common assumption in enterprise AI: if you have enough data, AI will work.

But in reality, that’s rarely the case.

The Missing Ingredient Isn’t Data

Most organizations today have:

Vast amounts of data

Modern data platforms

Advanced AI tools

And yet, many enterprise AI initiatives fail to deliver expected business outcomes.

Why?

Because data without context is not intelligence.

The Problem with Disconnected Data

Enterprise data is spread across:

Applications

Infrastructure

Cloud platforms

Business systems

Each dataset exists in isolation, and while data may be accurate, its meaning is often incomplete.

When algorithms ingest fragmented data, they lack the structural relationships that govern how an enterprise actually operates. A comprehensive study by the RAND Corporation found that over 80% of AI projects fail—more than double the failure rate of non-AI corporate technology projects—with researchers identifying a failure to ground models in operational context as the primary culprit. Furthermore, according to NewVantage Partners’ executive benchmark, only 29% of corporate leaders report achieving measurable business outcomes from their AI investments, pointing directly to persistent architectural silos and missing contextual relationships.

Why Context Matters

For AI to work effectively, it needs to understand:

Relationships between systems

Dependencies across processes

How data flows through the organization

Without this:

Insights lack relevance

Predictions lack accuracy

Decisions lack trust

When context is absent, teams burn enormous resources attempting to compensate with brute-force data engineering. Industry analysts at Gartner predict that at least 30% of generative AI projects will be abandoned after proof of concept due to poor data quality, missing risk controls, escalating costs, or unclear business value. Simultaneously, data scientists and engineers still spend up to 80% of their working hours manually preparing, cleaning, and reconstructing disconnected context rather than training and optimizing intelligent models.

From Data to Understanding

The shift organizations need is clear: moving from simply collecting data lakes to understanding how data is dynamically connected across systems, workflows, and business outcomes.

This is where concepts like:

Enterprise knowledge graphs

System-level dependency mapping

Real-time digital twins

become critical.

Context provides the semantic anchor that bridges raw technical data and business intelligence. It transforms an isolated metric or log entry into a living relationship: which application produced it, which downstream API consumes it, and which revenue-generating customer journey depends on it.

The Role of Context in AI

When AI is powered by continuous context:

Insights become more accurate

Decisions become more reliable

Outcomes become more actionable

Because the system understands not just what is happening, but why. Context provides the necessary guardrails that eliminate hallucinations, anchor agentic reasoning in verified ground truth, and give human stakeholders the transparency needed to trust algorithmic recommendations.

Where QyrusAI Fits In

This is where QyrusAI introduces a connected approach across the enterprise application lifecycle. As an agentic platform, QyrusAI creates a real-time, unified representation of your enterprise to ground AI systems in operational reality.

At the center of this capability is QyrusAI’s Live Enterprise Knowledge Graph, which continuously discovers enterprise assets, monitors live system interactions, and maps dependencies across services, databases, APIs, and business workflows.

By combining:

Continuous asset discovery

Dynamic dependency mapping

Knowledge graph intelligence

QyrusAI provides the continuous context layer that enterprise AI systems require[cite: 2]. This intelligence is operationalized across three core pillars:

Modernize: Unpacks legacy architectures, assesses technical debt, and models real-time dependencies so modernization and portfolio decisions are driven by complete system context.

Assure: Delivers continuous, automated testing across web, mobile, API, SAP, and complex data pipelines, verifying data contract integrity and application behavior before changes reach production.

Operate: Brings operational intelligence across AIOps, observability, BizOps, and FinOps, directly correlating technical signals and operational anomalies with business outcomes.

This enables organizations to:

Build more reliable, hallucination-resistant AI models

Improve explainability and governance across automated decisions

Accelerate time-to-value from AI investments

Frequently Asked Questions (FAQs)

Why do enterprise AI projects fail even with massive data lakes?

Data volume is not the same as data understanding. Data lakes store raw data in isolated silos without architectural or business relationships. Without contextual mapping, AI models cannot determine which processes, transactions, or services that data actually influences.

What is an Enterprise Knowledge Graph, and why does AI need it?

An Enterprise Knowledge Graph is a dynamic, semantic model that connects disparate data assets, applications, and workflows based on real-world relationships. It provides AI models with deterministic context, preventing hallucinations and enabling autonomous agents to reason accurately about complex systems.

How does QyrusAI provide context to enterprise operations?

QyrusAI uses continuous discovery and automated dependency mapping to build a Live Enterprise Knowledge Graph. This connects raw telemetry, data pipelines, and infrastructure to the specific business workflows they support.

How does contextual intelligence improve AI governance and explainability?

When AI makes a recommendation or triggers an automated remediation, context reveals the exact dependency path and reasoning behind the decision. This transparency makes models auditable, safe, and trustworthy for enterprise leadership.

The Bottom Line

AI doesn’t fail because of a lack of data. It fails because of a lack of context.

The question that helps you separate experimentation from real impact is no longer:

“Do we have enough data for AI?”

It is:

“Do we understand our data well enough for AI to work?”

Ready to move from disconnected data to contextual intelligence?

Discover how QyrusAI connects your data, systems, and business outcomes into a living operational Knowledge Graph.

Talk to an Expert

The Cost You Can Measure and the Cost You Can’t

Enterprises are very good at measuring certain things like:

Infrastructure spend.

Cloud usage.

Licensing costs.

These numbers are visible, trackable, and often discussed. But there’s another category of cost that rarely appears clearly on any dashboard and that is the cost of not understanding business impact.

When an Incident Happens

Consider a typical incident. An application slows down, an alert is triggered and teams begin investigating.

But before anything else, there’s uncertainty.

Is this affecting a critical service?

Are customers impacted?

Is revenue at risk?

And until these questions are answered, the response remains cautious. Or at worst misdirected.

The Cost of Delay

In many cases, the biggest cost is not the incident itself. It’s the time taken to understand its impact. Because during that time:

Teams are aligning across functions

Information is being gathered manually

Decisions are being deferred

And all of this adds up, minutes turn into hours and the impact grows.

Industry benchmarks indicate that 70% to 80% of Mean Time to Resolution (MTTR) is spent solely on investigation and Mean Time to Identify (MTTI)—manually tracing dependencies, gathering logs, and determining what broke. When the median enterprise cost of downtime has climbed to approximately $15,000 per minute ($900,000 per hour), every minute spent searching for context in an active war room translates directly into catastrophic financial exposure. Furthermore, 91% of enterprises report that a single hour of IT downtime costs them more than $300,000.

