ax-go-playbook

You build an evolving playbook for Go LLM agents by applying context engineering workflows that refine task knowledge from live feedback or labeled examples, then rendering or persisting the playbook directly into your Ax runtime context.

Your Go agent produces inconsistent results across repeated runs because it relies on a static prompt. You can resolve this by building an evolving playbook that accumulates task knowledge from examples to produce consistent outputs.

To inject a playbook into Ax Go program context, use the generated Go package API and playbook lifecycle methods to render or persist the accumulated task knowledge directly into the runtime context.

Yes, you can validate a Go playbook using example-driven feedback. The context engineering workflow supports example-driven validation to refine task knowledge and keep the playbook aligned with changing task requirements.

The Ax Go playbook runtime does not require network API keys to execute. It explicitly supports both no-key and real-network execution profiles for flexible development and production environments.

You should use a dynamic playbook instead of a static prompt in Go when your agent needs to learn from examples, update its working context continuously, and adapt to changing task requirements over time.