Building Production AI Agents
Stop prototyping toy agents — learn to design, ship, and operate ones that survive contact with real users.
About this course
This is the course for developers who already understand what an agent is and are ready to build ones that work reliably under real traffic, real errors, and real cost constraints. You'll move past single-shot demos into production architecture: choosing the right agent loop for the job, designing tool interfaces that fail gracefully, wiring up retrieval that doesn't hallucinate, managing memory across sessions and users, and building the observability stack that lets you catch regressions before your customers do. Every lesson ships with concrete, realistic code patterns — not hand-wavy diagrams. The course goes all the way to production operations: deploying and scaling agent infrastructure under bursty real-world load, and locking down the security, cost governance, and on-call practices that keep an agent fleet safe, affordable, and recoverable once it's live. This course is entirely about getting one agent rock-solid in production — if you need multiple agents coordinating with each other, that's a distinct discipline covered in Multi-Agent Systems & Orchestration.
What you'll learn
- ✓Choose and implement the right agent architecture (ReAct, Plan-and-Execute, or Reflexion) for a given task instead of defaulting to whatever's trendy
- ✓Evaluate LangChain, LangGraph, OpenAI's Assistants/Agents APIs, and hand-rolled loops against your actual constraints, and defend the choice
- ✓Design tool schemas and error-handling logic that keep an agent working when APIs time out, rate-limit, or return garbage
- ✓Build a retrieval pipeline with sound chunking, embedding, hybrid search, and re-ranking that avoids the most common RAG failure modes
- ✓Implement conversation memory, long-term memory stores, and multi-user session isolation that hold up under concurrent load
- ✓Instrument agents with tracing, evals, and cost/latency logging so you can detect and fix regressions before they reach production
- ✓Deploy and scale agent workers with the right container/serverless split, queueing, and autoscaling for bursty, concurrent production load
- ✓Lock down agent credentials and defend against basic prompt injection, and run cost governance and on-call practices built for agent-specific failure modes
Curriculum
Module 1
Agent Architectures
The core control-loop patterns behind every production agent — ReAct, Plan-and-Execute, and Reflexion — and a practical framework for deciding which one fits your task.
- The ReAct Pattern: Reasoning and Acting in Loops35 min
- Plan-and-Execute Architectures35 min
- Reflexion and Self-Critique Loops35 min
- Module 1 Quiz: Agent ArchitecturesQuiz
Module 2
The Framework Landscape
A practical, opinionated tour of LangChain, LangGraph, OpenAI-style Assistants/Agents APIs, and rolling your own — with a decision framework for picking the right one for your team and task.
- LangChain vs. LangGraph: Chains, Agents, and Graphs35 min
- OpenAI's Agents / Assistants-style APIs and Managed Alternatives30 min
- A Decision Framework: Rolling Your Own vs. a Framework30 min
- Module 2 Quiz: The Framework LandscapeQuiz
Module 3
Tool Design & Function Calling in Practice
Writing tool schemas the model can actually use correctly, handling failures without derailing the agent, and the operational concerns — idempotency, rate limits, timeouts — that separate a demo tool from a production one.
- Writing Tool Schemas the Model Can Actually Use35 min
- Handling Tool Errors and Retries Gracefully35 min
- Idempotency, Rate Limiting, and Timeouts for Side-Effecting Tools35 min
- Module 3 Quiz: Tool Design & Function Calling in PracticeQuiz
Module 4
Retrieval-Augmented Generation for Agents
Chunking, embeddings, vector databases, hybrid search, and re-ranking — the full retrieval pipeline an agent depends on, plus the specific failure modes that quietly degrade answer quality in production RAG systems.
- Chunking Strategies: The Decision That Determines Everything Downstream35 min
- Embeddings and Vector Databases: A Practical Comparison35 min
- Hybrid Search, Re-Ranking, and Common RAG Failure Modes35 min
- Module 4 Quiz: Retrieval-Augmented Generation for AgentsQuiz
Module 5
State, Memory & Persistence
How agents remember — within a conversation, across sessions, and across users — without blowing the context window, leaking one user's data into another's, or losing state when a process restarts.
- Conversation Memory Strategies: Buffer, Summary, and Sliding Window35 min
- Long-Term Memory Stores: Remembering Across Sessions30 min
- Session and Thread Persistence, and Multi-User State Isolation30 min
- Module 5 Quiz: State, Memory & PersistenceQuiz
Module 6
Testing, Debugging & Observability
How to actually know whether your agent works: tracing individual runs, writing evals that catch real regressions, tracking cost and latency in production, and building the feedback loop that lets an agent improve over time instead of silently degrading.
- Tracing Agent Runs: Seeing What Actually Happened35 min
- Writing Evals for Agent Behavior35 min
- Logging, Cost & Latency Tracking, and Catching Regressions Before They Ship30 min
- Module 6 Quiz: Testing, Debugging & ObservabilityQuiz
Module 7
Deployment Infrastructure & Scaling Production Agents
How to actually run agent workloads in production: containerized workers versus serverless functions, queueing and worker-pool patterns for concurrent runs, autoscaling under bursty load, and graceful shutdown so an in-flight agent run survives a deploy instead of vanishing mid-step.
- Containerizing and Deploying Agent Workers: Long-Running vs. Serverless40 min
- Queueing and Worker-Pool Patterns for Concurrent Agent Runs40 min
- Autoscaling Under Bursty Load, and Graceful Shutdown & Checkpointing40 min
- Module 7 Quiz: Deployment Infrastructure & Scaling Production AgentsQuiz
Module 8
Security, Cost Governance & On-Call for Production Agents
The operational safety net every production agent needs: locking down the credentials tools use, defending against the basic prompt-injection attempts your agent will actually see, capping runaway spend before it happens instead of discovering it on an invoice, and running on-call the way agent failure modes actually demand.
- Secrets and Credential Management for Tool-Calling Agents40 min
- Defending Against Prompt Injection from Tool Outputs and User Input35 min
- Budget Alerting, Hard Spend Caps, and On-Call Runbooks for Agent Failure Modes40 min
- Module 8 Quiz: Security, Cost Governance & On-Call for Production AgentsQuiz
Building Production AI Agents — Final Exam
Comprehensive final exam covering the entire course.