When Effort Doesn’t Match Importance

Without clear business context, teams often fall into a common pattern. They respond to what is visible, not what is important. A technically complex issue may get immediate attention while a business-critical issue, if less visible, may take longer to surface. This misalignment is subtle but over time, it becomes expensive for the business.

Research reveals that 44% of IT incidents stem from detectable failures—technical issues that telemetry monitored, but teams failed to interpret or prioritize correctly in time. Worse yet, 47% of outages are reported by customers before internal monitoring catches them, exposing how traditional component alarms leave operational teams blind to real customer journeys and transactional bottlenecks.

The Ripple Effect Across the Organization

The lack of business impact visibility doesn’t just affect IT. It affects:

Business teams waiting for updates

Leadership trying to make decisions

Customers experiencing disruptions

And because the impact is not clearly understood, communication becomes fragmented. Different teams operate with different assumptions. The result is an accelerated sense of chaos.

The true toll extends far beyond immediate remediation fees. Studies indicate that enterprises face 3x to 5x hidden costs—encompassing lost cross-departmental productivity, regulatory penalties, and brand equity damage. In fact, 81% of technology leaders cite customer loss and churn as the most damaging long-term consequence of operational downtime.

Why This Problem Persists

Most organizations do not intentionally ignore business impact. They simply lack a way to see it in real time. This is because systems and business services are not clearly connected. Without that connection, the true nature and quantum of impact has to be guessed and this is both often inaccurate and risky.

This blind spot is a primary driver behind why 1 in 5 organizations (20%) report that their most recent major outage exceeded $1 million in total losses. Across the Global 2000, unplanned downtime siphons an estimated $400 billion annually—not because telemetry was missing, but because isolated signals lacked structural and commercial context.

From Reaction to Clarity

The organizations that handle this well do something differently. They don’t wait to understand impact after an incident. They already know it. Because their systems are mapped to business services, the dependencies are visible and the context is always available. This helps to avoid decisions that come from inaccurate guesswork.

Where QyrusAI Fits In

QyrusAI enables this shift by connecting systems to business services and continuously mapping dependencies, allowing organizations to understand impact instantly.

As an agentic platform designed to unify the enterprise application lifecycle, QyrusAI grounds operational workflows across three core pillars:

Modernize: Unpacks legacy architectures, illuminates hidden technical debt, and rationalizes application portfolios with comprehensive dependency context.

Assure: Delivers continuous, automated testing across web, mobile, API, SAP, and data pipelines to catch defects long before code reaches production.

Operate: Brings actionable operational intelligence across AIOps, observability, BizOps, and FinOps, directly correlating infrastructure alerts with customer journeys and business outcomes.

At the core of the platform is QyrusAI’s Live Enterprise Knowledge Graph, which maintains a continuously observed, real-time model of applications, integrations, and business processes.

So instead of asking:

“What does this affect?”

Teams already know. And instead of delaying decisions, they act immediately with clarity.

Frequently Asked Questions (FAQs)

What is the hidden cost of IT downtime?

The hidden cost refers to secondary and long-term damages—such as lost employee productivity, brand erosion, missed sales opportunities, regulatory penalties, and customer churn. Research shows these indirect damages are typically 3x to 5x higher than the immediate direct costs of system recovery.

Why does traditional IT monitoring fail to identify business impact?

Traditional monitoring tracks component-level telemetry (CPU, memory, server uptime, network packets) in isolation. It cannot determine whether a 500ms database delay affects a non-critical background task or halts an active checkout flow for high-tier enterprise clients.

How does QyrusAI establish real-time business context?

QyrusAI utilizes its Live Enterprise Knowledge Graph to continuously discover assets, map dynamic dependencies, and bind technical telemetry directly to business services, workflows, and KPIs. This ensures teams see the commercial blast radius of any incident in real time.

How does understanding business impact reduce Mean Time to Resolution (MTTR)?

Because 70% to 80% of MTTR is typically spent on investigation (MTTI), providing immediate topological and business context eliminates the guesswork. Teams instantly know which service is broken, which business workflow is impacted, and who needs to remediate it.

The Bottom Line

The cost of not understanding business impact is not always visible, it shows up in:

Delayed responses

Misaligned priorities

Increased disruption

The real question to ask here is:

How much is uncertainty costing you today?

Still reacting before understanding impact?

See how QyrusAI helps you identify, prioritize, and act on what truly matters to your business in real time.

Talk to an Expert

Technology spend is no longer predictable by looking at last month’s bill. Cloud, SaaS, on-premises infrastructure, and AI workloads now move too quickly, consume too differently, and depend on too many disconnected systems for traditional FinOps reporting to keep up.

This whitepaper explains why enterprises must move beyond visibility and build an architecture for cost predictability — one that connects billing data with telemetry, attribution, dependency context, AI usage signals, and decision intelligence.

Why Cost Visibility Should Be Your Priority

Most enterprises can see what they spent. Far fewer can explain why spend changed, which teams or applications drove the increase, or what will happen before the next invoice arrives.

The gap is not just a tooling issue. It is a data architecture issue. Predictable technology spend requires more than dashboards. It requires a connected model that understands usage, ownership, dependencies, utilization, AI token economics, and planned engineering change.

98%

of FinOps practitioners now manage AI spend.

90%

manage SaaS spend as FinOps expands beyond cloud.

29%

estimated IaaS/PaaS waste in 2026, driven by growing workload complexity.

What You’ll Learn in This Whitepaper

Why historical cost forecasting fails in dynamic cloud, SaaS, hybrid, and AI environments

How AI workloads introduce new attribution challenges through tokens, agents, retries, reasoning steps, and shared API usage

The five root causes of technology cost unpredictability

Why billing data alone cannot support finance-grade forecasting

How QyrusAI combines cost data, telemetry, attribution, dependency mapping, and AI intelligence into a cost predictability architecture

How enterprises can detect anomalies, explain cost drivers, and take action before spend becomes a month-end surprise

Built for Leaders Who Need Predictable Technology Spend

This whitepaper is designed for CTOs, CIOs, FinOps leaders, cloud leaders, engineering executives, finance stakeholders, and technology operations teams responsible for governing spend across modern, multi-source technology estates.

How QyrusAI Helps Enterprises Move from Visibility to Predictability

QyrusAI helps enterprises unify technology cost data across cloud, SaaS, on-premises infrastructure, licenses, and AI providers. By enriching billing data with telemetry, utilization signals, dependency maps, and AI usage instrumentation, QyrusAI turns cost reporting into cost intelligence.

