---
title: "Building Production AI Agents — free course with certificate"
description: "Design, build, secure, evaluate and ship AI agents that work reliably with real tools, data and people"
url: https://optimizeall.com/learn/ai-agents-engineering
updated: 2026-10-05
---

AI · Advanced · 506 minutes · free · updated Sep 2026

# Building Production AI Agents

Design, build, secure, evaluate and ship AI agents that work reliably with real tools, data and people

- **Lessons:** 18 in 7 modules
- **Video lectures:** 18 lectures, 162 minutes
- **Updated:** Sep 2026

## Tools you'll use

- Claude API
- Claude Agent SDK
- OpenAI Agents SDK
- Google ADK
- LangGraph
- CrewAI
- Microsoft Agent Framework
- OpenTelemetry
- FastAPI
- Python
- SQLite
- MCP

[Start the course](https://optimizeall.com/learn/ai-agents-engineering/what-is-an-agent)

## About this course

Agents are moving from demos to production, and most fail for engineering reasons, not model reasons. This advanced course teaches you to build agents that work reliably. You will learn when an agent is the right choice and when a workflow is better, the core architectures (prompt chaining, routing, orchestrator–workers, evaluator–optimizer, multi-agent), and how to hand-build the agent loop in Python. You will design tools models use correctly, tune reasoning effort, engineer context and memory, and make long-running agents durable and idempotent. You will add human approvals, defend against prompt injection with least privilege and validators, evaluate outcomes and trajectories with pass^k, and instrument agents with OpenTelemetry. Finally you will cut cost and latency, deploy with sandboxes and staged rollouts, compare today's agent SDKs (Claude Agent SDK, OpenAI Agents SDK, Google ADK, LangGraph, CrewAI, Microsoft Agent Framework), and build and launch a research-and-ops agent end to end.

## What you will learn

- Decide when to use an agent versus a workflow and choose the right architecture pattern
- Build a production-minded agent loop in Python with budgets, error handling and logging
- Design tools, plans and multi-agent delegation that models execute reliably
- Engineer context, memory, retrieval and durable state for long-running agents
- Implement risk-tiered approvals and layered defenses against prompt injection
- Evaluate agents on outcomes, trajectories, reliability (pass^k) and cost, with tracing
- Deploy agents with sandboxes, versioned config and staged rollouts, choosing the right SDK

## Before you start

- [Latest AI Techniques: RAG, Tool Use, Agents & MCP](https://optimizeall.com/learn/latest-ai-techniques-rag-agents-mcp)
- [Advanced Prompt Engineering](https://optimizeall.com/learn/advanced-prompt-engineering)

## Course content

### Agent foundations and architectures

What agents are, the pattern catalog from prompt chaining to multi-agent systems, and a hand-built agent loop you fully understand.

- [What an AI agent really is (and when not to build one)](https://optimizeall.com/learn/ai-agents-engineering/what-is-an-agent): 14 min
- [Workflow and agent architectures: the pattern catalog](https://optimizeall.com/learn/ai-agents-engineering/workflow-and-agent-patterns): 16 min
- [Build the agent loop from scratch in Python](https://optimizeall.com/learn/ai-agents-engineering/agent-loop-from-scratch): 18 min

### Tools, reasoning and multi-agent design

How to design tools agents use correctly, tune built-in reasoning and plans, and decide when multiple agents beat one.

- [Designing tools agents use correctly](https://optimizeall.com/learn/ai-agents-engineering/designing-agent-tools): 16 min
- [Planning, reasoning models and effort control](https://optimizeall.com/learn/ai-agents-engineering/planning-and-reasoning-models): 15 min
- [Multi-agent systems: orchestration, handoffs and delegation](https://optimizeall.com/learn/ai-agents-engineering/multi-agent-systems): 16 min

### Memory, retrieval and durable state

Context engineering, memory design, agentic retrieval with verified citations, and making long-running agents durable and idempotent.

- [Context engineering and agent memory](https://optimizeall.com/learn/ai-agents-engineering/context-engineering-and-memory): 16 min
- [Agentic retrieval: giving agents knowledge they can trust](https://optimizeall.com/learn/ai-agents-engineering/retrieval-for-agents): 15 min
- [State, durability and long-running agents](https://optimizeall.com/learn/ai-agents-engineering/state-durability-long-running): 15 min

### Human oversight, guardrails and security

Risk-tiered approvals, escalation design, prompt-injection defense, least privilege and red-teaming for agents that act on real systems.

- [Human-in-the-loop: approvals, escalation and review](https://optimizeall.com/learn/ai-agents-engineering/human-in-the-loop-approvals): 15 min
- [Guardrails, permissions and prompt-injection defense](https://optimizeall.com/learn/ai-agents-engineering/guardrails-permissions-prompt-injection): 17 min

### Evaluation and observability

Measuring agents on outcomes, trajectories, reliability and cost, and instrumenting them with traces, dashboards and alerts.

- [Evaluating agents: task success, trajectories and judges](https://optimizeall.com/learn/ai-agents-engineering/evaluating-agents): 17 min
- [Observability: tracing, logging and dashboards for agents](https://optimizeall.com/learn/ai-agents-engineering/observability-and-tracing): 15 min

### Production engineering: cost, deployment and frameworks

Cutting cost and latency, deploying agents as reliable services with sandboxes and safe rollouts, and choosing among today's agent SDKs and frameworks.

- [Cost and latency engineering for agents](https://optimizeall.com/learn/ai-agents-engineering/cost-and-latency-engineering): 16 min
- [Deploying agents: architectures, sandboxes and safe rollout](https://optimizeall.com/learn/ai-agents-engineering/deployment-patterns): 16 min
- [The agent SDK and framework landscape in 2026](https://optimizeall.com/learn/ai-agents-engineering/agent-frameworks-landscape): 15 min

### Capstone: build and launch Market Scout

Build a research and operations agent end to end in Python, then evaluate, trace, harden, red-team and prepare it for a staged launch.

- [Capstone part 1: build a research and ops agent end to end](https://optimizeall.com/learn/ai-agents-engineering/capstone-build-research-ops-agent): 25 min
- [Capstone part 2: evaluate, observe, harden and launch](https://optimizeall.com/learn/ai-agents-engineering/capstone-harden-evaluate-launch): 22 min

## Certificate: Certified Production AI Agent Engineer

The holder can design, build and ship production AI agents: choosing between workflows and agents, hand-building the agent loop, designing reliable tools, engineering memory and durable state, implementing human approvals and prompt-injection defenses, evaluating outcomes and trajectories with reliability metrics, instrumenting with tracing, controlling cost, and deploying with sandboxes and staged rollouts.

- **Final assessment:** 30 questions, 45 minutes
- **Passing score:** 80%