Hi folks,<p>I built this guide after watching AI agent prototypes repeatedly fail in production. It demonstrates transforming a monolithic marketplace assistant into a resilient multi-agent system using orra, an open-source platform I also built for production-ready multi-agent applications.<p>The patterns shown are valuable *<i>even if you're building your own orchestration layer*</i>. Each stage builds on the previous one, showing the evolution from fragile prototype to resilient system.<p>What makes this guide valuable:<p>* Architectural transformation with working code examples - split monolithic agents into specialised components and migrate from inefficient LLM function calls to dedicated services<p>* Solves real production challenges most frameworks ignore - implements compensation handlers for critical operations and proper state management when operations fail mid-transaction (like payment failures leaving inventory in inconsistent states)<p>* Prevents LLM hallucinations at the planning level - uses domain grounding with semantic verification and PDDL validation to formally verify execution plans<p>Here, orra's Plan Engine operates at the application level rather than just the agent level, enabling orchestration across both LLM agents and deterministic services.<p>Would love feedback from anyone who's hit these issues in production!
Finding this helpful in preventing looping and having a more control over my systems. Going from single-agent to a multi-agent I find, expoentially increases the amount of troubleshooting but has better benefits for more complicated tasks (ie. deep research)