agent-harness-construction

To optimize AI agent planning, you must design explicit action spaces, structure tool outputs, and implement robust recovery contracts to improve multi-step workflow reliability and speed.

Context budgeting in multi-step AI agent workflows is the architecture guidance that manages observation formatting and deterministic flows to prevent context overflow during complex tool use.

Designing deterministic tool interfaces requires defining structured tool outputs and explicit action spaces to ensure reliable function-calling and observation processing for probabilistic agents.

AI agents fail during error recovery when they lack structured observation formatting and robust recovery contracts, which are required to guide deterministic flows and handle tool use failures.

Yes, you can improve function-calling reliability in production agents by applying explicit action space design, structured tool outputs, and context budgeting to manage multi-step workflows.

The best way to format observations for multi-step agent tasks is to apply structured tool outputs and recovery contracts, ensuring the agent processes deterministic data within its context budget.