Practices for embedding AI agents into enterprise systems [Agent Registry / Internal Marketplace] 💡 Are similar agents proliferating across your organization with no coordination? This pattern catalogs all enterprise agents, tools, and skills to enable discovery, reuse, and centralized governance. 🔥 Problems it solves - Teams independently build similar agents, fragmenting resources and effort - Agents without proper eval or testing make it into production - Agents become unmaintained after their developer transfers to another team - No visibility into which agents hold what permissions and access what data 🏗️ Proposed pattern Attach metadata to each agent -- owner, target department, required permissions, eval results, cost history, SLA, and version -- and register them in a ServiceNow or Backstage-style service catalog. Enforce governance through request-and-approval workflows, and require eval gate passage before any new agent goes live. Track change history via versioning, and manage retirement through a staged lifecycle: deprecation, dependent notification, then shutdown. ✅ Selection criteria - Good fit: Organizations where agents are proliferating and duplicate development or orphaned agents are becoming a problem - Not a fit: Early stages with only a handful of agents (registry overhead is not justified) ⚠️ Pitfalls - Making registry enrollment optional leads to an empty catalog; embed it in the CI/CD pipeline to enforce compliance - Neglecting metadata freshness erodes trust in the catalog - Deleting agents without a deprecation process will break downstream agents and workflows without warning 🛠️ Implementation Approach 1. Leverage a service catalog platform (ServiceNow / Backstage) to centrally register metadata for every agent -- owner, target department, permissions, eval results, cost, and SLA 2. Embed registry enrollment into the CI/CD pipeline so that unregistered agents are automatically blocked from deployment 3. Require eval gate passage before any new agent goes live, preventing agents that fail quality standards from reaching production 4. Introduce versioning and change-history tracking so that every modification is auditable -- who changed what and when 5. Automate the retirement lifecycle (deprecation flag, dependent notification, grace period, shutdown) to prevent cascading failures from abrupt removal #AIAgents# #EnterpriseArchitecture#