agent-harness-construction

To design an AI agent action space for higher task completion rates, use stable, explicit tool names and keep inputs schema-first. This structured approach reduces ambiguity and ensures the agent selects the correct tools consistently during execution.

An AI agent observation format for effective error recovery must include status, summary, next_actions, and artifacts. Every error path needs root cause hints, safe retry instructions, and explicit stop conditions to prevent infinite loops.

Manage AI agent context budget by compacting context at phase boundaries and strategically allocating space for system prompts and guidance. This prevents token overflow and maintains agent focus on relevant task data.

Use the ReAct pattern for exploratory tasks requiring flexible reasoning, and the Function-calling pattern for structured, deterministic flows. Choosing the right architecture pattern ensures stable execution and optimizes agent behavior for the specific task type.

Benchmark AI agent performance by tracking completion rate, retries per task, pass@1, pass@3, and cost per successful task. These metrics reveal tool design efficacy and highlight areas for action space optimization.

Common anti-patterns in AI agent tool definitions include unstable tool names, non-schema-first inputs, and non-deterministic outputs. Error handling anti-patterns involve missing root cause hints or lacking explicit stop conditions during retries.