agent-harness-construction

To design an agent action space, implement stable, explicit tool names and restrict inputs to narrow parameters. This structured approach ensures predictable, safe, and scalable AI agent interactions.

Formatting observations for error recovery requires tool responses to include status, summary, next_actions, and artifacts. This robust observation design supports deterministic recovery contracts during iterative agent development.

Build an initial agent harness plan by outlining the actions your agent will perform, the tools it will call, and how it will recover from common errors. Establish architecture patterns like ReAct or Function-calling.

Context budgeting improves agent performance benchmarking by constraining context windows to ensure scalable, predictable outcomes. It supports production-grade workflows by preventing context overflow during complex tool calling.

Apply ReAct versus Function-calling architecture patterns based on your specific reasoning and tool integration needs. Hybrid patterns can also be established to balance explicit tool definitions with dynamic error recovery contracts.