AI Agents & Automation

Agent Monitoring & Optimization

Once agents are running unattended, when the question shifts from whether they work to whether they still work.

Monitoring covers what agents did and why, plus the evaluation needed to tell whether a change improved behaviour or merely changed it. Without evaluation, tuning an agent is guesswork with a confident narrator.

For agents specifically, the plan matters as much as the actions. Understanding why an agent did something requires seeing what it intended.

What the work includes

  • Run-level observability covering intent, actions, and outcomes
  • Evaluation suites so changes can be measured rather than asserted
  • Alerting on behaviour drift and anomalous run patterns
  • Cost and latency tracking per agent and per run
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// Common Questions

Questions
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Common questions about Agent Monitoring & Optimization.

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Whether they still work, not just whether they run. That means output quality over time, escalation rates, cost per task, and cases where behaviour changed without the configuration changing — usually the signature of a model or upstream data shift.

Only by measuring output quality continuously against a baseline captured when the agent was known good. Degradation rarely announces itself: an agent that has become subtly worse still returns confident answers, and without a baseline there is nothing to compare them against.

Changes underneath it — a model version, a data source, a schema, a document set that has drifted from what the prompts assumed. The agent's own configuration is often the last thing to have changed, which is why monitoring watches outputs rather than config.

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