10decoders
Picture the Dashboard Everyone Is Proud Of
Response time sits at 400 milliseconds. Error rate is under one percent. Token usage is flat. The on-call channel has been quiet for three weeks. Then a customer success lead forwards a screenshot of the agent telling a client that a refund window is 90 days when the policy says 30, and it turns out the agent has been saying that for a month.
Nothing on the dashboard was broken, because the dashboard was built for a different kind of system. Traditional monitoring asks whether a service is up, fast, and free of exceptions. An agent can be up, fast, and exception free while retrieving a stale policy document, misreading a tool result, or drifting after a silent model update. The failure is in the content of the answer, and HTTP status codes do not carry content.
That is the core of the observability gap. Infrastructure telemetry tells you the agent ran. It says nothing about whether the run was any good. Closing that gap takes three things layered on top of what you already have: traces that show every step the agent took, scores that judge the output on live traffic, and alerts that fire when those scores move. Most teams have the first in partial form, the second rarely, and the third almost never.
A green dashboard proves the agent is running. It has never once proved the agent is right.
Where Agent Observability Goes Blind
| Blind spot | What closes it | Severity |
|---|---|---|
| Latency and error dashboards stay green while answers are wrong | Quality scoring on a sample of live production traffic | Critical |
| Only the final model call is logged, not tool calls or retrieval | End to end traces with a span for every step and handoff | Critical |
| A model or prompt change ships with no before and after comparison | Version tags on every trace and score trends split by version | High |
| Cost is reviewed monthly, after a runaway loop has already run | Per request token and tool spend with a hard alert threshold | High |
| Traces store raw prompts containing customer data | Redaction at capture and short retention on sensitive fields | Moderate |
| Agent telemetry lives in a separate tool the platform team never sees | OpenTelemetry based export into the existing observability stack | Lower |
Not sure what your agent monitoring can actually see?
10decoders reviews your agent traces, quality checks, and alerting against what a production incident would need. You get a ranked list of blind spots and a scoped plan to close them.
Book a Free AI Assessment →Tracing Tells You What Happened. Scoring Tells You Whether It Was Good
Teams often treat these as one project and stop after tracing. Tracing is worth doing first, because a single user request in an agent system can involve several model calls, tool invocations, vector lookups, and a handoff between agents. When something goes wrong, you need the whole chain in front of you. Among teams that already run agents in production, LangChain reports that 71.5 percent have full tracing that lets them inspect individual steps and tool calls.
A trace is a record, though, not a verdict. Someone still has to read it and decide the answer was wrong, and nobody reads thousands of traces a day. Scoring is what automates that judgment: a grader, whether a rule, a model, or a human review queue, rates a sample of production outputs for correctness, groundedness, or task completion, and the scores become a time series you can alert on. Gartner's analysts describe the split the same way. Explainability shows why a model answered as it did, while observability checks that the behavior holds up over time and can be relied on.
The practical consequence is that quality becomes a metric with a threshold, like error rate. If the share of grounded answers drops from 94 percent to 86 percent after a Thursday deployment, you want that visible on Friday morning, not in a customer escalation two weeks later. Offline test sets still matter for release gating, but they are frozen samples. Production traffic changes daily, and the only way to see how the agent handles it is to measure the traffic itself.
Infrastructure Monitoring
Uptime, latency, and error rates are tracked. The agent is treated like any other service, so wrong answers only surface through user complaints.
Tracing Without Judgment
Full traces exist and engineers can debug a reported failure. Nothing scores live outputs, so problems are found only after someone reports them.
Traced, Scored, and Alerting
Live traffic is sampled and graded, scores are split by model and prompt version, and a quality drop pages a named owner with a written first step.
Production Agent Observability Checklist
Run this against every agent that touches a customer, a payment, or a regulated record.
Before You Call an Agent Observable
Add the quality score to the dashboard before the next release, or wait for a customer to add it for you.
What to Do This Week
01 Pick one agent and trace a single real request end to end
Choose the agent closest to a customer or a financial decision, send one real request through it, and check whether you can see every model call, tool call, and retrieval result in order. Whatever is missing from that view is your first instrumentation ticket, and it is usually the tool calls.
02 Define one quality score you can compute automatically
Start narrow. For a retrieval agent that might be whether the answer is supported by the retrieved passages. For a workflow agent it might be whether the final task state matches the request. Write the rule down, run it on fifty recent production traces, and read every failure by hand to check the grader is not fooling you.
03 Tag every trace with the model version and prompt version
This is a small change with large payoff. Once traces carry version tags, you can compare scores before and after any deployment or vendor model update and see quickly whether a change helped or hurt. Without tags, a quality drop has no obvious cause and the investigation starts from zero.
04 Set one alert on the quality score and name the person it wakes
Pick a threshold from the fifty traces you just reviewed, route the alert to a named engineer, and write two lines describing what that person does first. An alert with no owner and no first step will be muted within a month, which returns you to the green dashboard problem.
Let 10decoders Close the Observability Gap in Your AI Agents
We review your traces, live quality scoring, cost signals, and alert ownership, then deliver a ranked gap list and a plan to reach production grade observability without rebuilding your stack.