An AI agent engineering course worth your time has to cover more than “call the tool-use endpoint.” These seven free DeepLearning.AI courses cover the full agent stack: memory, browser and computer use, evaluation, multi-agent orchestration, and LLMOps (the production layer most tutorials skip). Six agent courses plus one LLMOps section, each a distinct capability.
Organized by agent capability: memory first (the foundation), then action (browser and computer use), then evaluation, then orchestration (multi-agent), then ops (production).
Agent memory
LLMs as Operating Systems: Agent Memory (DeepLearning.AI, free)
The clearest treatment of agent memory on this list. Frames context management as an operating-systems problem (pages of context swapped in and out of a fixed window) and shows how to build a memory layer that actually scales beyond the context length. If your agent forgets what it was doing three turns ago, this is the course. The conceptual model alone is worth the hour.
Course: LLMs as Operating Systems: Agent Memory.
Browser and computer use
Building AI Browser Agents (DeepLearning.AI, free)
Browser agents that navigate real websites, fill forms, and scrape structured data from pages that were never meant to be scraped. Covers DOM interaction, selector strategies that survive redesigns, and the failure modes (modals, auth walls, JS-heavy SPAs) that make browser agents fragile. For anyone building agents that have to touch the open web.
Course: Building AI Browser Agents.
Building Towards Computer Use with Anthropic (Anthropic, via DeepLearning.AI, free)
The more ambitious sibling: agents that control a full desktop (screenshots, mouse, keyboard), not just a browser. Covers Anthropic computer-use API, the screenshot-act loop, and the safety guardrails you need when an agent can click anything. For research and internal tooling; production deployment needs careful sandboxing, which the course addresses.
Course: Building Towards Computer Use with Anthropic.
Evaluating agents
Evaluating AI Agents (DeepLearning.AI, free)
The evaluation discipline that agent projects most often skip. Covers trajectory evaluation (did the agent take the right steps, not just produce the right answer), LLM-as-judge setups, and regression suites that catch agent drift after a prompt or model change. The single most useful course on this list if you already have an agent in production and no way to tell if a change made it better or worse.
Course: Evaluating AI Agents.
Multi-agent systems
Practical Multi AI Agents and Advanced Use Cases with CrewAI (DeepLearning.AI, free)
The applied multi-agent course. Covers CrewAI (a popular orchestration framework), role assignment (researcher, writer, reviewer), and the handoff patterns that make a multi-agent system more effective than a single agent. Heavy on code, light on theory. For teams evaluating whether to split a monolithic agent into a crew.
Course: Practical Multi AI Agents and Advanced Use Cases with CrewAI.
Multi AI Agent Systems with CrewAI (DeepLearning.AI, free)
The companion course to the one above, focused on system design rather than individual use cases. Covers when a multi-agent architecture pays off, when it is overkill, and how to design the communication topology (hub-and-spoke, peer-to-peer, hierarchical) for a given problem. Read alongside the practical course for the full picture.
Course: Multi AI Agent Systems with CrewAI.
LLMOps (production layer)
LLMOps (DeepLearning.AI, free)
The production layer that every other course on this list assumes you already have. Covers model registry, pipeline automation for fine-tuning, monitoring (drift, latency, cost), and the CI/CD patterns specific to LLM systems (where “the model” is a moving target). Merged into this roundup as its own section because it is a single course, but it is arguably the most important one if you are shipping agents to real users. Without LLMOps, the rest is a notebook.
Course: LLMOps.
New to agents: start with Agent Memory (the clearest conceptual model), then Evaluating AI Agents (so you can tell if anything you build actually works), then one of the two CrewAI courses for orchestration. Skip computer use until you have a concrete sandboxed use case; it is powerful but easy to get wrong.
Continue learning
- Back to the LLM/ML courses roundup hub
- Foundations roundup (Karpathy)
- RAG and vector databases roundup
- HuggingFace Agents Course (dedicated tutorial)
- Agentic AI Design Patterns (dedicated tutorial)
- Berkeley Advanced Agents (dedicated tutorial)