AI agent monitoring that tells you what your agent did, and why.
When an agent runs in production, you need to see every tool call, retrieval, and decision, catch quality and cost drift before users do, and roll back fast when something breaks. This is what AI agent observability covers, and where it is worth the effort.
AI agent observability is the practice of capturing and inspecting every step a production agent takes, its prompts, tool calls, retrievals, decisions, cost, and outcomes, so you can monitor quality, detect drift, alert on failures, and roll back with confidence.
Bring one messy workflow. We will show whether an agent, automation, SaaS product, or no build is the right next move.
Traces: the unit of agent observability
A trace is the full record of one agent run: the prompt it received, each reasoning step, every tool call and retrieval, the data that came back, and the final action. Without traces you are debugging a black box from user complaints. With them you can replay any run and see exactly where it went wrong.
- Every tool call and its arguments and result
- Every retrieval and the chunks it pulled
- Each decision and the step that triggered it
Evals in production, not just before launch
Pre-launch evals catch known failures. Production evals catch the ones real traffic surfaces: new edge cases, prompt regressions after a model update, retrieval that quietly went stale. Scoring a sample of live runs against a rubric turns vague reports into a measurable quality line you can watch.
- Score live runs on a sampled basis
- Compare quality across model and prompt versions
- Flag regressions before they spread
Low confidence, policy exception, or protected data.
Cost, quality, and drift monitoring
Three things move once an agent is live: cost per run, output quality, and the distribution of inputs it sees. Token spend can triple after a prompt change, quality can slide after a model update, and inputs drift as the business changes. Monitoring all three on dashboards means you find out from a chart, not an invoice or an escalation.
- Cost per run and per workflow over time
- Quality scores trended by version
- Input and output drift against a baseline
Alerting and rollback
Observability only pays off if it shortens the time between a problem starting and a human knowing. Alerts on error rate, latency, cost spikes, and eval-score drops route to the owner. A one-step rollback to the last known-good prompt, tool config, or model version contains the damage while you investigate.
- Alerts on errors, latency, cost, and eval drops
- Named owner and on-call, not a shared inbox
- One-step rollback to the last good version
p95 latency 1.2s
eval pass 12/12
rollback ready
Where heavy observability is overkill
Not every agent needs full tracing and live evals. A low-stakes internal tool that drafts text for a human to review, runs a handful of times a day, and touches no systems of record does not justify the instrumentation overhead. Match the depth of observability to the blast radius: the more autonomous, high-volume, or consequential the agent, the more you need. For a read-only assistant with a human in every loop, basic logging and a cost cap are enough.
- Human reviews every output before it acts
- Low volume and no writes to real systems
- Start with logs and a cost cap, add tracing as stakes rise
Concrete places agents earn their keep.
Policy matched. Refund ready for approval.
Traces
The full record of each run: prompt, every tool call, every retrieval, each decision, and the final action, replayable end to end.
Production evals
Live runs scored against a rubric on a sampled basis, so quality is a measurable line you watch, not a guess.
account score
Cost monitoring
Token spend per run and per workflow, trended over time, so a prompt change that triples cost shows up on a chart.
Drift detection
Input and output distributions compared against a baseline, flagging when the traffic or the agent's behavior shifts.
Alerting
Thresholds on error rate, latency, cost spikes, and eval-score drops, routed to the owner and on-call.
Rollback
A one-step revert to the last known-good prompt, tool config, or model version when something drifts.
Common questions.
What is AI agent monitoring?+
What is the difference between logging and agent observability?+
What should I monitor for an AI agent in production?+
What is drift detection for AI agents?+
When is full agent observability overkill?+
Does Gaper build observability into the agents it ships?+
Want agents like these in your stack?
Book a free assessment, we'll map where an AI agent creates real leverage in your workflows and scope the first one to ship.