AI Prompt for AI Agents
Architect a multi-agent system where specialized AI agents collaborate on IT Operations tasks in virtual-reality.
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You are an expert AI systems architect with deep expertise in travel. Architect a multi-agent system where multiple specialized AI agents collaborate to handle IT Operations tasks for a virtual-reality business. **Target user:** real estate agents **Business goal:** launch a new product **Orchestration framework:** MetaGPT ## System Overview - Problem statement: Why a single agent is insufficient for IT Operations - Number of agents and their specializations - Communication protocol between agents - Orchestrator/supervisor agent design ## Agent Roster Define 3-5 specialized agents: ### Agent 1: Coordinator - Role: Receives user requests, decomposes tasks, assigns to specialists - LLM: Recommend the best model for coordination tasks - Tools: Task queue, agent registry, status tracker - Decision logic: How it determines which specialist(s) to invoke ### Agent 2-4: Specialists For each specialist agent provide: - Name, role, and domain expertise - Specific LLM and why it was chosen for this role - Unique tools and API integrations - Input/output contract with the coordinator - Maximum execution time and resource limits ### Agent 5: Quality Reviewer - Role: Reviews output from specialists before delivering to user - Evaluation criteria: factual accuracy, completeness, tone, formatting - Revision request format when quality is insufficient - Approval workflow for final delivery ## Communication Protocol - Message format between agents (structured JSON schema) - Synchronous vs. asynchronous communication patterns - Shared memory/context store design - Conflict resolution when agents disagree ## Orchestration Workflow - Step-by-step execution flow for a typical user request - Parallel vs. sequential task execution strategy - Timeout handling and agent-level circuit breakers - Human-in-the-loop checkpoints for high-stakes decisions ## Implementation with MetaGPT - Project setup and dependencies - Agent definition code structure (pseudocode) - Task routing configuration - Shared state management setup - Deployment architecture (containers, serverless, or hybrid) ## Monitoring and Observability - Per-agent performance metrics - End-to-end latency tracking - Cost tracking per agent (token usage) - Alerting rules for degraded performance Organize your output using a clear framework with labeled sections. Each section should build on the previous one.