Agent observability: 7 proven, risky, hidden checks before launch

Table of Contents

Intro: the agent worked on Friday, then Monday happened

You ship a new agent build late Friday. On Monday morning, it starts writing the wrong values into your CRM. Nobody notices until the forecast looks weird and a VP asks uncomfortable questions. You have logs, but they only show the final answer. So you can’t tell whether the failure came from retrieval, a tool call, or a simple timeout.

That’s why end-to-end visibility into agent runs is now a production requirement. Agents are probabilistic, multi-step, and tool-driven. As a result, you need visibility into plans, tool calls, retrieval, memory, and outcomes, not just prompts and responses.

In this article you’ll learn…

What to instrument in an agent run, step by step.
Which metrics catch failures early, including cost blowups.
A practical checklist for traces, evals, alerts, and release gates.
The main risks of logging too much, plus how to reduce them.

Why this became urgent in 2025 (and why it gets costly fast)

In 2025, a clear pattern emerged: teams stopped treating observability as a nice-to-have. Instead, they started treating it like payment processing or authentication. If it breaks, your business breaks.

Meanwhile, tooling has converged around a common loop: traces plus evaluations plus monitoring. That matters because it helps you reproduce a single failure and also measure whether your next release improved anything.

Comet captures the problem well: “They can fail in ways that are hard to predict or reproduce.” When your agent takes actions, that unpredictability becomes expensive and sometimes dangerous.

What to instrument: the surfaces that actually explain failures

Most teams log the final response first. Then they hit the wall. The final response is often the least helpful clue, because the real mistake happened earlier.

Instead, instrument an agent run like a distributed system. First, generate a single trace_id for the user request. Next, create step spans so you can see exactly where time, cost, and errors appear.

At minimum, capture these surfaces:

Planning output. The plan, next action, or tool selection decision.
Tool calls. Tool name, sanitized arguments, response, status, and timings.
Retrieval. Query, top-k doc IDs, scores, and index or collection name.
Memory reads and writes. What was read, what was written, and why.
Model inputs and outputs. Prompt template version, model version, and completion metadata.
User and tenant context. Role, permissions, and feature flags that shape behavior.

However, don’t log raw sensitive payloads by default. If you do, you may create a compliance incident while trying to prevent one.

A practical framework: TRACE, EVAL, MONITOR, GOVERN

If you’re setting this up from scratch, use a simple four-part loop. It keeps work focused and avoids dashboard theater.

1) TRACE: make every run replayable

Tracing answers one question: “What happened?” If you can’t replay a run, you will argue about it instead of fixing it.

A quick checklist you can implement this week:

Generate a trace_id at request start and propagate it everywhere.
Create a span for each agent step, including planning and self-check steps.
Capture tool inputs and outputs as separate spans, with redaction.
Store the model name, model version, temperature, and prompt template revision.
Record retrieval doc IDs and chunk IDs so you can reproduce RAG results.

In practice, this is distributed tracing applied to LLM workflows. Once you have it, debugging gets boring in the best way.

2) EVAL: measure quality, not vibes

Evaluation answers: “Was it good?” Without evals, you’ll ship based on anecdotes and one dramatic screenshot in Slack.

Use two layers. First, run offline evals on a fixed dataset of real tasks. Then, run online evals on sampled production traffic. This is where evaluation and release engineering meet.

For example, track these evaluation dimensions:

Task success. Did it complete the workflow correctly.
Groundedness. Did it cite or use retrieved sources appropriately.
Policy compliance. Did it avoid disallowed actions and data access.
User effort. How many turns or corrections were needed.

Comet notes that platforms help you “trace requests, evaluate outputs, monitor performance, and debug issues before they impact users.” That is the loop you’re building.

3) MONITOR: catch incidents and slow rot

Monitoring answers: “Is it healthy over time?” Many agent failures are not dramatic. They are slow drift, rising latency, or gradually higher tool error rates.

Watch three buckets, and alert on leading indicators:

Quality signals. Success rate, escalation rate, and policy violations.
Ops signals. Latency per step, timeout rate, tool error rate, and queue backlogs.
Cost signals. Tokens per run, retries, and tool vendor costs.

Next, set alerts on precursors. For instance, a spike in tool retries often shows up before a full outage.

4) GOVERN: keep humans in the loop for risky actions

Governance answers: “Should the agent be allowed to do this?” Observability is not only about performance. It is also about accountability when the agent can change real systems.

