Guide to transform fragile AI agents into production-ready systems
Hi folks, 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. The patterns shown are valuable *even if you're building your own orchestration layer*. Each stage builds on the previous one, showing the evolution from fragile prototype to resilient system. What makes this guide valuable: * Architectural transformation with working code examples - split monolithic…
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In the maker’s words, at launch
Hi folks, 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. The patterns shown are valuable *even if you're building your own orchestration layer*. Each stage builds on the previous one, showing the evolution from fragile prototype to resilient system. What makes this guide valuable: * Architectural transformation with working code examples - split monolithic agents into specialised components and migrate from inefficient LLM function calls to dedicated services * 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) * Prevents LLM hallucinations at the planning level - uses domain grounding with semantic verification and PDDL validation to formally verify execution plans 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. Would love feedback from anyone who's hit these issues in production!
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