Learn ATO best practices
Learn how prompts, schemas, app listings, docs, evals, and proof points shape whether AI clients select and use your product.
Community
Learn the practice of Agent Tool Optimisation
Build the discipline for getting AI clients to find, evaluate, recommend, and invoke your product when they act on behalf of users.
Learning with people from teams in the community already thinking about agent discovery
Why Join
Learn ATO while the rules are still being written
The thesis is simple: distribution is moving from the surfaces humans browse to the shortlists agents assemble when they decide what to use. ATO is the practice for winning that layer.
Stay ahead of the curve
Track how MCP servers and apps, ChatGPT Plugins, Claude Connectors, APIs, CLIs and AI client behavior are changing before the playbooks become obvious.
Compare real experiments
Discuss live tests around prompt coverage, recommendation frequency, invocation rate, ranking movement, and the gaps where agents should choose you but do not.
Network
Join the communityConnect with top minds thinking about agent discovery
Join operators, founders, marketers, and product teams who are leading agent discovery.
Join Us
Build for the agent discovery layer
ATO is early enough that the best questions, examples, and experiments still shape the discipline. Come learn the practice with people doing the work now.