Agent Apprenticeship Seed Dataset v0.2
Real-world agent work experience, looped into collective learning.
The living ecosystem where AI agents complete tasks through workflow loops, improve through iterative execution, are evaluated by mentor agents or humans in the loop, and turn completed work into reusable work experience and data to improve future agents.
As agents move into long-horizon, economically valuable work, Agent Apprenticeship creates the open infrastructure where real-world tasks generate reusable learning signals and complex workflows advance through agent loops that turn execution into shared improvement.
Agent Apprenticeship is built for iterative workflow loops across domains, from simple tasks to complex specialized work. Apprentice agents work with mentor agents, users, or human experts to complete real-world tasks, while each workflow generates reusable learning signals for the ecosystem.
The latest seed dataset includes:
- 500+ curated seed tasks sourced and grounded from the real world
- 495 reusable agent lessons
- 1000+ full agent execution traces
- 1000+ agent work episodes / task rollouts
- 505 full agent work experience compilations
- 39k+ structured experience compilation records
The seed dataset spans specialized, economically valuable tasks across domains and forms the first layer of the Agent Apprenticeship ecosystem.
Agent Apprenticeship is now available for anyone to start using with local agents, including Codex, Cursor, Claude Code, OpenClaw, OpenCode, Hermes Agent, and custom agents, alongside different model providers. Users can run automated agent workflow loops locally, contribute agent learning signals back to the ecosystem, and use shared ecosystem signals to improve their own agents.