Unified cost data: Normalize spend across cloud, SaaS, AI providers, software licensing, and on-premises infrastructure.

Telemetry-enriched attribution: Connect cost to real usage, resource efficiency, application behavior, and ownership.

AI-powered forecasting: Forecast spend at enterprise, business unit, application, service, and resource levels.

Anomaly intelligence: Detect cost deviations, identify root causes, route them to the right owners, and recommend actions.

AI cost attribution: Track token, model, feature, agent, retrieval, and retry-level consumption to understand AI unit economics.

Change impact simulation: Understand the financial effect of infrastructure, workload, and AI architecture changes before they happen.

Explore QyrusAI for FinOps

Learn how enterprises can build a cost predictability architecture for the AI era — and how to give Finance and Engineering a shared, actionable view of what spend is doing, why it is changing, and what can be done before the cost occurs.

October 26, 2026 | Madison, Wisconsin

Every SAP change comes with a practical question: will the business processes people rely on still work as expected?

That’s a conversation worth having in person. QyrusAI is heading to Madison, Wisconsin, on October 26, 2026, for the ASUG Wisconsin Chapter Fall Meeting and Social to explore how AI can help teams approach SAP change with greater confidence.

From planning modernization initiatives to testing everyday updates, SAP teams have plenty to balance. A change in one application can affect integrations, data, and workflows across the business. Understanding those connections—and validating them—is essential to moving forward.

QyrusAI brings application discovery, AI-powered testing, and operational intelligence together to help teams see the bigger picture. In Madison, we’re looking forward to conversations about:

Understanding dependencies across SAP and connected applications.

Strengthening testing for critical, end-to-end business processes.

Reducing repetitive manual work as systems evolve.

Connecting modernization decisions with software quality and operational reliability.

We also want to hear your side: what’s taking too long, where testing gets complicated, and what your team hopes to improve next.

If you’ll be joining the ASUG community in Madison, let’s connect. Bring your questions and your toughest SAP testing challenges—we’d love to talk them through.

See you in Madison on October 26!

Most enterprise IT environments weren’t designed. They accumulated. A system that ran fine on its own five years ago has since picked up two integrations, a merger’s worth of inherited applications, and a handful of workarounds nobody documented. None of that shows up as a problem, until someone tries to change it. 

That’s the real challenge behind modernization: it’s rarely about deciding whether something needs updating. It’s about knowing what else gets affected when it is. Application discovery and dependency mapping is how enterprise architects, IT operations teams, and CIOs answer that question, by building an accurate picture of what’s actually running across an environment and how each piece depends on the rest, before anything gets touched. 

What Application Discovery and Dependency Mapping Actually Means 

Application discovery and dependency mapping is really two linked steps. Discovery identifies what exists — the applications, services, servers, and infrastructure running across an environment. Dependency mapping traces how those pieces connect: which service calls which database, which application sits on top of which infrastructure, and which business process breaks if a given component goes down. 

That second part carries more weight than it first appears to. A relationship just means two systems exchange traffic, they talk to each other. A dependency is directional: it means one system’s failure will break another. A payment gateway and an authentication service might have a relationship in a topology diagram, but if the authentication service fails, the payment gateway is the one that stops working, not the other way around. Knowing which direction that failure travels is what determines who gets paged first during an incident, and what teams prioritize before a planned change. 

Static inventories and manually maintained configuration management databases (CMDBs) were built for slower-moving environments. They capture what’s there at a point in time, but they don’t hold up once infrastructure starts changing weekly instead of quarterly, which is now the norm, not the exception, for organizations running hybrid cloud and containerized workloads. 

Why This Is Harder Than It Used to Be 

Enterprise environments have gotten more complex faster than the tools tracking them have kept pace. A typical environment today spans hybrid cloud infrastructure, containerized microservices, distributed APIs and SaaS applications, and infrastructure that scales continuously rather than on a fixed schedule. Layer in a few acquisitions or a legacy system nobody’s fully documented, and the gap between what’s on paper and what’s actually running starts to widen. 

The numbers back this up. Gartner has found that only 25% of organizations extract meaningful value from their CMDB investments, the rest are working from data that’s incomplete, stale, or simply not trusted enough to act on. Separately, Forrester research cited by Virima found that 54% of IT professionals report unknown assets in their environments, systems nobody currently tracks are still running, still connected to something else, and still capable of breaking a change nobody saw coming. 

That visibility gap has a direct line to project risk. Application dependencies are one of the most commonly cited concerns IT teams raise about cloud migration projects, and budget overruns tied to unplanned rework are common enough that they show up in nearly every year’s IT leadership surveys. Periodic discovery scans and manually updated spreadsheets can’t keep up with environments that change this often, which is why the shift underway across the industry is toward continuous, automated discovery instead. 

From Discovery to a Living Model: The Knowledge Graph 

A one-time discovery scan produces a snapshot, accurate the day it’s run, and progressively less accurate after that. Continuous discovery instead produces something closer to a living model: infrastructure changes get detected automatically, dependencies stay current as the environment evolves, and the resulting map doesn’t need to be manually reassembled every time someone asks for it. 

The more useful shift is what happens to that discovery data next. Rather than sitting in a dashboard as a point-in-time diagram, discovered assets and their dependencies can feed into a persistent knowledge graph, a structured, queryable model of the environment that stays current as things change. That’s a meaningfully different architecture than “run a scan, export a report.” It turns discovery into infrastructure other systems can build on: incident response tools that can trace an issue back through its dependency chain automatically, planning tools that can simulate the impact of a proposed change, and automation that can act on accurate, current context instead of a document that was correct six months ago. 

Put together, the pattern looks like a pipeline rather than a single tool: discovery identifies what’s running, dependency mapping traces how it connects, a knowledge graph holds that model in a form other systems can query, and from there, automated and AI-driven operations become possible because they’re finally working from something trustworthy. Skip the first two steps, and everything built on top of them inherits the same blind spots. 

Where This Shows Up: Cloud Migration 

Cloud migration is where the cost of skipping discovery becomes concrete fastest. Moving an application to the cloud is rarely just moving the application. It may depend on a database that isn’t moving on the same timeline, communicate with a service that stays on-premises, or support a larger business process that spans systems nobody thought to check. 

If those dependencies surface after the move, the project has a problem: a service that worked fine in testing breaks in production because something it depended on didn’t come along or came along on a different schedule. If they surface beforehand, the same information becomes a planning input instead of an incident. Teams can sequence the migration around what actually needs to move together, flag the dependencies that need special handling, and set expectations before a cutover date gets missed. 

