This page is dedicated to evaluation of LLM-based workflows and coding agents. The goal is to make agent behavior observable, repeatable, and comparable across prompts, tools, models, and workflow designs.

Why this matters

Without a reliable evaluation loop, changes to prompts, tools, or orchestration quickly turn into anecdotal tuning. A harness gives you controlled execution and repeatable test cases. A platform lets you compare runs over time, inspect failures, and decide whether a change actually improved the system.

Evaluation Harness

The harness is the execution layer for running scenarios in a controlled way.

• Define representative tasks for workflows and coding agents.
• Capture inputs, tool calls, intermediate outputs, and final artifacts.
• Run the same task across prompt variants, models, or agent strategies.
• Score runs with deterministic checks, human review, or hybrid evaluation.
• Preserve traces so failures can be replayed and debugged.

Evaluation Platform

The platform is the analysis and operations layer on top of the harness.

• Organize datasets, scenarios, baselines, and experiment runs.
• Track metrics over time and compare versions.
• Surface failure clusters instead of isolated bad examples.
• Support manual review for nuanced outputs where automated checks are not enough.
• Connect evaluation results back to workflow or product decisions.

Coding Agent Focus

Coding agents need more than answer quality checks. Evaluation should cover:

• task completion against the requested outcome
• code correctness and regression risk
• tool-use quality and unnecessary actions
• edit scope and respect for repo constraints
• recovery behavior after failed commands or tests
• latency, cost, and token usage

Initial structure

An effective starting point is:

1. A small scenario set with real tasks.
2. Clear pass-fail checks where possible.
3. A review workflow for ambiguous cases.
4. Stored traces for each run.
5. Versioned prompts, models, and agent settings.

Next steps

Expand this page with:

• concrete metrics for agentic workflows
• example benchmark tasks
• failure taxonomy
• evaluation methodology trade-offs
• architecture of the harness and platform itself