Building Production Agents
AGAI 401
Move from prototype to production. Learn the engineering practices required to build AI agents that are reliable, observable, cost-effective, and maintainable at scale — including evaluation, tracing, error handling, and CI/CD for AI systems.
From Demo to Production
Building a working demo of an AI agent is relatively easy. Building one that works reliably in production — handling edge cases, managing costs, providing observability, recovering from failures, and improving over time — is a different challenge entirely.
The Production Engineering Stack
This course covers the full stack of practices required for production AI agents: evaluation frameworks, prompt versioning, tracing and observability, cost management, error handling, testing strategies, and deployment patterns. These are the practices that separate prototype-quality AI from production-quality AI.
What You Will Learn
You will build evaluation frameworks using real tools like LangSmith, Braintrust, and Langfuse; instrument agent workflows with traces and spans; implement prompt versioning with review and rollback workflows; design fallback and graceful degradation strategies; optimize for cost and latency; and set up CI/CD pipelines and production monitoring for AI systems. Every lesson includes working code examples and references to real production tooling.
Who This Course Is For
This course is for engineers who have built working AI agent prototypes and are ready to make them production-worthy. If you have shipped traditional software and understand CI/CD, testing, and observability — but are new to AI-specific engineering challenges — this course translates that experience into the AI domain. Strong software engineering fundamentals are assumed.
What you will learn
- Build an evaluation framework for an AI agent
- Implement tracing and observability for agent systems
- Apply prompt versioning practices in a production codebase
- Design error handling and fallback strategies
- Optimize agent pipelines for cost and latency
- Set up monitoring and alerting for AI systems
Major topics
Why this course matters
The gap between a demo and a production AI system is enormous. The practices in this course are what make AI reliable enough to trust with important tasks — and what make it possible to improve AI systems systematically over time.
Course modules
Evaluation for Production Agents
Production AI systems require evaluation strategies that go beyond traditional unit tests. This module teaches how to build eval datasets, judge model behavior, compare agent trajectories, and use modern evaluation frameworks to keep agent quality measurable over time.
Observability and Reliability
Production agents need traces, logs, prompt versions, fallback paths, and graceful failure behavior. This module teaches how to make agent systems inspectable, debuggable, and resilient when models, tools, or retrieval systems fail.
Deployment, Operations, and Optimization
Move agent systems into production with cost controls, latency budgets, CI/CD, monitoring, alerting, and incident response. This module focuses on the operational practices required to keep AI systems reliable and maintainable after launch.
Common misconceptions
You can test AI agents the same way you test traditional software
Evaluation is a one-time step before deployment
Cost optimization requires sacrificing quality
Tracing is only useful for debugging, not monitoring
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