agent-harness-construction

Improve AI agent reliability by restructuring tool action spaces and enforcing consistent observation and recovery contracts. This requires explicit tool naming, schema-first narrow inputs, deterministic output shapes, and structured tool responses with status, summary, next_actions, and artifacts.

Design tool observations for function calling agents by enforcing a structured response contract. Each tool response must include status, summary, next_actions, and artifacts, alongside explicit error recovery instructions and safe retry plus stop conditions to reduce ambiguity.

Manage context budgeting for LLM agents by keeping the system prompt minimal and moving large guidance into on-demand skills. Select ReAct, function-calling, or a hybrid approach based on task structure to optimize planning and execution loops.

AI agents fail to recover from tool calling errors when lacking explicit recovery contracts. Define safe retry behaviors, explicit stop conditions, and deterministic output shapes to ensure consistent error handling across deployment, migration, and permissions operations.

Choose ReAct versus function-calling based on task structure complexity. Use this approach to redesign your agent's tool list and observation schema, selecting a hybrid architecture when balancing planning, execution, and edit, read, or search loops.

Agent-harness-construction applies to tool-using LLM agents in error-prone workflows across deployment, migration, and permissions operations. It redesigns tool action spaces and observation contracts to achieve a higher completion rate on repeated task runs.