Handling Race Conditions in Multi-Agent Orchestration

Race conditions in multi-agent LLM orchestration systems are a predictable consequence of parallel execution, not edge cases. When multiple agents read and write shared state concurrently, silent data corruption can occur without any errors. Key mitigation strategies include optimistic and pessimistic locking, task queuing via Redis Streams or RabbitMQ, event-driven architectures to reduce shared-state overlap, and idempotent operations with unique operation IDs to handle retries safely. Testing approaches like stress testing with concurrent agents and property-based testing help surface timing-dependent bugs before production. A concrete shared-counter example illustrates the problem and three solutions: locking critical sections, atomic operations, and versioned writes.