Monitoring an AI agent in production has to go well beyond the uptime and latency metrics that cover a conventional service, because an agent can be fully available, fast, and error-free by every conventional metric while quietly taking actions nobody intended. The failure mode that matters most for agentic systems isn't "the service is down." It's "the service is working exactly as designed and that design just got manipulated." That means monitoring needs to watch decisions and action patterns, not just system health, and it needs a baseline of what normal agent behavior actually looks like so deviations are visible against something concrete.
Log the decision, not just the outcome
A log line saying a tool call succeeded tells you almost nothing about whether it should have happened. Effective agent monitoring captures the full decision context: what triggered the action, what identity and authorization applied, what policy check ran, and what the actual outcome was. Without that context, investigating a suspicious action after the fact means reconstructing from scratch something that should have been captured automatically at the time.
Build a baseline before you look for anomalies
You can't detect unusual agent behavior without first knowing what usual behavior looks like, tool-call frequency per session, typical action sequences, normal cost per task, common data access patterns. Teams that skip this step end up either missing real anomalies because nothing is flagged, or drowning in false positives because every alert threshold was guessed rather than measured against real traffic.
Track cost and resource usage as a security signal
A sudden spike in tool calls, tokens processed, or spend per session is often the earliest visible sign of a loop, a cost abuse attempt, or a manipulated agent doing far more than a normal task requires. Routing this data only to a finance dashboard misses its value as a leading security indicator, one that's frequently available before any more sophisticated behavioral analysis would catch the same problem.
Watch for action sequences that don't match the task
Beyond individual tool calls, the sequence and combination of actions within a session carries signal. An agent that normally reads a customer record and drafts a response, but in one session reads a customer record, queries three other unrelated accounts, and then exports data, is exhibiting a pattern worth flagging even if every individual step, in isolation, looks like a legitimate tool call.
Alert with urgency that matches actual impact
Not every anomaly deserves the same response. A minor deviation in an agent with only read access to non-sensitive data can wait for a daily review. A deviation in an agent that can issue payments or modify production infrastructure needs real-time alerting and, ideally, an automatic pause pending review. Tuning alert urgency to actual blast radius keeps the monitoring system useful instead of generating noise that gets ignored.
Review monitoring data even when nothing alerts
Some of the most useful findings come from periodic manual review of agent activity logs, not from an alert firing, because behavior that's technically within normal bounds can still reveal a permission that's broader than it should be, or a workflow drifting slowly away from its intended design. Scheduled review catches what threshold-based alerting structurally can't.
A dashboard nobody owns is not monitoring
Collecting the right data and displaying it somewhere is only half the job. Monitoring only works as a control if someone is actually responsible for looking at it regularly and has the authority to act, pause an agent, tighten a permission, escalate a finding, when something looks wrong. It's common to see teams build genuinely good observability dashboards for their agents and then have nobody check them outside of an active incident, which means the monitoring exists on paper but isn't functioning as a control in practice. Assigning explicit ownership for reviewing agent monitoring data, on a defined cadence, is as much a part of the monitoring program as the instrumentation itself.
Where TELEON fits
TELEON's audit trail of agent and tool activity is built to support exactly this kind of monitoring: identity, policy decision, and outcome captured for every tool call, giving teams the decision-level context needed for both automated anomaly detection and manual review, across agents regardless of which framework built them. Trust scoring adds a running signal for when a given agent's or user's behavior starts deviating from its established pattern.
Defining the specific baselines, thresholds, and alert routing for a given production system is work that has to reflect that system's own normal traffic and risk tolerance.
A short checklist
- Capture full decision context per tool call: trigger, identity, policy result, outcome.
- Build a measured baseline of normal agent behavior before setting alert thresholds.
- Track cost and resource usage as a security signal, not only a finance metric.
- Monitor action sequences and combinations, not just individual tool calls in isolation.
- Tune alert urgency to the actual blast radius of the agent involved.
- Schedule periodic manual review of activity logs, not only automated alerting.
- Revisit baselines whenever an agent's task, tools, or scope changes meaningfully.
Monitoring an agent well means accepting that "working as designed" and "working safely" are two different claims, and only one of them shows up in a conventional uptime dashboard. The teams that catch real problems early are the ones watching what the agent actually decided to do, session by session, not just whether it kept responding.