This is also where discovery connects to the rest of the modernization lifecycle rather than standing apart from it. The same dependency data that de-risks a migration is what later informs application rationalization — deciding which systems are redundant or safe to retire — and technical debt assessment, which starts from knowing what’s actually in the environment before deciding what to fix. Discovery isn’t a standalone exercise; it’s the input every later modernization decision draws on. 

How QyrusAI Approaches Auto Discovery 

QyrusAI is an enterprise AI platform built around three workspaces — Modernize, Assure, and Operate — covering how organizations understand, test, and run their IT environments. Auto Discovery sits under Modernize, where it works alongside dependency mapping, application rationalization, and technical debt analysis to help enterprise teams understand what they’re working with before they change it. 

Auto Discovery continuously scans an organization’s IT environment across cloud, on-premises, and hybrid infrastructure, identifying what’s actually running, applications, services, infrastructure, and mapping the dependencies between them. It traces those relationships upstream and downstream and feeds the results into QyrusAI’s Enterprise Knowledge Graph, building a continuously updated model of the environment rather than a static diagram that has to be manually maintained. 

That shift from periodic to continuous, knowledge-graph-backed discovery shows up in the numbers: organizations applying this approach to their modernization lifecycle have seen a 25–40% reduction in migration timelines, 20–30% of legacy applications identified for rationalization, and a 98%+ migration success rate, driven by earlier, more accurate dependency visibility rather than late-stage surprises. The goal isn’t just a more complete inventory — it’s giving teams a foundation they can actually plan against before a project starts, not one they reconstruct after something breaks. 

Frequently Asked Questions 

What’s the difference between application discovery and dependency mapping? 

Discovery identifies what’s running in an environment — applications, services, infrastructure. Dependency mapping traces how those components connect and rely on each other. They’re typically delivered together because discovery data is what dependency mapping is built from. 

Is application discovery and dependency mapping the same as a CMDB? 

No. A CMDB is a database that stores configuration item records, often updated manually or on a schedule. Application discovery and dependency mapping is the process — increasingly automated and continuous — that keeps that data current and can feed directly into a CMDB rather than replace it. 

How often should IT environments be re-discovered? 

As close to continuously as the tooling allows. Environments that scale, deploy, or reconfigure weekly can go stale within days under a periodic scan model, which is why the industry trend is toward continuous, automated discovery rather than scheduled scans. 

Does application discovery work across hybrid and multi-cloud environments? 

Modern discovery tools are generally built to span cloud, on-premises, and hybrid infrastructure in a single pass, rather than requiring separate tools per environment. That unified view is what makes dependency mapping useful — a dependency that crosses environments is invisible if the discovery tool only sees one side of it. 

What happens if you skip dependency mapping before a migration? 

Dependencies tend to surface anyway — just later, and usually as an incident instead of a planning input. A database, service, or business process that wasn’t accounted for can break after a cutover, turning a planned migration into unplanned rework. 

Understand It Before You Change It 

Modernization projects don’t start with the first change — they start with understanding what’s already there. Application discovery and dependency mapping gives enterprise architects, IT operations teams, and CIOs the visibility to plan migrations, rationalize applications, and manage change with fewer surprises along the way. 

QyrusAI’s Auto Discovery continuously maps environments across cloud, on-premises, and hybrid infrastructure and feeds that model into the Enterprise Knowledge Graph — giving teams a current, connected view of their systems rather than a static inventory. Request a demo to see how QyrusAI Auto Discovery maps your environment before you modernize it.

At QyrusAI, we are dedicated to transforming how enterprises validate and assure their most complex application estates.

We are proud to announce that QyrusAI has been positioned as a Visionary in the October 2026 Gartner® Magic Quadrant™ for Agentic Software Quality Assurance Platforms authored by Ross Power, Sushant Singhal and C.A. Swan.

We believe this recognition reflects our deep commitment to addressing the emerging demand for validating not just traditional applications, but AI-generated software and the autonomous decision paths of the agents that create and operate them.

Our AI-agentic quality platform supports comprehensive testing across web, mobile, API, desktop, data, and LLM/RAG autonomous workflows.

Why We Believe QyrusAI Was Recognized:

Innovation in Agentic Capabilities: Our platform differentiates itself through investments in evaluating AI agents, RAG pipelines, and autonomous workflows.

The SEER Framework: Our proprietary Sense, Evaluate, Execute, and Report (SEER) framework automates test impact analysis, test selection, execution, and reporting across multi-application workflows.

QyrusAI Pulse/Gym and QHive Memory: The platform combines QyrusAI Pulse/Gym’s AI evaluation capabilities—assessing LLM outputs and agent behaviors—with QHive Memory to support context-aware testing and validation.

Bridging the Engineering Gap: Our unique positioning effectively targets traditional QA and engineering leaders alongside AI/ML teams responsible for validating LLM applications and agentic workflows.

Disclaimer:

Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.

What is an LLM Tool?

A LLM tool is like a application, interface, or integration that exposes the reasoning capabilities of a large language model to end users or downstream systems. It covers features right from a simple chatbot to an API to a fully autonomous agent that can browse the web, writes code, executes files, and completes multi-step workflows without human input in the loop. In simplest terms, every LLM tool shares three components: a language model (as AI reasoning engine), an interface layer (to interpret how humans or machines communicate with it), and an integration layer (how it connects to real data, APIs, and actions in the world).

Here’s something you should know

LLM vs AI Tool vs AI Agent

These three terms are used interchangeably in most conversations — and that confusion is expensive. An LLM, an AI tool, and an AI agent are fundamentally different things with different capabilities, different failure modes, and very different evaluation requirements. Understanding the distinction is the first step to building or deploying any of them responsibly.

Side-by-side comparison

	LLM	AI Tool	AI Agent
Acts on its own?	No	Within scope	Yes — until goal met
Has memory?	Context window only	Session-level	Persistent across steps
Uses tools?	No	Predefined set	Dynamic selection
Self-corrects?	No	No	Yes — reflects and retries
Eval complexity	Output scoring	Output + integration	Trajectory + goal + safety
Autonomy	Low	Medium	High

Why LLM Tools Are Different from Traditional Software

Traditional software is designed to be deterministic. So it can give the same input and it will always produce the same output. But LLM tools are built to be probabilistic — outputs are generated by sampling from a learned distribution over language. This single difference changes everything: testing, debugging, monitoring, and quality assurance all require entirely new paradigms. Traditional software fails loudly. It crashes, throws errors, returns null. LLM tools fail silently. They generate confident, grammatically correct, beautifully structured text that happens to be wrong, biased, or dangerous. This is why evaluation — the practice of measuring LLM output quality systematically — has become one of the most critical disciplines in modern AI engineering.

