ai-agent

Design safe AI agents by defining clear operational requirements and decision boundaries before deployment. Specify pipeline stages, LLM and tool contracts, state management, and verification scenarios to ensure predictable behavior when combining LLM inference with external tools.

Define pipeline stages by documenting ordered steps from input and intent through retrieval, planning, tool execution, validation, response, and logging. Specify entry and exit contracts, error handling, and LLM call schemas for each stage to ensure verifiable agent behavior.

Set up observability by defining session boundaries, persistence rules, traceable identifiers, and metrics for model and tool calls. Create verification scenarios covering success, failure, approval gates, and rollback rules to audit agent behavior.

Implement approval gates and rollback rules when an AI agent performs risky actions like calling external services. Verification scenarios must cover these boundaries to provide auditable traces for compliance and safe production deployment.

Yes, you can review existing agents by evaluating their operational requirements, decision boundaries, and tool contracts. The specification process applies to designing or reviewing agents that orchestrate LLM inference, MCP servers, and external tools across runtime pipelines.

Defining operational problems first forces problem-first design by requiring clear target users, success criteria, constraints, and non-goals. This rigorous agent specification prevents unpredictable behavior by ensuring architecture follows verifiable requirements.