evaluation

An agentic harness is the wrapper around an LLM that turns it into an agent by running it in a loop, giving it tools, and managing state. Working Definition Agentic harness = controller loop + tool runtime + context/state management + guardrails + (optional) evaluation. In practice, the harness is the part that: Calls the model repeatedly (plan → act → observe → update → repeat) Executes tools (shell, files, web, APIs, repo ops) on the model’s behalf Manages context (what to include, summarize, persist, retrieve) Enforces constraints (step limits, timeouts, budgets, sandboxing, policy checks) Optionally adds evaluation (tests, graders, benchmarks, self-checks) Two Common Meanings Runtime harness - used to build/operate real agents (coding agents, research agents, ops agents). Evaluation harness - used to run task suites and measure performance across models/variants. Why It Matters Most “agent capability” comes from the harness: ...

Competitive Agentic Forking is a software development workflow pattern where specific tasks are “forked” to multiple independent AI agents or models. Instead of relying on a single agent, the system spawns parallel competitors that attempt the same task. Their outputs are then evaluated, compared, and either selected or merged. This approach brings the competitive evaluation model popularized by lmarena.ai/leaderboard directly into development workflows—not as external benchmarking, but as native process integration. ...

Encode editorial judgment as an explicit scoring rubric so prioritization is consistent, inspectable, and tunable.

Use AI personas to simulate user behavior and predict outcomes before launching changes, reducing risk and speeding iteration.