Why the Distinction Matters for Evaluation

The gap between these three categories is not just conceptual — it directly determines how hard evaluation is. An LLM needs output scoring: is this response accurate, relevant, safe? An AI tool needs integration accuracy on top: did it call the right API with the right parameters? An AI agent needs all of that plus trajectory evaluation: did it follow the right path, make the right tool choices at each step, and achieve the actual goal without unintended side effects? This is why QyrusAI is built as an end-to-end evaluation platform — not just an output scorer. As you move from LLM to tool to agent, the surface area for failure grows exponentially. The evaluation framework must grow with it.

Now, What Does It Actually Take to Build an LLM Tool?

Building an LLM tool in 2026 is more accessible than ever — but the gap between a working prototype and a production-grade system remains vast. The prototype takes hours. The production system takes months. The difference is almost never the model itself — models are commoditizing fast.

The Six-Layer Stack

Foundation Model Selection
- Closed frontier models (GPT-4o, Claude, Gemini) offer capability with minimal setup. Open-weight models (Llama 3, Mistral) offer cost control, data privacy, and fine-tuning flexibility.
Prompt Engineering Layer
- System prompts, few-shot examples, chain-of-thought scaffolding, and output format instructions shape model behavior before a single feature is written. Well-engineered prompts are the fastest lever for quality improvement — and the most underestimated engineering surface.
Context & Memory Management
- LLMs have finite context windows. Production tools must carefully manage what goes into each inference call: conversation history, long-term user memory, retrieved documents, tool outputs.
Tool & API Integration
- Modern LLMs call external tools via function calling or the Model Context Protocol (MCP). Each integration — search, database, calendar, code execution, CRM — multiplies the surface area for failures and requires its own evaluation criteria to catch regressions.
Orchestration Layer
- For agentic workflows, orchestration manages agent state, tool routing, retries, and multi-step task decomposition. Frameworks like LangGraph, OpenAI’s Agents SDK, and Qyrus’s SEER provide the primitives.
Evaluation & Observability Infrastructure
- Every production LLM tool needs logging, tracing, and automated evaluation. Without it, teams cannot detect regressions, understand failure modes, or improve systematically.

How LLMs Actually Work

A large language model is a probabilistic function that maps sequences of tokens to probability distributions over the next token. Train it on enough text and it learns to model the statistical structure of language at a depth that produces remarkable emergent capabilities — reasoning, translation, coding, logical deduction — that were never explicitly programmed.

The Transformer Architecture

Every major LLM — GPT-4, Claude, Gemini, Llama — is built on the Transformer architecture introduced by Google in 2017. Its core innovation is the attention mechanism: a mathematical operation that allows every token in a sequence to dynamically attend to every other token, learning which relationships matter for predicting what comes next.

The Attention Mechanism

Attention is the reason LLMs understand that “it” in a paragraph refers to a noun from three sentences back. For every token, attention computes a weighted combination of all other tokens in context, where the weights are learned across billions of training examples. The formula: Attention(Q,K,V) = softmax(QKᵀ / √d) · V. Each token acts simultaneously as a Query (what am I looking for?), a Key (what do I represent?), and a Value (what information do I carry?).

Pre-training and Alignment

LLMs are trained in two phases. Pre-training exposes the model to hundreds of billions to trillions of tokens — internet text, books, code, scientific papers. The objective is simple: predict the next token. At scale, this simple objective produces emergent capabilities in reasoning, translation, and logic that were never explicitly programmed.

Fine-tuning and alignment 

then adapts the raw model into a useful assistant. RLHF (Reinforcement Learning from Human Feedback) trains the model to prefer outputs rated highly by humans. Constitutional AI, used by Anthropic, trains self-critique using a set of principles. These alignment techniques are what transform a raw language predictor into a safe, helpful product.

How Large Data Drives Large Decisions

One of the most profound shifts in computing history is that modern AI systems are capable to make better decisions not primarily because of better algorithms — but because they trained on more data. This is a fundamentally different category from classical software engineering.

Scaling Laws

Research from OpenAI, DeepMind, and others established empirical scaling laws: as you increase training compute, dataset size, and model parameters together, model capability improves predictably on a smooth power-law curve. The Chinchilla paper (Hoffmann et al., 2022) showed that most large models at the time were undertrained — the optimal strategy is to scale data and parameters together in balance. Practically, this means the model powering your customer service chatbot has “read” the equivalent of millions of books, billions of web pages, and hundreds of millions of lines of code. When it answers a contract law question, it draws on patterns from thousands of real legal documents. When it writes Python, it has processed more code than any human could read in a lifetime.

The Data Flywheel Effect

The most defensible AI companies are not the ones with the best architecture today — they are the ones building data flywheels. More users generate more interactions. More interactions generate labeled feedback. Here labeled feedback produces better fine-tuned models. Better models attract more users. This loop is the real competitive moat in AI — not cleverness, not model architecture, but data velocity. OpenAI has over 400M weekly ChatGPT users. Google has it through Search and Workspace. Anthropic is building it through enterprise Claude adoption. The companies that win the next five years will be those who can convert user interactions into high-quality training signal at the highest speed — and measure that signal accurately with evaluation infrastructure.

From Data Volume to Decision Quality

In healthcare, LLMs trained on clinical notes assist with differential diagnosis. In finance, models trained on earnings transcripts help analysts identify patterns across thousands of filings simultaneously. In legal, models trained on case law surface relevant precedents faster than any human search ever could. LLMs don’t “know” things the way humans do. They are sophisticated compression and interpolation machines that give responses in human-generated text. Their limitations comes from the breadth and depth of their training data.

The LLM Progression

How Did We Get Here?

The honest answer to “what can an LLM do?” depends entirely on when you’re asking. In 2018, the answer was: complete sentences, sometimes coherently. In 2025, the answer is: write production software, pass bar exams, conduct multi-step research, operate computers autonomously, and reason through problems that stumped AI systems for decades. That gap — seven years — represents the most rapid capability expansion in the history of computing.

