agent-engineering

Prevent race conditions and stalls in multi-agent coordination by applying claim locks and explicit lifecycle state management. This ensures agents safely claim, execute, verify, and release tasks without overwriting each other's work.

Design multi-agent systems for production reliability by implementing durable handoffs, idempotency, cost budgets, and failure recovery. This prevents demo-only failures and ensures orchestrated workflows survive crashes across session boundaries.

Use orchestrator/worker patterns for delegation and fan-out/merge for parallel processing. Evaluating overhead crossover guidance helps determine when coordination overhead is worth it versus keeping tasks inline.

Add observability and safety caps to orchestrated AI workflows by integrating audit gates and cost budgets into the lifecycle. This monitors execution, detects context contamination, and prevents runaway spending.

Yes, you can audit an existing multi-agent workflow to detect failure modes like claim races and context contamination. The process evaluates your current setup and recommends correct coordination patterns and reliability controls.