How to Build a Self-Improving Agent with Google ADK

CopilotKit’s tutorial explains how to make an existing Python-based Google ADK agent improve over time by analyzing saved and future conversations through CopilotKit Intelligence’s Automatic Learning feature. The process involves connecting the ADK application while preserving its existing models, tools, authentication, and session handling, creating a Learning space for a specific workflow, and importing durable session history from database or Vertex AI/Agent Engine stores. Users then select relevant conversation threads as evidence, run an initial analysis to identify recurring requests and Product Insights, and review proposed reusable Skills before approving and publishing them. Approved Skills are delivered to the agent through a Python adapter that adds a SkillToolset and maintains a SkillRegistry during the application lifecycle, allowing the agent to load applicable instructions on later requests without replacing developer-authored guidance. The tutorial recommends testing the new behavior on a fresh task and checking publication, tool calls, and refresh timing if results do not change. After setup, eligible new conversations flow into Intelligence, nightly learning runs can analyze recurring patterns once sufficient threads are available, and reviewed Skills can be automatically delivered to selected ADK agents.

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