Google's free AI agents course is back on every founder's feed this week — specifically the distilled, roughly one-hour version of the 5-Day AI Agents Intensive that Google and Kaggle ran June 15–19, 2026. The full course reached more than 1.5 million learners on its debut and came back this year with a "vibe coding" framing. If you don't have an hour, here's the entire thing in five parts — and the one line on where each part actually breaks once real traffic hits it.
The through-line worth internalizing before anything else: an agent is a model in a loop with tools and a termination condition. The model is the least differentiated piece. Everything below is about the loop.
1. Your first agent: a model, instructions, and tools#
Part one builds the smallest real agent: an LLM given a set of tools and told to call them until a task is done. The lesson founders skip is the last clause. A single model call that uses one tool is a wrapper, not an agent — the agent part is the loop that decides whether to call another tool or stop.
Where it breaks: no exit condition. A loop that can't decide it's finished either quits too early (half-done work) or never quits (runaway cost). Define "done" before you define the tools.
2. Agent memory: short-term, persistent, long-term#
Part two splits memory into three: the scratchpad inside a single run, the state that persists across a session, and the long-term store the agent reads and writes across days. They have different lifetimes, different costs, and different failure modes — and conflating them into one blob is the most common design error we see.
Where it breaks: treating memory as one thing. Long-term memory you never prune becomes a token bill that grows every turn; short-term memory you never clear becomes context rot. Decide what each tier is for.
3. Agentic loops: long-running, self-correcting agents#
Part three is the heart of it: agents that plan, act, observe the result, and correct — over minutes or hours, not one turn. This is where the "agentic" in agentic engineering lives, and it's where the context window quietly becomes the constraint that decides whether the agent stays coherent to the end.
Where it breaks: the loop that never terminates and the context that silently fills. A long-running agent needs checkpointing and context management as first-class concerns, not afterthoughts. Budget the context the way you'd budget money.
4. MCP servers: the protocol vs. a plain API#
Part four teaches the Model Context Protocol — the standard way to expose tools and data to an agent — and, crucially, when MCP earns its complexity over a plain REST endpoint. MCP shines when many agents need to discover and call the same tools, or when you're publishing tools for clients you don't control. For a single in-house agent hitting one internal service, a function call is often the honest answer.
Where it breaks: building an MCP server where an API would do. The protocol is a distribution decision, not a default. Reach for it when you have consumers to standardize for.
5. Multi-agent systems: orchestrating specialists#
Part five composes single agents into a workflow — a planner, a researcher, a writer — coordinated toward one outcome. It's the most exciting module and the one most likely to be reached for prematurely.
Where it breaks: multi-agent before single-agent reliability. If one agent isn't dependable, three coordinating agents multiply the failure surface rather than dividing the work. And once you do go multi-agent, the first real decision is deterministic vs. LLM orchestration — who's actually in charge of the control flow.
What it means#
The curriculum is a good free course. It's a better free checklist. Whether or not you enroll, the five parts map cleanly onto the five ways agents fail in production, and the framing — model in a loop, with memory, tools, a protocol, and optional friends — is the right mental model to build against. The model you pick will be obsolete in a quarter. The loop discipline won't be.