2018–2019: Plausible text, unreliable reasoning

The first generation of Transformer-based LLMs (GPT-1, BERT, GPT-2) was able to produce grammatically coherent text and perform narrow NLP tasks — sentiment classification, named entity recognition, basic question answering. GPT-2, released in 2019 with 1.5 billion parameters, was considered dangerous enough that OpenAI staged its release. Looking back, it could barely maintain topic coherence across three paragraphs. The capability was limited to: fluent sentences on familiar topics, with frequent factual drift and no ability to follow multi-step instructions reliably.

2020: The emergent leap — GPT-3 and few-shot learning

Post Covid GPT-3 (175B parameters) changed the fundamental question from “can LLMs do task X?” to “how well can LLMs do task X with the right prompt?” It demonstrated few-shot learning — the ability to learn a new task from just 2–3 examples in the prompt, without any weight updates. This had never been seen at scale before. Suddenly a single model could translate between languages it was never explicitly trained to translate, write code in frameworks it had only seen mentioned, and draft legal language with good reasonable structure. The capability unlock was the highlight — one model, many tasks, no retraining required.

2021–2022: Instruction following and alignment

Raw capability means little if the model won’t do what you ask. InstructGPT (2021) introduced RLHF — training the model to follow instructions rather than just predict likely text. This was the transition from “impressive research” to “usable product.” Models now started being able to maintain personas, follow formatting instructions, refuse harmful requests, and produce outputs shaped around human preferences rather than before. ChatGPT in November 2022 was the product manifestation of this alignment work — and reached 100 million users in 60 days. During this same period, code generation became a serious capability. GitHub Copilot launched in 2021, trained on billions of lines of public code. By 2022, developers were reporting that Copilot was completing 25–40% of their code automatically. The model had learned not just syntax but idioms, patterns, library conventions, and common debugging strategies.

2023: Multimodality, long context, and reasoning

GPT-4 (March 2023) came with two step changes: multimodal input (the model could now see images, not just text) and dramatically improved reasoning. It passed the bar exam in the 90th percentile. It scored at or above human performance on multiple professional certification exams. Chain-of-thought prompting — asking the model to reason step by step before answering — unlocked latent reasoning capabilities that had always been in the weights but were inaccessible with direct-answer prompting. Context windows grew from 4K tokens (GPT-3) to 32K (GPT-4) to 100K (Claude 2). This meant LLMs could now process entire codebases, legal contracts, or research papers in a single pass — tasks that previously required chunking, retrieval pipelines, and significant engineering overhead.

2024: Tool use, agents, and computer control

The capability frontier shifted from what a model could say to what a model could do. Function calling — introduced as a first-class feature in mid-2023 and matured through 2024 — let LLMs call external APIs, query databases, run code, and trigger workflows. The model was no longer just a text generator; it was a reasoning engine that could take actions in systems. By late 2024, Anthropic shipped Claude for Computer Use with the ability for an LLM to see a computer screen, move the cursor, click, type, and operate arbitrary software like a human operator. OpenAI launched Operator on similar principles. GitHub’s 2024 report found that 46% of all new code written on the platform was AI-generated. LLMs had moved from helping write code to writing the majority of it.

2025: Agentic reasoning and multi-step autonomy

The current capability frontier is agentic — LLMs are now operating as autonomous orchestrators that decompose goals, plan sequences of actions, execute them across multiple tools and systems, observe results, self-correct, and iterate until a goal is achieved. This is not a chatbot anymore. This is software that can be given a business objective — “research competitors and produce a positioning brief,” “audit our API responses for compliance issues,” “set up the dev environment from scratch” — and pursue it without human input at each step. The o1 and o3 model families introduced extended chain-of-thought reasoning — models that “think” before responding, spending compute on internal reasoning traces before producing output. On mathematical olympiad benchmarks, these models moved from near-zero performance (GPT-3 era) to exceeding PhD-level performance on many problem classes. DeepSeek R1 demonstrated that this reasoning capability can emerge from reinforcement learning without supervised examples — a fundamental shift in how capability is developed.

The Rise of Agentic AI

For the first two years of the generative AI era, most LLM products were reactive: a user sends a message, the model responds, the loop ends. Agentic AI breaks this pattern entirely. An AI agent perceives its environment, makes decisions, takes actions, and operates across multi-step workflows — often without human intervention at each step. This shift — from reactive LLMs to agentic systems — is the most significant transition in AI since ChatGPT’s release. It transforms LLMs from sophisticated autocomplete into software that can do things in the world.

Multi-Agent Systems and MCP

The frontier is moving beyond single agents toward multi-agent systems — networks of specialized AI agents that collaborate, delegate, and verify each other’s work. An orchestrator agent decomposes complex tasks, dispatches to specialist agents (a researcher, a writer, a coder, a critic), and synthesizes outputs. This architecture mirrors how high-performing human teams operate. Anthropic’s Model Context Protocol (MCP), created in November 2024 and donated to the Linux Foundation in December 2025, became the industry standard for connecting agents to external tools and data — with over 10,000 MCP servers deployed. OpenAI’s Agents SDK (early 2025) formalized multi-agent primitives: agents with instructions and tools, handoffs for control transfer, and guardrails for validation. Every major AI lab now has its own agent framework.

Why Agents Make Evaluation Exponentially Harder

Evaluating a simple chatbot is already challenging. Evaluating an agentic system is orders of magnitude harder. Agents make sequences of decisions, each of which can compound errors. A single hallucination in step 3 of a 10-step workflow can corrupt all subsequent outputs. The final result may look reasonable even when the path to get there was deeply flawed. Traditional evaluation metrics — accuracy, BLEU score, human preference — were designed for single-turn outputs. Agentic evaluation requires new frameworks: trajectory evaluation, tool-use accuracy, goal completion rate, and safety monitoring across every action taken.

The First Movers

What Can an LLM Actually Do?

The answer in 2025 is: almost anything that involves language, reasoning, or code. But “almost anything” is not the same as “everything reliably.” Understanding where LLMs excel and where they fail is the foundation of good system design.

Why LLM Eval Matters Now

With custom LLM tools, copilots, and agents are becoming part of real workflows every day. But as usage grows, so do the risks — inconsistent outputs, hallucinations, broken prompts, and no clear way to measure quality. That is why the need for a reference point has become urgent. The problem we see is simple: LLMs are powerful, but they are not naturally predictable. The same prompt can produce different answers, prompt changes can shift behavior, and a model that looks strong in a demo can fail in real use. Without a standard way to evaluate quality, teams are left guessing whether they are actually improving or just seeing different output. That gap becomes even more serious as teams move from experimentation to production. Once an AI system is used for support, search, drafting, decision-making, or agent workflows, a weak output is no longer a harmless mistake. It can affect customers, slow teams down, and damage trust in the product.

