agent-engineering

What problem does it solve?

It helps you design and evaluate production AI agent systems that don’t just work in demos, but also coordinate safely, reliably, and within cost and reliability budgets.

Core Features & Use Cases

• Production-ready agent architecture: lifecycle state management (claim → execute → verify → commit → release), durable handoffs, and recovery across crashes.
• Multi-agent coordination patterns: orchestrator/worker, fan-out/merge, evaluator/optimizer, consensus/fusion, sequential chain, and hybrid—plus when each is worth the overhead.
• Reliability and audit gates: observability, cost budgets, idempotency, failure recovery, safety caps, and claim locks—along with common failure-mode detection and mitigation.
• Delegation decision framework: overhead crossover guidance (including when to keep work inline vs delegate) to prevent over-delegation and silent stalls.

Quick Start

Use the agent-engineering skill to audit your current multi-agent workflow and recommend the correct coordination pattern, failure-mode mitigations, and production readiness checks.