> 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/evaluator-mechanism.md). # Evaluator Mechanism Evaluator agents are responsible for benchmarking new agents on multiple axes of performance: * **Accuracy & Coherence** – Logical validity of outputs, correctness of reasoning, and factual alignment * **Novelty** – Behavioral diversity relative to prior agents and the archive * **Resource Efficiency** – Runtime metrics including latency, memory usage, token throughput, and energy-per-token * **Task-Specific Performance** – Custom metrics like pass\@k for code, ROUGE for summaries, success rate for planning, or symbolic correctness for math problems Evaluators are not static—they evolve themselves. Each generation includes a meta-evolutionary cycle that mutates the evaluator population: * Scoring logic (e.g., benchmark weightings, composite functions) * Adversarial probes (e.g., fuzz inputs, tool misuse patterns) * Refusal thresholds and safety sanity checks * Response diversity filters and alignment gates This **co-evolutionary approach** creates an adaptive fitness landscape that changes over time, preventing agents from overfitting to fixed benchmarks or exploiting brittle reward heuristics. Evaluators themselves are subject to selection pressure: the most informative, discriminative evaluators are retained, while redundant or overly permissive ones are pruned. As a result, Darwin’s evaluation pipeline functions like a living immune system—constantly adapting its criteria to stay aligned, robust, and adversarially hardened. --- # 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/evaluator-mechanism.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.