Phase 4: Agent Orchestration in Client Environments · 55 min · Python · Docker · Kubernetes
Agent Deployment on Client Infrastructure
An agent in a notebook is a demo. An agent on Kubernetes is a product.
What you will learn
- Containerize agents with Docker: images, dependencies, environment configuration
- Deploy on Kubernetes: pods, deployments, services, configmaps, secrets
- Configure Agent Server runtime: API servers for requests, queue workers for execution
- Set up observability: LangSmith tracing, step-level logging, trajectory capture
- Enforce step budgets in production: preventing runaway agents, cost limits, timeout enforcement
Objective
What you'll learn
This lesson takes approximately 55 min. By the end, you will be able to:
- Containerize agents with Docker: images, dependencies, environment configuration
- Deploy on Kubernetes: pods, deployments, services, configmaps, secrets
- Configure Agent Server runtime: API servers for requests, queue workers for execution
- Set up observability: LangSmith tracing, step-level logging, trajectory capture
- Enforce step budgets in production: preventing runaway agents, cost limits, timeout enforcement
The Problem
An agent in a Jupyter notebook is a demo. An agent on Kubernetes with autoscaling, health checks, and rolling updates is a product. Deploying agents on client infrastructure means containerizing the service, configuring resource limits, setting up monitoring, and ensuring the agent survives node failures.
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