Multi-Agent System

Problem

Single-agent systems break down for complex tasks that require specialist knowledge across multiple domains. One agent cannot be expert at research, coding, analysis, and communication simultaneously, leading to shallow results on multi-step workflows.

Solution

Design a system of dedicated specialist agents with defined roles, shared context via a memory layer, orchestration contracts for handoffs, and deterministic arbitration when agents produce conflicting recommendations.

Implementation Steps

1. Design agent role map

   Define each specialist agent's responsibility, input/output contracts, and interaction boundaries. Keep roles narrow and non-overlapping.

   Tip: Start with 2-3 agents maximum. Add specialists only when you can measure the quality gap a new role would fill.

2. Build shared memory layer

   Create a structured context store (Redis + Postgres) that all agents read from and write to. Define the schema for handoff context between agents.

   Tip: Define a measurable success metric and review weekly to improve quality and cost.

   # Shared memory for multi-agent handoffs
   class AgentMemory:
       def __init__(self, session_id: str):
           self.redis = Redis()  # Fast session state
           self.pg = Postgres()  # Durable context log
       
       async def handoff(self, from_agent: str, to_agent: str, context: dict):
           await self.redis.set(f'{self.session_id}:current', to_agent)
           await self.pg.insert('handoffs', {**context, 'from': from_agent, 'to': to_agent})

3. Define orchestration contracts

   Specify when and how agents hand off work. Use structured output schemas to ensure receiving agents get the context they need.

4. Implement arbitration logic

   When agents disagree, apply deterministic rules: prefer the specialist for their domain, use confidence scores to break ties, or escalate to a human decision-maker.

5. Instrument cost and quality metrics

   Measure token usage, latency, and output quality per agent role. Identify which agents add value and which can be consolidated.

Recommended combos

FAQs