What you get from each, and why the gap matters
Observability and evaluation solve different problems. Conflating them means you can run a green dashboard while your agent gives users wrong answers, takes unintended actions, or quietly degrades over weeks. This article draws a precise line between the two, explains what each one structurally can and cannot answer, and describes how to run them together in a production loop.
If you want to go deeper on the measurement side first, agent evaluation in production: what to measure and how to prove it covers the full scoring framework, golden sets, and how to turn eval results into evidence your stakeholders will accept.
The distinction matters more now than it did two years ago. According to Gartner Q1 2026 data, 80% of enterprise applications shipped or updated in the first quarter of 2026 embed at least one AI agent, up from 33% in 2024. Yet only 31% of enterprises have an agent in production. The gap between "we shipped something" and "we can prove it works" is partly a tooling confusion: teams instrument for observability and assume they have covered evaluation.
What observability gives you
An observability layer records what happened. For an AI agent, that means traces across tool calls, spans with timestamps and token counts, latency per step, error rates, and cost per request. A well-instrumented agent will surface a trace showing every LLM call, every retrieval hit or miss, every external API invocation, and how long each took.
This is genuinely useful. You can answer questions like: did the agent complete the request without an exception? How many tokens did it consume? Which tool calls failed? Is p99 latency within your SLA? Did cost spike after a model update?
Observability also catches operational failures fast. If your agent starts throwing 500s on 12% of requests, the error rate chart tells you immediately. If token consumption triples after a prompt change, cost monitoring flags it before your invoice does.
The OpenTelemetry ecosystem has made this layer fairly standard. Most agent frameworks emit spans that conform to the OpenTelemetry semantic conventions for AI, and a growing set of collectors, dashboards, and alerting tools consume them. Instrumentation is no longer the hard part.
What observability cannot tell you
Here is where the structural limit appears. Observability records the execution path, not the quality of the output at each step.
Consider a customer support agent that processes a refund request. Observability tells you: the agent called the refund API, the call returned 200, the conversation completed in 4.2 seconds, and it cost $0.003 in tokens. Everything looks fine.
What it cannot tell you: did the agent issue the refund for the correct amount? Did it follow your refund policy? Did it communicate clearly with the customer? Did it take an action the customer never asked for? A span with status "OK" says nothing about whether the underlying decision was correct.
This is not a tooling gap you can close by adding more spans. The span records the event; it cannot score the event against your intent. That is a categorically different operation.
The problem compounds over time. Quality can drift without any observable signal. If your agent's answer accuracy degrades from 91% to 74% over six weeks because a model update shifted response tendencies, no error rate moves. No latency changes. The dashboard stays green. Forrester 2026 data found that agents without automated evaluations have a 47% rollback rate, compared to 9% for agents with full eval coverage. The difference is not in what they observe; it is in what they measure.
What evaluation adds
Evaluation operates at the task level. It asks: given this input, did the agent produce a correct, appropriate, and policy-compliant output?
To answer that question you need four things that observability does not provide by default.
Defined criteria. You need a written statement of what "correct" means for each task type. For a coding agent, correctness might mean the generated code passes a test suite and does not introduce known vulnerability patterns. For a customer support agent, it might mean the response answers the user's question, cites the right policy, and does not promise something outside your terms of service.
Golden sets. A curated collection of inputs with known-good outputs lets you run regression tests the same way you would for deterministic code. When you change a prompt, swap a model, or update a retrieval index, you run the golden set and measure how many cases still pass.
LLM-as-judge with structured outputs. For cases where the output space is too large for hand-crafted ground truth, a second LLM can score the primary agent's output against your criteria. Research published in January 2026 found that LLM-as-judge methods achieve 80 to 90% agreement with human judgment at 500 to 5,000 times lower cost than manual review. The key is structured output: the judge returns a score, a pass/fail flag, and a short rationale, not free text. That makes the result queryable and aggregable across thousands of runs.
Action schema validation. For agents that take actions, correctness is not only about text quality. It includes whether the action itself was permitted and appropriate. An agent that calls a write API when only a read was warranted has passed every observability check and failed every evaluation check. Ghost actions, where agents take steps nobody requested, are one of the harder failure modes to catch with traces alone because the trace records the action as a success.
