n-agentic-harnesses-anthropic

To design an Anthropic-compatible agent harness, you structure LLM tool orchestration with explicit system boundaries, permission policies, and durable workflow state so agents behave predictably. This approach converts architecture requests into actionable design plans with clear guardrails.

Evaluation-first development for agent workflows ensures every build includes success criteria, regression checks, and failure tests rather than relying on prompt tweaks. It requires evidence-based inspection and a concrete verification plan to measure agent behavior over time.

To make agent sessions resumable with durable context memory, you design harness architectures that maintain workflow state explicitly. This allows paused orchestration processes to resume without losing tool execution history or approval gate status.

To diagnose unexpected actions or runaway cost in an agent harness, you pressure-test context and memory behavior using evaluation plans with failure tests. This identifies harness gaps and enforces lean boundaries to prevent uncontrolled tool execution.

You do not need multi-agent coordination to build a reliable LLM workflow orchestrator. The approach emphasizes explicit system boundaries, permission policy, and workflow state over complexity, defaulting to lean guardrails and single-agent tool registries.