Lesson 61: Introduction to MLOps for Agentic AI

Highlights

What we build:

• A persistent AgentPipelineRegistry that tracks every agent variant through defined lifecycle stages

• A StageTransitionLog capturing timestamps, triggers, and evaluation scores at each stage gate

• A PipelineRunRecord storing inputs, outputs, latency, token cost, and drift signals per run

• A LifecycleOrchestrator that drives the L60 MarketResearchCrew through the full MLOps loop

• A React dashboard visualising pipeline health, stage distribution, and run telemetry in real time

Connection to L60: The MarketResearchCrew and CrewResultSchema from L60 become the subject under management. L61 wraps them in a lifecycle harness; every crew run is now a PipelineRunRecord with evaluation, versioning, and transition logic attached.

Enables L62: The AgentPipelineRegistry, BaseEvaluator interface, and CIReadinessReport schema exported here are the exact inputs L62’s GitHub Actions pipeline will consume to automate continuous training and deployment gates.

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Architecture Context

Place in the 90-lesson arc

Lessons 1–60 built the agent stack bottom-up: foundations, memory, RAG, multi-agent orchestration. Module 5 pivots to operations — the discipline that keeps production agents reliable, measurable, and improvable. L61 is the conceptual anchor: it establishes the vocabulary, schemas, and instrumentation layer that every subsequent Module 5 lesson extends.

Integration with L60 components

L60 Output                   L61 Role
─────────────────────────────────────────────────────
MarketResearchCrew           Subject under management
CrewResultSchema             Evaluated artifact
MASTaskRouter                Stage-transition trigger
SQLite task log              Extended to PipelineRunRecord

Module 5 objectives

Module 5 covers the full MLOps maturity ladder for agents: lifecycle framing (L61), CI/CD automation (L62), experiment tracking (L63), canary deployment (L64), drift detection (L65), and feedback-loop closure (L66–L75). L61 defines the shared data model all subsequent lessons read and write.