Capability · AI agent monitoring
AI agent monitoring for production systems
Agents fail in ways uptime checks miss: they loop, call the wrong tool, run up a bill or finish confidently with the wrong answer. Here are the metrics and alerts that catch those failures, and what to do when one fires.
Last reviewed by the Perimattic AI Suite team
In short
How do you monitor AI agents in production?
Monitor outcomes, behaviour and cost together. Track whether sessions complete their task, how many steps and retries they take, tool error rates, cost per session, quality scores on the final answer and how often a human has to take over. Alert on sustained changes in those numbers, and link every alert to the traces behind it so the on-call engineer can see the failing step.
Monitoring vs observability
Observability is the data: the full trace of each agent session. Monitoring is the small set of numbers you watch and alert on from that data.
Start with five numbers
Task success rate, steps per session, tool error rate, cost per session and escalation rate cover most production incidents.
Capabilities
Agent monitoring in Perimattic AI Suite
Alerts on agent behaviour
Thresholds on errors, latency, steps, cost and quality, built on the same traces your engineers debug with.
Signal it produces: Alert history linked to sessions
Cost per agent and per user
Token spend split by agent role, feature and end user, with budget alerts.
Signal it produces: Cost per session and per user
Quality on live traffic
Faithfulness and custom LLM-as-judge scores on sampled sessions.
Signal it produces: Quality trend per agent
Incident records
Alerts, affected sessions and resolution kept together for later review.
Signal it produces: Incident file for audits
Metrics
Agent monitoring metrics and example alerts
Thresholds below are starting points. Set your own from a few weeks of baseline data.
| Metric | What it reveals | Example alert (starting point) |
|---|---|---|
| Task success rate | Whether sessions reach a correct end state | Drops 10 points below its 7-day average for 30 minutes |
| Steps per session | Loops, dead ends and planning failures | p95 steps doubles against baseline |
| Tool error rate | Broken integrations, bad arguments, permission problems | Any single tool above 5% errors for 15 minutes |
| Cost per session | Runaway loops and expensive model choices | p95 cost per session above budget for an hour |
| Latency per session | Slow tools, slow models, queueing | p95 session time above your SLO for 15 minutes |
| Answer quality | Wrong or unsupported final answers | Faithfulness score average falls below your threshold |
| Escalation rate | How often a human has to take over | Rises 50% week on week |
| Injection attempts | Attacks through inputs, documents or tool results | Attempts from one source above normal levels |
Service levels
Set service level objectives for agents
Classic SLOs cover availability and latency. Agents need two more: an outcome objective and a cost objective. Writing them down gives on-call engineers and product owners the same definition of “broken”.
Availability
Share of sessions that start and finish without a system error.
Example: 99.5% of sessions per 30 days
Latency
Time from request to final answer for most sessions.
Example: p95 under 20 seconds
Outcome
Share of sessions that complete the task correctly, measured by evaluation or review.
Example: 90% task success on sampled sessions
Cost
Spend per session or per resolved task, against a budget.
Example: p95 under your per-task budget
Runbook
When an agent alert fires
Open the traces
Go from the alert to a sample of affected sessions and find the step where they diverge.
Classify
Is it a tool outage, a model or prompt change, bad input data, or an attack?
Mitigate
Roll back the change, switch to a fallback model, disable the tool or route to a human.
Record
Keep the incident with its traces. Regulated teams reuse it as SOC 2, DORA or EU AI Act evidence.
FAQ
Common questions
Short answers to the questions engineering and platform teams ask most often.
What is the difference between AI agent monitoring and agent observability?
Agent observability captures the full trace of each session: every model call, tool call and handoff. Agent monitoring is the set of metrics and alerts you run on top of that data. You need observability to explain an alert; you need monitoring to know when to look.
Which metrics matter most for AI agents?
Task success rate, steps per session, tool error rate, cost per session and escalation rate. Add answer quality scores and injection attempts once the basics are in place.
How do I measure task success for an agent?
Define what “done” means for each task, such as a ticket resolved or a form completed correctly. Measure it from the final state where you can, and from evaluation or human review on a sample where you can’t.
Can I use my existing APM for agent monitoring?
For latency and errors, yes. APM tools don’t usually understand agent steps, tool calls or answer quality, so teams send the same OpenTelemetry data to their APM and to an AI observability tool. Read Perimattic vs Datadog for how that works in practice.
Go further
Related tools, guides and services
- Free toolAI agent complexity estimatorEstimate build and run complexity before you design your monitoring.Open
- Free toolAI observability maturity assessmentSee which monitoring and tracing practices you already have.Open
- ArticleAI agent development cost in 2026What drives the cost of building and running agents.Open
- ServiceAI model monitoring servicesPerimattic engineers set up monitoring, alerting and runbooks.Open
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