Production AI Evaluation and Monitoring

AI evaluation scores the quality of a probabilistic system across many representative cases instead of returning a binary pass or fail on fixed logic. Unit tests assume the same input always produces the same output, so a green suite proves the behavior is locked in. LLM and agent systems break that assumption: the same prompt can return different text on different runs, and the model, prompt, and retrieved context all shift underneath you. So evaluation grades quality across a whole set of cases, often as a percentage or average, and re-runs that set whenever a prompt or model changes. You set a threshold the way you set code coverage, and a change that drops below it counts as a regression. It is closer to statistical quality control than to assertion-based testing.

Measure task-specific metrics, because a single accuracy number hides the failures that matter for each system type. For RAG, track faithfulness and groundedness (is the answer supported by retrieved context?), context precision and recall, and answer relevance. For agents, track tool-call accuracy (right tool, right arguments), task completion, step efficiency, and budget adherence. For chatbots, track helpfulness, tone and instruction-following, refusal correctness, and safety. All three share latency, cost per request, and error rate. The point is to grade the thing that actually breaks: a RAG system that retrieves badly and a chatbot that ignores instructions fail in different ways, and one combined score would let both slip through.

LLM-as-judge is reliable enough to scale evaluation, but only if you actively control its known biases, so we never trust raw judge scores alone. A model scoring other model outputs has documented failure modes: position bias, where it favors whichever answer is shown first; verbosity bias, where it rewards longer answers; and self-preference, where it rates its own model family higher. We counter these with calibrated rubrics, position-swap runs that score answers in both orders, and a structured rationale before the score. Then we anchor the judge with human spot-checks, where reviewers grade the same sampled cases and we measure agreement. When they diverge, we fix the rubric, not the humans. The judge handles volume while people keep it calibrated, which is far cheaper than labeling everything by hand.

Start from production logs, not invented examples, so the eval set reflects what users actually do rather than what you imagined they would. Mine traces for representative cases across your real input distribution, then deliberately add the hard ones: ambiguous queries, adversarial or jailbreak attempts, edge cases, and past incidents. Give each case a reference answer or a graded rubric so it scores automatically, and version the dataset like code to track coverage and compare scores across versions. The set is never finished: every correction from human review becomes a new labeled case, so it keeps converging on your live distribution. A few hundred well-chosen cases that mirror production beat thousands of synthetic ones.

Offline evaluation runs your fixed golden dataset before you ship, online evaluation scores real traffic after you ship, and a production system needs both. Offline is the CI gate: repeatable, runs on every prompt or model change, and tells you a change is safe against a known baseline. Its blind spot is that the test set is frozen, so it misses new user behavior or a silent provider update. Online samples live requests and scores them with the same metrics, often an LLM judge plus human review on a slice. It catches drift, surfaces inputs you never anticipated, and shows real cost and latency under load. Offline tells you a change passed your bar; online tells you the live system still meets it. Cases that online eval flags get added back into the offline set.

Treat regressions and drift as two separate problems, because they have different causes and detection methods. Regressions come from changes you make, like a reworded prompt, a model upgrade, or a retrieval tweak. We re-run the eval suite in CI on every change and compare to the last passing baseline, so a drop blocks the release before it reaches users. Drift comes from changes you do not make, like a provider quietly shipping a new model or the input mix shifting. We sample live traffic, score it offline with the same metrics, and monitor distributions: embedding-space shifts, output length, refusal rate, and tool-use patterns. When a metric crosses a threshold, the alert links to example traces so you can diagnose it. One is a gate at deploy time; the other is a watch on the running system.

Catch hallucinations with grounding checks and tool-call errors by validating calls against their schemas and results, since these are different failures with different fixes. For hallucinations in RAG, score faithfulness so each claim traces back to a retrieved chunk: reference-based comparison where a known answer exists, an LLM judge with a strict rubric where it does not, and flagging facts the context never supported. For tool-call errors in agents, trace every call and check that the right tool was chosen, the arguments match the schema, and the result was used in the next step rather than ignored. Loop detection catches an agent that keeps retrying a failing tool. Both signals feed the scorecard and the review queue, so flagged cases get human eyes and then become regression cases in the golden dataset.

We are tooling-agnostic and choose based on your stack, but in practice we build on OpenTelemetry conventions and trace into LangSmith or Langfuse, because portable traces matter more than any single vendor. OpenTelemetry semantic conventions keep spans in a standard format, so you are not locked in. LangSmith is strong for LLM tracing and eval workflows in the LangChain ecosystem. Langfuse fits when you want open-source and self-hosting, which helps with data residency or HIPAA-aware handling. We run these alongside your existing metrics, logging, and alerting rather than replacing them. The non-negotiables are queryable traces of every run, an eval set wired into CI, and online sampling in production. The dashboard matters less than having those three work together, so we fit the tools to your constraints.

Yes, retrofitting evals onto a live system is one of the most common ways we start, and the existing traffic actually makes it faster. Real production logs mean we build the golden dataset from cases that actually happened. We add tracing and scoring around your existing pipeline without rewriting the application, establish a baseline so you finally know your current quality, then turn on drift detection and budget guardrails so it stops degrading silently. The usual first finding is a quality or cost problem nobody had measured, which is exactly the value of doing it. From there the system behaves like one that had evals from day one. You do not have to rebuild to make a shipped system measurable.

Budgets cap how much an agent can spend on a task and loop detection stops it spinning in place, so a misbehaving run fails cleanly instead of billing all night. We set per-task ceilings on tokens, tool calls, wall-clock time, and total steps, enforced in the runtime. When a session hits a limit it stops, returns a clear failure, and gets logged, rather than calling paid APIs in the background. Loop detection watches for repeated states, the same tool called with the same arguments, and re-planning cycles, then breaks out. Alerts on cost per request and total spend catch an upward trend while it is still small. The principle is that the system should bound itself before anyone has to read the invoice.

Yes, gating launches behind evals is the core of how we make AI releases safe, and it works just like a test gate in your existing CI. Any change to a prompt, model, or retrieval step triggers a scoring run against the versioned golden dataset. Results are compared to the last passing baseline, and a drop below your threshold fails the build, the same way a failing unit test blocks a merge. For bigger model swaps we add a canary or shadow rollout: route a slice of traffic to the new version, score it with online eval, and promote only if it holds the bar. This turns shipping AI from a hopeful deploy into a measured decision, and it gives non-engineers a clear answer to whether a change made the system better or worse before users see it.

We are an AI engineering company that embeds senior engineers into your team to build and then operate production AI, so we run the evals and monitoring we set up rather than handing you a dashboard and walking away. Founded in 2019, based in Ahmedabad with US-overlap delivery hours, we work as part of your team across time zones. We have built and operate real systems with this discipline, including a nightly medication-adherence voice system and SOAP-note automation, both of which depend on tracing, quality checks, and guardrails. For sensitive data we apply HIPAA-aware and SOC 2-aware controls that help you meet your obligations. The differentiator is that we stay to operate it, so the eval set keeps growing and quality compounds instead of decaying.