LLMOps practices for agent systems: versioning prompts and workflows, deployment pipelines, monitoring, and safe rollbacks.

LLMOps

Overview

LLMOps extends MLOps concepts to LLM-driven systems: prompts, retrieval pipelines, tool contracts, and evaluation harnesses must be versioned, deployed safely, and monitored for drift.

Key topics

  • Versioning and change control for prompts, tools, and workflows.
  • CI/CD with automated evals and regression gates.
  • Runtime monitoring for quality, cost, and reliability.
  • Rollback strategies for prompt/model/tool changes.
  • Dataset management for golden sets and red-team scenarios.

Common pitfalls

  • Manual prompt edits with no audit trail or evaluation.
  • Deploying model changes without regression testing.
  • Monitoring only uptime, not output quality.
  • No rollback plan when behavior changes unexpectedly.

Recommended practices

  • Treat prompts and workflows as code with reviews and tests.
  • Run evals automatically and alert on regressions.
  • Track cost and latency alongside quality metrics.
  • Deploy gradually and maintain fast rollback capability.

This page is intended to be actionable for engineering teams. For platform-specific details, cross-reference /platform/agents, /platform/orchestration, and /platform/knowledge.