The original title is "Lifecycle, DevOps & Multi-Agent Orchestration for Enterprise AI"

Short summary

Enterprise AI is moving from simple chat assistants to complex multi-agent meshes, creating severe platform engineering challenges around non-deterministic behavior, prompt regressions, and recursive delegation loops. The article proposes a Lifecycle & DevOps framework using declarative agent manifests stored as signed OCI artifacts, automated Ahead-of-Time evaluation gates with golden benchmarks, and progressive canary deployments with automated rollback. It emphasizes treating prompts as code, enforcing strict inter-agent communication protocols, and maintaining continuous observability through OpenTelemetry to operate multi-agent systems reliably in production.

• •Declarative agent manifests in Git with version-controlled prompts, tools, and model configs packaged as OCI artifacts
• •AOT evaluation gates using Ragas/DeepEval to block PRs when golden benchmark scores drop below baseline
• •Progressive canary deployments via Argo Rollouts with automated rollback on error-rate thresholds

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