n-agentic-harnesses

To design permission tiers and approval gates, you specify explicit tool boundaries that prevent unsafe actions by requiring operator approval before risky writes. This creates deterministic safety boundaries for orchestration, execution, and workflow state in LLM systems.

An agentic harness defines tool boundaries, permission policy, approval gates, and context memory assembly for LLM systems. You need one when your AI assistant requires deterministic tool boundaries, operator visibility, and safety controls to prevent unreliable tool use.

Evaluating agent workflow state and context memory requires performing a findings-first gaps assessment against your target architecture. This process checks resumability, idempotency, durability, and regression-focused evaluation plans to ensure reliable context assembly and safe tool use.

Yes, you can add observability and tool permissions to an existing workflow runtime by running an evaluation mode assessment. This produces a findings-first audit that identifies gaps in operator visibility, permission tiers, and workflow state durability for your current setup.

The best way to plan phased implementation for a solo-dev coding agent is to generate a buildable output contract. This contract defines MVP scope, acceptance criteria, and evaluation checks, ensuring the agent can read files and propose edits while requesting approval for risky writes.