Most people's first experience with AI is a question-and-answer loop: you type something in, a model responds, and the interaction ends there. An AI agent is different — it's a system where a model can take actions (call a tool, run a search, write a file, hit an API), observe what happened, and decide what to do next, often across multiple steps, with minimal human input along the way.
A single wrong step in a multi-step agent can compound — an agent that misreads one tool's output can confidently take five more wrong actions built on that mistake before anyone notices. This is why the actual engineering work in agent systems is less about clever prompting and more about constraints: what actions is the agent even allowed to take, what gets validated before it's treated as fact, and where does a human need to check in before something irreversible happens (sending an email, executing a trade, modifying a database).
Not every automation problem needs an "agent" in the full sense — plenty of valuable automation is simpler and more predictable: a script that watches for a trigger and runs a fixed, well-tested pipeline. The AI-agent framing is worth reaching for when the steps genuinely can't be hard-coded in advance, because the right next action depends on unpredictable real-world input.
The AI Agents & Automation track in the Academy is built around that distinction — starting with tightly-scoped automation projects where the logic is fully understood and testable, then building up to agent-style systems where reliability constraints are the actual design problem, not an afterthought.
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