Multi-Agent Systems: Beyond the Single Agent
How to run specialized AI agents that collaborate — routing work channels to a Work Agent, personal messages to a Personal Agent, and a Coordinator that decides who handles what.
One agent is useful. Multiple agents working together is transformative. OpenClaw's AGENTS.md system lets you define specialized agents with different roles, models, and tools — then orchestrate them. Here's how to architect multi-agent setups that actually work in production.
3 Multi-Agent Patterns
Router Pattern
A lightweight coordinator routes incoming messages to the right specialist agent based on channel, intent, or content.
Classify intent, route to specialist. Runs on cheapest model.
Handles Slack messages, calendar, meeting prep, email drafts.
Family schedules, grocery lists, personal reminders.
GitHub PRs, code review, debugging. Needs strongest reasoning.
# AGENTS.md ## Router - model: haiku-3.5 - channels: all - behavior: Classify incoming → route to specialist - rules: work keywords → @work, code/PR → @code, else → @personal ## Work Agent - model: sonnet-4 - channels: #slack, #teams - tools: calendar, email-draft, meeting-notes - memory: work-context.md ## Personal Agent - model: sonnet-4 - channels: #telegram, #whatsapp - tools: reminders, grocery, family-cal - memory: personal-context.md ## Code Agent - model: opus-4.5 - channels: #github-notifs - tools: git, code-review, terminal - memory: project-context.md
Pipeline Pattern
Agents process work in sequence — each agent's output becomes the next agent's input.
Gather data from RSS, APIs, scrapers. Raw collection.
Process, filter, summarize, extract key points.
Create polished reports, summaries, recommendations.
Format and send to channels (Slack, email, Notion).
# Pipeline: Daily Intelligence Report ## Step 1: Collect (every 6 hours) - agent: collector - sources: RSS feeds, Reddit, HN, Twitter lists - output: raw-intel.json ## Step 2: Analyze (after collection) - agent: analyst - input: raw-intel.json - output: analyzed-intel.md (key themes, sentiment, signals) ## Step 3: Draft (after analysis) - agent: writer - input: analyzed-intel.md - output: daily-report.md (executive summary + details) ## Step 4: Distribute (8am daily) - agent: distributor - input: daily-report.md - channels: #intel-feed (Slack), email (newsletter)
Consensus Pattern
Multiple agents independently analyze the same input, then a judge synthesizes their perspectives.
Analyze from a bull/positive perspective.
Identify risks, weaknesses, red flags.
Focus on practical feasibility and timeline.
Synthesize all perspectives into balanced recommendation.
# Consensus: Investment Analysis ## Input: Company pitch deck or quarterly report ## Parallel Analysis - optimist: What's the bull case? Growth potential? Market size? - critic: What are the risks? Competition? Red flags in financials? - pragmatist: What's realistic? Timeline? Execution challenges? ## Synthesis (judge) - input: all three analyses - output: Balanced assessment with confidence level - format: recommendation + key risks + timeline estimate
Real-World Challenges
When NOT to Use Multi-Agent
Key Takeaways
Start with Router pattern
It's the simplest multi-agent setup and covers 90% of use cases. Route by channel first, then by intent.
Each agent needs its own context
Shared memory creates confusion. Separate memory files + explicit handoff messages = clarity.
Cost shouldn't increase linearly
A well-designed 3-agent system costs the same as 1 agent. The router agent is near-free on Haiku.
Audit everything
Multi-agent systems are harder to debug. Log every routing decision, every handoff, every tool call.