AI Agent Engineering Course: 6 Picks + LLMOps (2026)

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. Together they trace one argument: a single prompt, however clever, is not a system, and every layer in this roundup exists because that limit shows up somewhere real.

Organized by agent capability: memory first (the foundation), then action (browser and computer use), then evaluation, then orchestration (multi-agent), then ops (production).

The durations are friendlier than they look. Everything here is beginner or intermediate level, and every course runs between roughly one hour and three. The evaluation course is among the longest, which tells you where the field’s center of gravity has moved: anyone can demo an agent, the hard part is knowing whether it worked. The instructor roster is the quiet quality signal: two Letta founders on memory, the crewAI founder on both orchestration courses, Anthropic’s own API at the center of the computer-use course.

How to read the order below: it is a dependency chain, not a ranking. Memory before action, because acting without state is wandering. Evaluation before orchestration, because more agents without measurement is just more ways to fail. Ops last, because it wraps everything. If you already own one layer, jump straight in and read the rest as context.

Agent memory

LLMs as Operating Systems: Agent Memory (Charles Packer and Sarah Wooders, via DeepLearning.AI, free)

The clearest treatment of agent memory on this list. Charles Packer and Sarah Wooders founded Letta, the team behind MemGPT, and they teach context management as an operating-systems problem: the model’s context window is RAM, external storage is the disk, and a capable agent has to page between them on its own. You build agents with editable memory and watch them manage it autonomously. If your agent forgets what it was doing three turns ago, this is the course. The conceptual model alone is worth the ninety minutes.

Why the OS framing matters practically: most agent failures that look like reasoning failures are actually eviction failures, the wrong facts fell out of the window at the wrong time. Once you treat the window as a managed resource, those bugs stop being mysterious and start being schedulable, measurable, and testable, like any other resource contention problem. That reframing is the course’s real product. At an hour and a half, intermediate level, it is a dense stop but not a heavy one.

Course: LLMs as Operating Systems: Agent Memory.

Browser and computer use

Building AI Browser Agents (Div Garg and Naman Garg, via DeepLearning.AI, free)

Browser agents that navigate real websites, fill forms, and scrape structured data from pages that were never meant to be scraped. Div Garg and Naman Garg build up in honest steps: a simple scripted web agent, then an autonomous one, then the reliability work, DOM interaction, selector strategies that survive redesigns, and the failure modes (modals, auth walls, JS-heavy single-page apps) that make browser agents fragile in the wild. For anyone building agents that have to touch the open web.

The reliability emphasis deserves its own sentence. Most browser agent projects die not because the agent cannot click but because the page changed and nobody noticed; the course spends its back half on exactly that class of failure, which is the difference between a demo and a tool you can schedule. At an hour and change, intermediate level, it is also the shortest substantive stop on this page.

Course: Building AI Browser Agents.

Building Towards Computer Use with Anthropic (Colt Steele, with Anthropic, via DeepLearning.AI, free)

The more ambitious sibling: agents that control a full desktop (screenshots, mouse, keyboard), not just a browser. Colt Steele walks the Anthropic computer-use API end to end, including the screenshot-act loop that underlies it 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 rather than hand-waves.

Take this one after the browser course, not instead of it. Computer use is strictly more general and strictly more dangerous, and the failure stories (an agent confidently closing the wrong window) only make sense once you have felt how brittle even browser-scoped agents are. The ordering in this roundup is deliberate. Beginner level, an hour and three quarters, nine lessons: the gentlest entry point into this track despite the premise.

Course: Building Towards Computer Use with Anthropic.

Evaluating agents

Evaluating AI Agents (John Gilhuly and Aman Khan, via DeepLearning.AI, free)

The evaluation discipline that agent projects most often skip. John Gilhuly and Aman Khan teach structured assessment end to end: decompose the agent into its moving parts, build labs around real tasks, and grade trajectories (did the agent take the right steps, not just produce the right answer) rather than final answers alone. LLM-as-judge setups and regression suites that catch agent drift after a prompt or model change round it out. 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.

It is also the longest short course here at two and a half hours, and the length goes into labs, not slides. You build an agent first, then build the thing that tells you whether your next version of it is better. That second build is the one your team keeps using long after the course content fades.

Course: Evaluating AI Agents.

Multi-agent systems

Practical Multi AI Agents and Advanced Use Cases with CrewAI (João Moura, via DeepLearning.AI, free)

The applied multi-agent course. João Moura founded crewAI, so the framework is taught by its own author, and the use cases are real business tasks rather than toy dialogues: an automated project spanning planning, estimation, and allocation runs through the course as a spine. Role assignment (researcher, writer, reviewer) and the handoff patterns that make a crew more effective than a single agent, heavy on code, light on theory. For teams evaluating whether to split a monolithic agent into a crew.

One pattern worth stealing even outside crewAI: the spine project treats planning, estimation, and allocation as separable jobs and assigns them to different agents. Watching a multi-agent system do project management on screen is the fastest way to understand what orchestration actually buys you, and what it does not.

Course: Practical Multi AI Agents and Advanced Use Cases with CrewAI.

Multi AI Agent Systems with CrewAI (João Moura, via DeepLearning.AI, free)

The companion course, focused on system design rather than individual use cases, and at three hours the longest on this page. Where the practical course shows one crew working, this one asks when a multi-agent architecture pays off at all, when it is overkill, and how to design the communication topology (hub-and-spoke, peer-to-peer, hierarchical) for a given problem. You direct a team of agents through natural language and learn to exceed what single-model prompting does. Read alongside the practical course for the full picture; if you only take one and your problem is coordination-heavy, take this one.

A design warning the course makes well: more agents is not more capability. Every added agent adds coordination cost and a new surface for failure, and the topology lessons exist to keep that cost visible. Start with the smallest crew that could work, and only add members when the evals from the previous section say you need them.

Course: Multi AI Agent Systems with CrewAI.

LLMOps (production layer)

LLMOps (Erwin Huizenga, via DeepLearning.AI, free)

The production layer that every other course on this list assumes you already have. Erwin Huizenga walks a complete fine-tuning lifecycle for one specific task: prepare the data, automate the pipeline, deploy the result, monitor it. Model registry, 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.

The scope is honest about being narrow: one task, one pipeline, done properly. That narrowness is the lesson. Most LLMOps failures are breadth failures, five half-finished pipelines and no registry; this course shows what finished looks like, so you can recognize the difference in your own stack. Beginner level, ninety minutes, six lessons: the shortest course on this page, and the one whose absence is loudest in failed agent projects.

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.

If you are staffing a team rather than training yourself, hand out the layers: one person on memory, one on evaluation, everyone on LLMOps. The courses are short enough that a reading group can clear this entire page in a fortnight of lunch hours, and the shared vocabulary is worth more than any single certificate. Total cost in hours for this entire page: under fifteen, less than most teams lose to one unclear launch review.