Multi-Agent Architect

Design a multi-agent system by defining agent roles, selecting a coordination pattern (sequential, hierarchical, peer, or swarm), and implementing communication via CrewAI or LangGraph. Each agent handles a specialized capability, and the orchestrator manages task flow, parallelization, and consensus-based decisions across 2–5 agents to solve problems single agents cannot.

Both CrewAI and LangGraph orchestrate multi-agent systems, but CrewAI emphasizes role-based agent teams with built-in collaboration patterns, while LangGraph provides lower-level graph-based workflow control. Choose CrewAI for rapid team design with predefined patterns; use LangGraph for fine-grained state management and dynamic routing logic.

Yes, multi-agent architectures support self-correction through peer review by assigning review or validation roles to dedicated agents. Use hierarchical or peer collaboration patterns where agents critique each other's outputs, propose refinements, and reach consensus before finalizing results.

Sequential, hierarchical, and swarm patterns enable parallel processing. Sequential chains tasks linearly; hierarchical routes through a supervisor; swarm distributes independent tasks across homogeneous agents. Choose based on task dependencies—swarm maximizes parallelism when tasks are independent.

Add observability by logging agent actions, decisions, and message exchanges through shared memory or event systems. Track state transitions, coordination decisions, and individual agent outputs to debug agent behavior, validate reasoning chains, and audit the system's problem-solving process.

Multi-agent systems support both fixed workflows and dynamic routing. Fixed workflows execute predefined agent sequences for consistent, predictable tasks. Dynamic routing allows the orchestrator or agents to choose next steps based on intermediate results, suited for adaptive problem-solving.