Traditional application monitoring answers questions such as whether a service is available, how long requests take and how many errors occur. Generative AI systems add another class of questions: was the answer grounded, did the agent choose the right tool, did retrieval return useful evidence, did safety behavior change after a model update, and is the system becoming more expensive as usage grows?
AI-103 therefore treats observability and evaluation as part of the application architecture. Candidates in the Microsoft AI certifications path need to connect tracing, metrics and evaluation so that model behavior can be diagnosed rather than merely observed from the outside.
Observability starts with a trace of what actually happened
An agent request can involve several model calls, retrieval queries and tools before the user receives a final response. A single HTTP status code cannot explain that path. Tracing captures the sequence so engineers can see where time was spent and which decision produced the failure.
A useful trace can include model invocations, tool calls, retrieved evidence, intermediate agent steps, token counts and latency. The goal is not to collect every possible byte forever. It is to preserve enough structure to answer operational questions quickly.
Trace correlation is especially important in multi-agent or multi-service systems. A parent request should be connected to child operations so the team can reconstruct the complete trajectory rather than searching several systems manually.
Operational metrics and quality metrics answer different questions
Latency, request rate, failure rate, token consumption and tool-call count are operational metrics. They help teams manage capacity, cost and reliability. Quality metrics ask whether the AI system did the right thing.
A system can be operationally healthy while producing poor answers. It can also produce high-quality answers while becoming too slow or expensive for production. Both dimensions need explicit thresholds.
Agent dashboards can therefore combine operational signals with evaluation outcomes. A spike in tool errors may explain a sudden drop in task-completion quality. Increased token use may indicate that prompts or retrieval contexts have grown without improving answers.
Evaluation needs defined tasks and success criteria
“The answer looks good” is not an evaluation plan. Teams should define representative tasks and the evidence that counts as success. For a RAG assistant, the expected source may be known. For an agent, the correct result may be a state change in an external system. For extraction, the expected fields can be compared with labeled data.
Different graders can measure different dimensions: relevance, groundedness, completeness, safety or task completion. Separating dimensions makes failures easier to diagnose than collapsing everything into one score.
Evaluation sets should include difficult cases. Missing data, ambiguous requests, conflicting sources, denied tool permissions and unsafe inputs reveal weaknesses that clean happy-path examples will miss.
RAG systems need retrieval observability
If a grounded answer is wrong, the problem may not be the model. The expected document may be missing from the index. Chunking may have split the relevant context. A filter may exclude the correct source. Vector retrieval may rank a similar but incorrect passage above the needed evidence.
Retrieval telemetry should therefore expose query behavior, result relevance, index health and ingestion quality. The team should be able to distinguish “the model ignored good evidence” from “the system never retrieved good evidence.”
This separation prevents expensive model changes from being used to solve a search problem. It also makes relevance tuning measurable.
Agent evaluation must inspect both outcome and process
An agent can reach a correct final answer while using an unacceptable path. It may call a tool it did not need, require excessive retries or bypass an intended approval step. Process evaluation looks at the trajectory, not only the final text.
Useful agent metrics include task completion, tool-selection accuracy, handoff success, navigation efficiency and recovery from errors. Traces provide the evidence needed to grade those behaviors.
This is where observability and evaluation meet. Evaluation without traces may tell the team that a task failed but not why. Tracing without evaluation produces a large amount of telemetry without a clear definition of good behavior.
Continuous evaluation catches drift after deployment
Pre-production evaluation establishes a baseline. Production traffic introduces new language, new tool states and new edge cases. Continuous evaluation samples real interactions and checks whether quality and safety remain within expected ranges.
Drift can come from many places. A model version may change. Source documents may be updated. An index may degrade. A prompt may be edited. A tool API may return a new schema. Monitoring should therefore connect quality changes to deployment and data changes where possible.
Scheduled evaluation on fixed datasets is useful alongside production sampling. The fixed suite provides comparability across versions, while sampled production traffic reveals conditions the original suite did not anticipate.
Safety evaluation belongs beside quality evaluation
A system that becomes more helpful but less safe has not simply improved. Evaluation should include harmful content, prompt injection, sensitive-data handling, permission boundaries and human-approval behavior where relevant.
Red-team exercises can deliberately search for ways to bypass controls. The objective is not to create an unrealistic collection of trick prompts; it is to understand whether the system fails safely when exposed to adversarial or ambiguous conditions.
Safety metrics should be reviewed with false positives in mind. Overly strict controls can destroy utility. The target is behavior appropriate to the workload’s actual risk.
Cost is an observability concern because architecture drives spend
Token usage, repeated tool calls, retrieval volume and multi-agent orchestration can all change operating cost. A model that is only slightly more accurate may be dramatically more expensive at scale. A worker agent that duplicates research may add cost without improving the result.
Observability can expose the cost of specific paths. If one category of request consistently uses much more context, the architecture may need better retrieval or summarization. If retries dominate spend, the underlying reliability issue should be fixed rather than accepted as normal.
The same measurement discipline used for traditional IT KPIs applies here. The site’s discussion of measurable IT KPIs is useful background: metrics become valuable when they are tied to decisions rather than collected for their own sake.
Evaluation should act as a release gate
Changes to prompts, agent instructions, tools, retrieval configuration or models should be tested against a known suite before deployment. The release can then be compared with the baseline on both quality and safety dimensions.
This does not mean every small change needs a massive benchmark. The suite should be proportional to the risk of the application. High-impact workflows deserve more coverage, adversarial testing and manual review than a low-risk drafting assistant.
Across the broader AI and generative AI certifications space, observability is becoming a defining production skill. AI-103 developers are expected not only to build a system that works in a demonstration, but to instrument it well enough that the team can detect regression, explain failure and improve behavior with evidence.
Baselines make model and prompt changes comparable
Without a baseline, teams can tell that a new version feels different but not whether it is actually better. A fixed evaluation set creates a reference point for changes in models, prompts, retrieval settings and tools.
Baselines should include both quality and cost. A version that improves groundedness by one point while doubling latency may not be an improvement for the business. Similarly, a cheaper model that increases escalation or retry rates can cost more once the full workflow is measured.
Version labels should be carried into telemetry so production changes can be connected to metric shifts. This makes rollback and root-cause analysis far faster when a regression appears.
Alerting should focus on actionable changes
AI systems can generate an overwhelming number of metrics. Alerts are useful when they correspond to a condition someone can act on: a sustained drop in task completion, a spike in tool failures, a retrieval index falling behind ingestion, or token cost increasing beyond an agreed threshold.
Single unusual conversations are often better handled through sampling and review than paging an engineer immediately. High-impact workflows may justify tighter thresholds, while low-risk assistants can rely on trend detection.
The objective is operational clarity. Observability should help the team decide what to investigate next, not simply produce more dashboards.
Evaluation data needs its own quality controls
Test sets can become stale just like production knowledge. If user behavior changes or new features are added, an old suite may continue to pass while missing the failures that now matter. Evaluation datasets should therefore be reviewed, versioned and expanded from real incidents.
Labels and expected outcomes also need validation. A flawed grader can make a good model look bad or reward behavior the business does not actually want.
High-confidence evaluation comes from maintaining the benchmark as carefully as the application itself.
Sampling strategy should reflect business risk
Continuous evaluation does not require grading every production request. Sampling can control cost while still revealing trends, but the sample should not be purely random when some workflows are more important than others. High-risk actions, new features and recently changed prompts may deserve heavier sampling until their behavior is well understood.
A mature monitoring plan combines broad statistical sampling with targeted review of incidents, low-confidence cases and security-relevant events. That gives teams both coverage and focus.