harness-engineering

Harness engineering codifies the full environment around autonomous AI agents—scaffolding, constraints, and evaluation pipelines—to make agent behavior reliable, observable, and aligned in production. It establishes a reproducible environment through context management and architectural guardrails.

To set up context management, create a CLAUDE.md or AGENTS.md map at your project root to establish a single source of truth. Add design-docs and exec-plans directories to guide development and structure the agent's operational context.

Build an evaluation harness by implementing a five-stage pipeline: definition, interface, inference, scoring, and reporting. Wire this pipeline into your CI/CD system to monitor agent behavior continuously using CLASS metrics and automated drift detection.

Yes, you can enforce architectural constraints by applying layered dependency rules, entropy management, and automated drift detection. These mechanisms prevent architectural drift and maintain system integrity across research and production environments.

Observability for AI agents supports tracing, cost attribution, and latency measurement. It provides governance for safe, auditable agent workflows, ensuring you can monitor performance and operational metrics across deployed agentic pipelines.

The best way to evaluate autonomous agent behavior is implementing a structured evaluation harness with explicit metrics. This enforces measurable evaluation through structured reporting, ensuring aligned and observable workflows in production environments.