So, log and enforce these basics:

Approval gates for high-impact actions, like refunds or record deletes.
Role-based access control for tools and data sources.
Pre-action policy checks before writes to production systems.
Audit trails that tie actions back to a user request and trace_id.

Two mini case studies (because theory is cheap)

It’s easier to design observability when you picture real failure modes. Here are two you can borrow.

Case study 1: the support agent that quietly spammed refunds

A mid-market SaaS team let a support agent issue refunds through an internal tool. After a prompt update, the agent began refunding edge cases that should have been denied. The chat transcripts looked fine, which made the issue hard to spot.

Because the team had step-level traces, they found the exact step where the agent misread the policy snippet retrieved from the knowledge base. Then they added an eval that checks refund eligibility against known scenarios. As a result, refunds returned to baseline within a day.

Case study 2: the RAG agent with a sneaky latency trap

Another team shipped a RAG agent that drafted compliance answers for internal users. Over a month, latency doubled. Nobody changed the prompt, so the team blamed the model provider.

Tracing showed the real culprit: retrieval started hitting a slower index after a migration. They added monitoring for retrieval p95 and an alert when it drifted. Consequently, the next infrastructure change didn’t become a surprise outage.

Common mistakes (the rakes teams keep stepping on)

Even strong teams repeat the same errors. The good news is that each one has a clean fix.

Logging only final answers, not intermediate steps and tool results.
Missing correlation IDs across tools, queues, and downstream services.
Running evals once, then never turning them into a release gate.
Alerting on vanity metrics instead of failure precursors.
Storing sensitive prompts and user data without redaction and retention rules.
Ignoring cost metrics until finance asks why bills jumped.

However, you can fix most of these with a disciplined schema and a weekly trace review habit.

Risks: what can go wrong when you add observability

Observability reduces production risk. On the other hand, it introduces new risks if you are careless.

Privacy and compliance risk. Traces can include PII, secrets, or regulated data.
Security risk. Logs become a high-value target if they contain tool outputs.
Cost risk. High-cardinality logging can explode storage and query costs.
False confidence risk. Evals can miss edge cases, so you ship regressions.
Alert fatigue risk. Too many alerts means the real incident gets ignored.

So start with data minimization, aggressive redaction, and short retention windows for raw payloads. Then keep only what you truly need for debugging and audits.

Tooling: what to look for (without buying a dashboard you won’t use)

You don’t need the fanciest charts. Instead, you need a system that matches how agents fail: across steps, tools, and handoffs.

Look for capabilities like these:

End-to-end tracing with step spans and tool call capture.
Dataset management for evals and regression testing.
Collaboration features so PMs and engineers can review runs together.
Easy integration with your agent framework and your data stack.

Comet’s LLM observability overview.

Langfuse on agent observability.

Maxim AI’s 2025 tools roundup.

What to do next: a practical setup plan (3 steps)

If you want real progress this week, keep it simple. Pick one production agent and instrument it end to end. Then expand.

Map the workflow. List each step, tool call, and external dependency.
Add trace propagation. Implement trace_id and step spans across tools and queues.
Ship a small eval gate. Build a dataset of 50 real tasks and run it before prompt changes.

Then add these quick wins:

Sample 1% to 5% of runs for deeper payload logging and replay.
Redact PII fields at ingestion, not later.
Set a per-run cost budget and cut off suspicious loops.
Review the top 10 failed traces every week with a clear owner.

Explore more Agentix Labs resources on production AI agents.

FAQ

What is agent observability in plain English?

It’s your ability to see what the agent did step by step, and to measure whether it keeps doing the right thing over time.

Do I need distributed tracing for a single agent?

Often yes. Even a “single” agent calls tools, APIs, vector stores, and internal services. Those boundaries are where failures hide.

What should I log first if I’m starting from zero?

Start with trace IDs, step spans, tool calls, and retrieval doc IDs. Those usually explain the majority of production incidents.

How do I evaluate an agent without humans reviewing everything?

Use a labeled dataset plus automated checks for policy, format, and grounding. Then sample production runs for spot checks and escalation review.

How do I control token and tool costs?

Monitor tokens per step, detect loops, and set per-run budgets. Also cache safe tool outputs and retrieval results where it makes sense.

How often should I run evals?

Run them before every release. In addition, run nightly on a rolling sample to catch drift early.