Monitoring and Improving AI Agents with Logfire

I demonstrate how to use Logfire to inspect agent runs and identify opportunities to improve an AI agent after deployment. A travel-replanning agent provides a concrete example, covering prompt and tool-description changes, runtime errors, and managed variables.

■ Agent monitoring and improvement
- Reviewing runs, costs, traces, tool calls, and runtime exceptions in Logfire
- Applying suggested changes to prompts and tool descriptions through managed variables
■ Related capabilities
- Using the MCP server for agent monitoring tasks such as dashboards and alerts
- Discussing evaluation-case generation as a feature under development

For developers building Python-based AI agents, this walkthrough offers a practical view of post-deployment monitoring and iteration. You can use the approach to review your own agent runs and decide which prompt or runtime changes to make; the presentation is a product walkthrough rather than a full implementation tutorial.