AI Agent Handbook Understand Monitoring and Observability

Monitoring and Observability

Once your LLM systems are live, everything depends on what they do — and why.

Agents operate as probabilistic systems.

They generate answers, make tool calls, retrieve documents, and decide next steps.

But without the right observability, you’re flying blind.

This section will show you how to track, debug, and understand agent behavior across workflows, tenants, and time.

What You Need to Monitor

At a minimum, you should be capturing:

  • Prompt + completion: including latency, token usage, model used
  • Tool invocations: tool name, input parameters, outputs
  • Document retrievals: query used, docs returned, source metadata
  • Execution flow: which agent(s) were involved, and in what order
  • User + tenant context: who triggered what and when

For each request, you want a full trace of input → decision → output.

How Orcaworks Supports Observability

Orcaworks supports deep observability out of the box:

  • Built-in request tracing for every agent run
  • Exportable logs for model calls, tool calls, and retrievals
  • Support for OpenTelemetry and custom backends
  • Usage dashboards by org, user, and tenant

We provide structured data, so you can:

  • Build dashboards
  • Set alerts for anomalies (latency spikes, failure rates)
  • Investigate incidents across agents and workflows

LMS Example: Tracking Agent Drift

Say you’re auto-marking thousands of student answers a day.

Initially the agents work well.

But performance starts drifting.

Some answers are being graded inconsistently.

With Orcaworks observability, you can:

  • Compare prompt/completion diffs over time
  • Audit changes in retrieved examples from RAG
  • Detect shifts in tool behavior or failure patterns
  • Flag agents that deviate from baseline evaluations

You get visibility, versioning, and control — without adding complexity to your codebase.

Recap: Why Observability Matters

LLMs are probabilistic — same input may yield different outputs.

Issues may show up only after scale.

This includes token costs, poor answers, and flakiness.

Observability gives you confidence, control, and context.

With Orcaworks, monitoring isn’t an afterthought.

It’s built-in from day one.