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.
Hiring signal: System design interviews test agent deployment knowledge: candidates who describe Agent Server runtime (API servers for requests, queue workers for execution, independent scaling) pass. Candidates who deploy agents as simple Flask apps fail. Step budgets in production (preventing runaway agents, cost limits, timeout enforcement) are the operational patterns that demonstrate production experience.
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
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.
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers Containerizing Agents with Docker, Deploying on Kubernetes, Agent Server Runtime: API + Queue Architecture, Observability: LangSmith Tracing, Enforcing Step Budgets in Production, Practical Application, What Hiring Managers Look For, Resources, Key Takeaways, Next Steps — plus a hands-on lab, quiz, and project artifact.
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