AgentOps: Monitoring, Evaluation and Guardrails
The operations layer that production agents need and rarely have: tracing of every decision, evaluation on whole trajectories, spend and step ceilings, guardrails, and an incident process that works when nobody is watching.
Agents are non-deterministic software with permissions. Ordinary application monitoring will tell you the request succeeded; it will not tell you that the agent reached the right answer by a route nobody would sanction, or that it has been retrying the same failing call for four hours. AgentOps is the layer that closes that gap.
AgentOps is the operational discipline for agents in production: tracing every decision, tool call and outcome; evaluating whole trajectories rather than final outputs; enforcing spend, step and time ceilings; applying guardrails at the input and output boundary; and providing the alerting, circuit breakers and incident process that make an autonomous system safe to run.
What ordinary monitoring misses
| Signal | Standard observability | AgentOps |
|---|---|---|
| Success | HTTP 200 returned | The task was actually completed correctly |
| Route | Not captured | Which tools were called, in what order, and why |
| Cost | Aggregate spend | Cost per task and per tool, with outliers surfaced |
| Quality | Not captured | Trajectory scored against agreed correct behaviour |
| Safety | Not captured | Injection attempts, refusals, boundary violations |
| Stuck behaviour | Looks like normal traffic | Loop detection, step ceilings, stall alerts |
The row that surprises teams most is the first. An agent can return a perfectly formed success while having done the wrong thing, and without trajectory-level capture nobody finds out until a customer does.
What we put in place
Trajectory tracing
Every run captured as a structured trace: the plan, each tool call with its arguments and result, each decision point, token and time cost per step, and the final outcome. Traces are replayable, which turns a vague complaint into a specific defect in minutes rather than an afternoon of speculation.
Evaluation on decisions, not just answers
A suite of real cases with correct outcomes and acceptable routes, scored on both, running as a release gate. An agent that reached the right result by calling a write tool twice fails, because that behaviour will eventually produce a duplicate that matters. This extends the evaluation practice described under LLM evaluation and benchmarking.
Ceilings and circuit breakers
Hard limits on steps, wall-clock time and spend per task and per day, with loop detection. A single tested switch halts the agent across every environment, owned by a named person, exercised before launch rather than during the incident it was built for.
Guardrails at the boundary
Input filtering for injection attempts, output validation against schema and policy, refusal behaviour for out-of-scope requests, and blocking of actions that exceed the agent's permission scope regardless of what it decided to do. Guardrails are enforced in code, not requested in a prompt.
Alerting that means something
Alerts on quality regression, escalation rate change, cost per task drift, injection attempts, stalls and failure clusters, routed to whoever is on call with the trace attached. We tune these to be rare enough to be believed, because an alert nobody acts on is worse than no alert.
An incident process for agents
Written before it is needed: how to halt, how to assess what the agent did while misbehaving, how to reverse what is reversible, who is told, and how the case enters the evaluation set so the same failure is tested for ever after.
Retrofitting tracing costs several times more
Adding trajectory capture to an agent already in production usually means restructuring how state and decisions flow, which is why we build it in from the first milestone on our own agent engagements. If you already have an agent without it, this engagement exists precisely to close that gap.
The numbers you should be watching
- Task success rate, verified against the real outcome rather than against a returned status.
- Escalation rate and reasons, trended, because a change here is usually the first sign of drift.
- Cost per completed task, with outliers surfaced individually rather than averaged away.
- Steps per task, where a rising median means the agent is working harder for the same result.
- Guardrail activations, including injection attempts and blocked actions.
- Time to detect and halt, measured in exercises rather than assumed.
How the engagement runs
The kill switch and the incident process are tested before anything else is declared finished.
Assessment
Current agents reviewed for tracing, evaluation, ceilings and controls, with the gaps ranked by risk.
Tracing and dashboards
Structured trajectory capture, replay, and dashboards for success, cost, steps and escalation.
Evaluation suite
Real cases with correct outcomes and acceptable routes, scored on both, wired into CI as a release gate.
Guardrails and ceilings
Input and output guardrails, permission enforcement, spend and step ceilings, loop detection, circuit breaker built and tested.
Incident readiness and handover
Written incident process, an exercise run with your on-call team, and handover of dashboards and runbooks.
What you receive
The controls, the measurement and the practised response, handed to the team that will own them.
Trajectory tracing
Structured, replayable capture of plans, tool calls, decisions, costs and outcomes.
Trajectory evaluation suite
Cases scored on outcome and route, running in CI as a release gate.
Guardrail layer
Input and output validation, injection defence, permission enforcement and refusal behaviour.
Ceilings and circuit breaker
Step, time and spend limits with loop detection, plus a tested cross-environment kill switch.
Operational dashboards
Success, escalation, cost per task, steps per task and guardrail activations, with alerting.
Incident runbook
Halt, assess, reverse, communicate and learn, exercised once with your on-call team before handover.
Is this the right engagement?
Worth being direct. AgentOps: Monitoring, Evaluation and Guardrails is the wrong spend in some situations, and those are listed rather than buried.
Good fit if
- An agent is in production and nobody can reconstruct why it did something last Tuesday.
- Agent spend is unpredictable or has produced a surprise.
- There is no single tested way to stop the agent quickly.
- Quality is assessed by complaint rather than by measurement.
- Security or risk has asked what controls exist around autonomous actions.
Choose something else if
- No agent exists yet, in which case this is built into the agent engagement instead.
- The agent is a short-lived experiment that will never take real actions.
- Nobody will be on call for it, which means the alerting has no recipient.
- The requirement is model quality rather than operational control. See LLM evaluation.
Frequently asked questions
Marked up with FAQPage schema so these answers can surface directly in search results and inside AI assistant responses.
What is AgentOps?
The operational layer for agents in production: tracing every decision and tool call, evaluating whole trajectories rather than just outputs, enforcing spend and step ceilings, applying guardrails at the boundary, and providing alerting, a circuit breaker and a rehearsed incident process.
Why is standard observability not enough for agents?
Because it reports that a request succeeded, not that the task was done correctly or by an acceptable route. Agents fail in ways that look like success: partial completion, an unsanctioned route, a quiet loop. Those are only visible if the trajectory itself is captured.
What does trajectory evaluation mean?
Scoring the sequence of decisions as well as the final result. An agent that produced the right answer after calling a write tool twice has a defect that an output-only test will never catch, and that defect will eventually produce a duplicate that someone has to unpick.
How do you control agent costs?
Ceilings on steps, wall-clock time and spend per task and per day, loop detection, and cost per completed task reported with outliers surfaced individually. Averages hide the runaway task, which is exactly the one you need to see.
Can this be added to agents we built ourselves, or with another supplier?
Yes, and that is the most common version of this engagement. We assess what exists, add tracing and evaluation around it, and put the ceilings and controls in place without rebuilding the agent, unless the assessment finds something that genuinely has to change.
Often paired with this
Most clients combine two or three engagements from the AI Agents & Agentic Automation pillar. These are the ones that most often run immediately before or after.
Custom AI Agent Development
An agent that completes work in your systems, with scoped tools, trajectory evaluation and a circuit breaker.
Read more →Multi-Agent System Orchestration
Several agents coordinated with explicit hand-off contracts and contained failure, only where one agent genuinely will not do.
Read more →Agent Framework Migration
Move between agent frameworks with behaviour parity proven by evaluation, and a tool layer that survives the next move.
Read more →Is this the right engagement?
Tell us what you are trying to build. If a different service fits better, or if you do not need us at all, we will say so.