The Growth Problem

The rise of custom LLM tools is creating a new problem: usage is outpacing measurement. Enterprises are investing in AI systems, model experimentation, and custom workflows, but the discipline around evaluation is still catching up. Because teams are realizing they need a better way to measure outcomes. That matters because custom AI is not just one model call anymore. It often includes prompts, retrieval, tools, routing logic, agent loops, and guardrails. Each layer adds complexity, and each layer can fail differently. If the system is changing every week, a one-time test is not enough. Teams need something repeatable that can tell them whether quality is improving or drifting. This is where most teams feel the pain first:

Output quality varies from one run to the next.

Prompts that work today can fail after a small edit.

A better-looking answer is not always a better answer.

Multiple models can behave differently on the same task.

Agents can take the wrong step even when the final answer looks fine.

Without a shared measurement layer, those issues become hard to spot early. The result is slower releases, more manual review, more rework, and less confidence in production AI.

What Teams Are Struggling With

Teams building custom LLM tools are not just asking, “Did it work?” They are asking deeper questions:

Is the output correct?

Is it consistent?

Does it stay safe on edge cases?

Does it behave the same after a prompt or model change?

Can we trust it at scale?

Those questions are hard to answer without a reference point. That is why so many AI teams end up using subjective review, ad hoc prompt checks, or manual spot testing. Those methods can help early on, but they do not scale well once the product grows. What starts as a quick check becomes a weak process.

The biggest problem is that AI output is not binary. Traditional software testing often checks whether something passes or fails. LLM systems are fuzzier. They can be partially right, confidently wrong, overly verbose, too vague, or inconsistent across runs. That makes evaluation less about one perfect answer and more about a structured way to compare quality over time. That is also why model comparison alone is not enough. A team may switch models and see better output in one area, but worse behavior somewhere else. Without evaluation, that trade-off is invisible. With evaluation, it becomes measurable.

Real-World Failures: What Happens Without Evaluation

Expert Perspective

“In aviation, we don’t let a plane fly without rigorous testing. In pharmaceuticals, no drug reaches patients without clinical trials. Yet we’re deploying AI systems that influence millions of decisions daily with virtually no standardized evaluation.” — Dr. Percy Liang, Director, Stanford Center for Research on Foundation Models (CRFM)

Why a Reference Point Is Needed

Sources: Stanford HAI 2024, MIT Sloan Management Review, Gartner The industry needs a standard way to answer one question: Is this AI system actually getting better? That is what evaluation metrics provide. They create a baseline that teams can return to whenever they change a prompt, swap a model, add retrieval, or release a new version. A good evaluation metric acts like a compass. It does not just tell you whether a single answer looks decent. It tells you whether the system is moving in the right direction. That matters for quality, but it also matters for speed. When teams know how to measure improvement, they can move faster with less fear. Evaluation also helps teams compare across the things that matter most:

Accuracy.

Consistency.

Safety.

Retrieval quality.

Tool use.

Helpfulness.

Resistance to hallucination.

Once those are measured consistently, AI stops feeling like a black box and starts behaving more like a product discipline. That is the shift the market is moving toward now. This is the moment where evaluation becomes a competitive advantage. Teams that can test clearly will ship more confidently. Teams that can compare reliably will choose better models and prompts. Teams that can catch regressions early will avoid expensive surprises later. As AI moves into real workflows, evaluation will become more essential. It is no longer enough for an AI system to sound intelligent. It has to be measurable, repeatable, and dependable in the situations that matter most. That is why the next step is not more hype. It is better evaluation. And that is exactly where a solution becomes valuable. ❌ Without Evaluation

Unknown hallucination rates

Undetected bias and discrimination

Security vulnerabilities

Regulatory non-compliance

Reactive incident response

Brand and trust damage

✅ With Systematic Evaluation

Quantified accuracy and reliability

Proactive bias mitigation

Security assurance

Regulatory compliance

Preventive quality control

Stakeholder confidence

Source: Estimated from Statista, SimilarWeb, GitHub, Google data (2025) If LLMs handle 3 billion decisions per day with just a 1% error rate, that’s 30 million failures daily. Over a year: 10.9 billion errors. Without evaluation, you don’t even know which 1%.

LLM Evaluation — How It Works & The QyrusAI Method

LLM evaluation is the practice of systematically measuring how well an LLM-powered system performs its intended function. As AI moves into production, evaluation is not optional quality assurance — it is the foundational engineering infrastructure that separates teams that improve their models from teams that just guess.

What an LLM evaluator actually does

An LLM evaluator takes a test case — a prompt, the LLM’s response, and optional reference material — and scores it across one or more dimensions: accuracy, relevance, faithfulness, safety, coherence, goal completion. It can be a deterministic code check, an LLM-as-judge, a human reviewer, or a hybrid. The output is a score and a reason — structured signal that tells you exactly what to fix. QyrusAI connects to your live LLM API endpoints, fires your test cases, scores each output using your chosen evaluator model, and returns pass/fail results with detailed per-dimension scoring. You pay credits per test execution. No infrastructure to stand up. No dedicated ML platform team required.

LLM Evaluation Best Practices — 8-Point Checklist

These are the practices that separate teams running reliable AI in production from teams constantly firefighting model regressions. Apply them in order — each builds on the last.

