How do you build a multi-agent prompting architecture?

All answers to this question.

Building multi-agent systems requires shifting your perspective from single-turn prompts to designing explicit finite state machines. Instead of forcing a single model to act as an expert across every domain, break your problem down into hyper-focused micro-agents, each possessing a single, dedicated system instruction. Prompt your coordinator agent to act as an orchestrator that analyzes user inputs and routes the state payload to the appropriate specialist. Each specialist agent must be prompted to return a standardized output format containing an explicit completion flag or a next-step routing signal, allowing your core application loop to manage data transitions safely.

   Answered 2025-10-10 by Martha Bryant

Should we use centralized JSON logs to pass state history between agents, or allow them to communicate directly?

   Answered 2025-10-12 by Albert Simmons

• Albert, passing a centralized, sanitized JSON state graph is far more reliable. Allowing free-form chat communication between agents frequently leads to infinite loops, runaway API costs, and context degradation over long execution steps.

     Commented 2025-10-14 by Bobby Patterson

Defining clear, constrained tool definitions within your agent prompts prevents models from executing unauthorized system actions.

   Answered 2025-10-17 by Julia Coleman

• Excellent point, Julia. Restricting tool access via crisp system boundaries is an absolute prerequisite for deploying these autonomous workflows securely.

     Commented 2025-10-18 by Martha Bryant