How to Build Agents on Top of Your APIs with Agentspan

Agentspan is presented as an execution layer for Python-defined AI agents that stores workflow state, tool history, execution IDs, and step-by-step traces on a server. Its `api_tool()` capability converts published OpenAPI, Swagger, or Postman API specifications into agent-callable tools, avoiding the need to manually maintain endpoint wrappers. The walkthrough demonstrates a service catalog API that lets an on-call assistant identify a service owner, review the latest production deployment, and find a relevant runbook. It emphasizes restricting the agent’s available operations with `tool_names` and `max_tools` to reduce model choice complexity and improve trace readability, while distinguishing `api_tool()` from tools for custom Python functions, individual HTTP endpoints, and MCP servers. Agentspan’s UI then lets users inspect the shared execution record, including API discovery, filtering, HTTP calls, LLM activity, and the final response under a single execution ID.

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