agent-harness-construction

Improve AI agent planning and tool invocation by applying structured design primitives with explicit action schemas, deterministic tool interfaces, and clear observation formats to reduce ambiguity and increase completion rates.

Structure observation schemas by enforcing consistent status, summary, next_actions, and artifacts fields in every tool response, which maintains progress and provides structured recovery paths during complex agent workflows.

Error recovery and context budgeting provide structured recovery paths and context budget guidelines to maintain progress, ensuring agents can handle errors and convergence scenarios without exceeding operational limits.

Choose ReAct, function-calling, or hybrid planning strategies based on whether your tasks require deterministic execution or exploratory actions, using architecture guidance to optimize agent action spaces and tool selection.

Design explicit action spaces by creating stable tool names and narrowing inputs, which directly reduces ambiguity in tool selection and improves task completion rates across complex agent workflows.