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AgentOps Open Course

Learn from one completed Ops Copilot, from its first local model call to an observable Kubernetes workload. Every chapter inspects and runs the same reference, so concepts stay connected to code, tests, policy, and operations. The capstone then guides you through replacing the fictional incident domain with your own agent platform.

Start without an account

The required path uses Ollama and the Apache-2.0 open-weight Qwen3 model from the first interactive run. It needs no account, no mandatory SaaS, and no usage fee. Native Gemini and the final GKE/Vertex lab are optional proprietary comparisons.

What will you be able to do?

  • Design an agent only where model autonomy is worth its latency and risk.
  • Build typed ADK agents with tools, Agent Skills, MCP, memory, workflows, and A2A.
  • Test behavior offline, evaluate model-backed trajectories, redact PII, and require human approval for writes.
  • Route model, tool, and agent traffic through agentgateway with one stable application contract.
  • Run the same container on local k3d or a small GKE lab managed by kagent.
  • Trace the system in self-hosted MLflow and monitor it with OpenTelemetry, Prometheus, and Grafana.

What is the system you will inspect and extend?

flowchart TD
    User[Engineer] --> Gateway[agentgateway]
    Agent[Ops Copilot] --> Gateway
    Gateway --> MCP[MCP tools]
    Gateway --> Model[Qwen3 locally<br/>Gemini optionally]
    Gateway --> Agent
    Agent --> State[(SQLite state)]
    Agent --> OTel[OpenTelemetry]
    OTel --> MLflow[MLflow traces]
    OTel --> Metrics[Prometheus + Grafana]

The bundled incident, log, runbook, and skill data is immutable. A runtime copy receives mock state changes and append-only audit records, which keeps each exercise resettable and safe. You do not reconstruct this system file by file: main is the working reference, and each checkpoint asks you to understand, verify, or deliberately change one boundary.

Where should you start?

New to agent systems? Read the chapters in order. Already shipping LLM applications? Use the outcomes below as a map:

Chapter You will leave with
0. Overview A clear AgentOps lifecycle, architecture, and provider choice.
1. Setup A pinned local workspace and an offline verification checkpoint.
2. Agents A first ADK agent with explicit configuration and session semantics.
3. Capabilities Typed tools, least-privilege skills, MCP, retrieval, workflows, and A2A.
4. Quality Branch-covered tests, evaluations, guardrails, and security regressions.
5. Gateway Governed MCP, A2A, and model traffic through agentgateway.
6. Platform Reproducible k3d and optional GKE deployments with kagent.
7. Observability Self-hosted tracing, metrics, evaluation, feedback, and audit evidence.
8. Community A maintainable project and an evidence-backed capstone of your own.

What does "open source" mean here?

Google ADK, agentgateway, kagent, MLflow, OpenTelemetry, Prometheus, Grafana, Ollama, the Apache-2.0 open-weight Qwen3 model, and the course code form the required open-source path. The optional Gemini API, Vertex AI, GKE, and repository/site hosting are proprietary services. They are integrations, not hidden requirements.

How do you begin?

Start with 0.0. Course, or use README.md#local-quickstart from the repository root. Every chapter ends with a checkpoint; Chapters 5-7 also include explicit verification and teardown steps. Finish by adapting the reference through 8.7. Capstone.

The source repository is public at MLOps-Courses/agentops-open-course. To preview documentation changes locally, run mise run serve at http://127.0.0.1:8000.