Why green dashboards coexist with failing agents
The DoorDash voice support deployment illustrates the gap concretely. Their Claude 3 Haiku-based IVR system achieved response latency under 2.5 seconds and reduced agent transfers by 49%. Those are operational metrics. But the 12% improvement in first-contact resolution and the reduction in transfers came from ongoing evaluation of whether the agent was actually resolving requests, not just completing them quickly. Latency and transfer rate are observable. Resolution quality required scoring.
Klarna's customer service agent handled 2.3 million chats in its first month with a two-minute resolution time, against eleven minutes for human agents. Measuring that resolution time is observability. Measuring whether those resolutions were accurate, whether refund amounts matched policy, whether customers required follow-up, is evaluation. The headline metrics are operational; the trust metrics require a separate layer.
The same pattern appears in the ServiceNow internal IT agent, which deflected 54% of support tickets. Deflection rate is measurable from logs. Whether those deflected tickets were actually resolved correctly, or just closed without resolution, requires scoring each case against defined criteria.
This is also why teams building internal ops agents often discover quality problems weeks after launch rather than at launch: the traces are clean, but no one is scoring the outputs.
How the two work together in a production loop
Observability and evaluation are not competing approaches. They operate at different layers and feed each other.
The production loop looks like this. Observability instruments every run: every span, every tool call, every token count, every error. That data is stored. Evaluation then runs continuously over that stored data, scoring outputs against defined criteria, flagging action schema violations, and tracking quality trends over time. The delta between "completed without error" and "completed correctly" is what you act on.
Concretely, the loop has three steps.
Observe everything. Capture complete traces for every agent run. Do not sample down to 10% to save cost; for agents taking consequential actions, you need the full record. This is both a quality requirement and an audit requirement. Teams working in regulated industries should read our notes on audit trail requirements for AI agents in regulated industries.
Evaluate continuously. Run your golden set on every prompt or model change. Run LLM-as-judge scoring on a statistically representative sample of live traffic, daily or more frequently depending on volume. Validate action schemas on every run, not just in testing. An autonomous background agent running overnight without schema validation is observable but unevaluated.
Act on the delta. The signal you care about is the gap between operational health (what observability gives you) and task quality (what evaluation gives you). A rising error rate is an ops incident. A falling quality score with a stable error rate is a regression that observability alone would never surface.
Prefactor, as a category example of a platform that does both, instruments agents via SDK to capture spans, then scores those spans against task-level criteria and activity schemas in a single pipeline, so the delta is visible in one place rather than split across two tools.
Questions to ask any tool that claims to do both
Observability vendors increasingly add "evaluation" features. Before you treat that as a complete solution, ask these questions.
Can it score task-level correctness against criteria you define, not just check that a call completed? A tool that flags "LLM returned an error" is doing observability. A tool that scores "LLM returned an answer that matches your policy" is doing evaluation. These are different capabilities.
Does it support golden set regression testing wired into your deployment pipeline? If quality can only be checked manually after the fact, you will find regressions weeks late.
Can it validate action schemas, not just log actions? Logging that an agent called a write API is observability. Flagging that the write API call was outside the permitted action set for that task type is evaluation. Teams thinking through what customers need before they trust an AI agent consistently cite action scope as a top concern.
Does the LLM-as-judge implementation return structured, queryable results, or free text? Structured scores aggregate across thousands of runs. Free text does not.
Can you query the quality trend over time, not just the current state? Quality drift is the failure mode that green dashboards miss. You need a time series of scores, not a snapshot.
For teams evaluating tooling across the full stack, the Langfuse alternative comparison and the AgentOps comparison both cover how observability-first tools handle, or do not handle, these evaluation requirements.
Where to start
Pick one agent in production and define correctness criteria for its three most common task types. Run your existing traces through an LLM-as-judge against those criteria and compare the pass rate to your current error rate. The gap between those two numbers is your baseline.
Start evaluating your agents and read the docs for SDK setup, schema validation configuration, and golden set management.