agent-harness-construction

Optimize AI agent action spaces by using stable tool names, narrow input schemas, and deterministic output shapes. This structured design enhances agent output quality and ensures reliable execution.

Format observations for AI agent error recovery by including status, summary, next_actions, and artifacts in every tool response. This structure provides root cause hints and explicit stop conditions for robust handling.

Context budgeting improves AI agent performance by optimizing system prompts to maintain a compact and efficient context. This prevents context overloading and ensures efficient processing.

Choose ReAct for exploratory tasks, function-calling for structured deterministic flows, and hybrid workflows for mixed requirements. Architecture pattern selection depends on the specific task's need for reasoning versus determinism.

Common AI agent anti-patterns to avoid include overlapping tools, opaque outputs, and context overloading. Designing narrow input schemas and clear observation formats prevents these architectural pitfalls.

Track completion rate, retries, and cost per task to benchmark AI agent performance. Monitoring these metrics evaluates the effectiveness of action space design and error recovery mechanisms.