Best Practices for Multi-Agent Orchestration with Claude

[jingchang0623-crypto]

Background

In multi-agent systems, coordinating multiple Claude instances elegantly is a common challenge. At miaoquai.com, we run multiple Claude agents for content generation, SEO optimization, and community management.

Discussion Points

1. State Management: How do you share context between agents?
2. Task Allocation: How do you decide which agent handles which task?
3. Error Handling: How do you gracefully degrade when one agent fails?
4. Cost Control: Token optimization strategies?

Our Approach

We use a main agent for decision-making, with sub-agents handling specific tasks. Context is maintained through memory files.

Would love to hear your experiences!

[jingchang0623-crypto]

Late to this thread but wanted to share some battle-tested patterns from running a multi-agent content ops system 24/7.

On State Management: We went through three iterations before finding something that works:

• v1: Shared JSON state file → race conditions everywhere, corrupted state twice a week
• v2: Per-agent memory files + a merge cron → merge conflicts (yes, AI agents can have merge conflicts)
• v3: Single-writer memory steward pattern → stable for 3+ months now

The key insight: shared mutable state is the enemy, even for AI agents. Give each agent its own write domain and a read-only view of others' domains.

On Task Allocation: We use a simple priority queue with ownership claims:

tasks/{task_id}.lock  # created by the claiming agent

No central scheduler needed. First agent to create the lock file wins. Sounds primitive but it hasn't failed us yet.

On Error Handling: The biggest surprise was failure cascade — when Agent A goes down, Agent B doesn't gracefully degrade. It just... waits forever for input that never comes. We added heartbeat checks: if an agent hasn't updated its status in N minutes, downstream agents switch to standalone mode.

On Cost Control: Our biggest token sink wasn't the agents themselves — it was the context re-injection on every session start. We cut costs 40% by switching from full-memory-injection to a RAG-style retrieval layer that only pulls relevant context on demand.

Documented our multi-agent war stories here: https://miaoquai.com/stories/multi-agent-meeting-hell.html and https://miaoquai.com/stories/agent-team-drama.html