Define evaluation criteria before you write a single prompt
- Know what “good” looks like before you build. What accuracy threshold is acceptable? What tone must be maintained? What topics must be avoided? Teams that define criteria after the fact spend weeks retrofitting. Teams that define criteria first ship 3x faster.
Test hallucination rate as a first-class metric
- Hallucination rate should be tracked on every deployment like latency or uptime. Build a test suite of prompts specifically designed to probe the model’s weak spots — topics where training data is sparse, numerical claims, citations. Measure the rate. Set a threshold. Alert when it crosses it.
Use multiple evaluator types — never rely on one
- Code-based checks for structured outputs. LLM-as-judge for open-ended quality. Human review for high-stakes edge cases. Each catches different failure modes. A pipeline with only one evaluator type will have systematic blind spots.
Run regression tests on every model update
- Model providers push silent updates. A prompt that worked perfectly on GPT-4-turbo-2024-04-09 may behave differently on the next version. Every deployment update — model version, system prompt, context window change — must run your full benchmark suite before going live.
Evaluate RAG retrieval and generation separately
- If your RAG pipeline is underperforming, you need to know whether the problem is in retrieval (wrong documents fetched) or generation (model fails to use good documents). Treating them as a single system makes debugging nearly impossible. Evaluate each component with its own metrics.
For agents: evaluate trajectories, not just final outputs
- An agent that reaches the correct answer via a flawed path is not a passing agent. It means your system got lucky. Evaluate every tool call, every intermediate decision, every handoff. Build automated checks for: correct tool selected, correct parameters passed, appropriate fallback triggered on failure.
Monitor production continuously — not just pre-deployment
- Pre-deployment testing catches regressions from changes you made. Production monitoring catches degradation from changes the world made — new query patterns, seasonal topics, data drift. A model that passed all pre-deployment tests can still degrade silently in production over weeks.
Feed evaluation results back into your improvement loop
- Every failed eval is a training signal. Every user complaint that triggers a human review queue is data. The teams compounding improvements fastest are those who convert evaluation results directly into prompt improvements, fine-tuning datasets, or retrieval configuration changes — within days, not quarters.

Frequently Asked Questions

The questions that come up most when teams are starting to evaluate LLMs — answered directly. What is an LLM? A large language model (LLM) is an AI system trained on massive text datasets to understand and generate human language. It predicts the next token in a sequence using billions of learned parameters, producing coherent text, code, and reasoning at scale. Examples include GPT-4, Claude, Gemini, and Llama. What is an LLM evaluation tool? An LLM evaluation tool measures the quality, accuracy, safety, and relevance of LLM outputs systematically. It automates scoring across dimensions like hallucination rate, faithfulness, coherence, and goal completion — giving teams a repeatable, scalable way to know if their AI is actually working. QyrusAI is an example: you connect your LLM API, define criteria, choose an evaluator model, execute, and get scored results. What is the difference between an LLM and an AI agent? An LLM is a text model that responds to prompts reactively. An AI agent wraps an LLM with planning, persistent memory, dynamic tool access, and a feedback loop — allowing it to autonomously pursue multi-step goals without human input at each step. Every AI agent uses an LLM; not every LLM is an agent. What is LLM hallucination and why does it happen? LLM hallucination is when a model generates confident, fluent text that is factually incorrect or fabricated. It happens because LLMs generate text by predicting the most statistically likely continuation — they have no internal truth-checking mechanism. When training data is sparse on a topic, the model fills the gap with plausible-sounding content. Evaluation tools measure and track hallucination rates so teams can catch and fix it before it reaches users. What is RAG and how does RAG evaluation work? RAG (Retrieval-Augmented Generation) grounds LLM responses in retrieved source documents. The pipeline retrieves relevant chunks from a knowledge base and passes them as context to the model. RAG evaluation measures two things: retrieval quality (did we fetch the right documents?) and generation quality (did the model accurately use what it received?). Key metrics include faithfulness, context precision, context recall, and answer relevance. How does QyrusAI work? QyrusAI connects to your live LLM API endpoints end-to-end, runs your defined test cases against the live output, scores each response using your chosen evaluator model (GPT-4o, Claude Sonnet, Gemini, etc) and returns detailed pass/fail results with per-dimension scoring. You can define the evaluation criteria — or use our built-in rating system. Credits are consumed per test executed. No infrastructure setup required. What are LLM testing best practices? The eight most important practices: define evaluation criteria before prompting, track hallucination rate as a first-class metric, use multiple evaluator types (code, LLM-as-judge, human), run regression tests on every model update, evaluate RAG retrieval and generation separately, evaluate agent trajectories not just final outputs, monitor production continuously, and feed evaluation results back into your improvement loop. Ready to evaluate your LLMs with confidence? infra required. Credits-based. Any model. Sign up today

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BFSI

CIO / Head of IT

CFO / IT Finance / FinOps

IT Operations / AMS Head

VP Procurement / Vendor Management

Head AI, Data Platforms & Governance

VP Quality/QA Lead/Release Manager

60%

Modernize

Assure

Operate

Modernize + Assure

Assure + Operate

Modernize + Operate

All Three - The Full Loop

Manufacturing

Retail

Healthcare

Food & Beverages

BFSI

CIO / Head of IT

CFO / IT Finance / FinOps

IT Operations / AMS Head

VP Procurement / Vendor Management

Head AI, Data Platforms & Governance

VP Quality/QA Lead/Release Manager

60%

The Business Processes Behind the Transformation

Where AI-Driven Quality Fits

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Learning From the SAP Community

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Customer Background

Problem Statement

QyrusAI Solution

Automated IT Discovery Framework

Technical Approach & Techniques

Outcomes & Business Impact

Business Value

Strategic Takeaways

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The Comfort of Seeing Everything

The Gap Between Signals and Meaning

Why More Data Doesn’t Solve the Problem

When Everything Looks Important, Nothing Is Prioritized

The Cost of Misalignment

From Visibility to Understanding

Where QyrusAI Makes the Difference

Frequently Asked Questions (FAQs)

Why is component-level visibility no longer enough for modern enterprise IT?

What is the difference between observability and Business Operations Intelligence (BizOps)?

How does QyrusAI prevent alert fatigue during high-severity incidents?

How does connecting IT to business context accelerate MTTR?

The Bottom Line

The Missing Ingredient Isn’t Data

The Problem with Disconnected Data

Why Context Matters

From Data to Understanding

The Role of Context in AI

Where QyrusAI Fits In

Frequently Asked Questions (FAQs)

The Bottom Line

The Cost You Can Measure and the Cost You Can’t

When an Incident Happens

The Cost of Delay

When Effort Doesn’t Match Importance

The Ripple Effect Across the Organization

Why This Problem Persists

From Reaction to Clarity

What Application Discovery and Dependency Mapping Actually Means 

Why This Is Harder Than It Used to Be 

From Discovery to a Living Model: The Knowledge Graph 

Where This Shows Up: Cloud Migration 

How QyrusAI Approaches Auto Discovery 

Frequently Asked Questions 

What is an LLM Tool?

LLM vs AI Tool vs AI Agent

Now, What Does It Actually Take to Build an LLM Tool?

How LLMs Actually Work

Fine-tuning and alignment 

How Large Data Drives Large Decisions

The Rise of Agentic AI

What Can an LLM Actually Do?

LLM Evaluation — How It Works & The QyrusAI Method

LLM Evaluation Best Practices — 8-Point Checklist

Frequently Asked Questions