> For the complete documentation index, see [llms.txt](https://docs.darwinslab.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.darwinslab.ai/agent-architecture-and-evaluator-dynamics/branching-and-fork-control.md). # Branching & Fork Control Each Evolution Path (EP) undergoes clonal propagation across a fleet of parallel execution threads: the parent state vector is replicated into identical child trajectories, each submitted to the ideator ensemble for concurrent evaluation. A continuous divergence-detection module monitors agent-issued mutation kernels and architectural proposals; when the statistical variance between two child trajectories exceeds a predefined divergence threshold, the system automatically forks the branch—e.g., EPₐ bifurcates into EPₐ₁ and EPₐ₂—thereby growing a hierarchical tree of sub-branches. Each fork inherits a MaxTry budget parameter, defining the upper bound on exploratory iterations or agent interactions permitted for that branch. Should a branch exhaust its MaxTry budget without surpassing a minimal fitness-improvement criterion, it is pruned to reclaim compute resources. This budget-gated forking strategy ensures both the preservation of promising evolutionary channels and the culling of unproductive search trajectories, maintaining high algorithmic efficiency and architectural diversity. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.darwinslab.ai/agent-architecture-and-evaluator-dynamics/branching-and-fork-control.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.