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The action space defines what an agent can reach. Too small and it cannot finish the job; too large and it makes more mistakes and expands the blast radius of each one. Scoping it deliberately is a security control, not a configuration detail. In practice: Read the calendar and send invites — but not delete events or email externally.
Models are stateless, so memory is something you build: short-term state within a task, and long-term storage retrieved when relevant. Without it, an agent relearns the same facts every session. With it, you inherit questions about accuracy, staleness, and data protection. In practice: Remembering across sessions that this client wants British English and no bullet points.
Agentic AI is the adjectival form: the qualities that make a system an agent, chiefly autonomy and the ability to affect the world. The practical shift is from ’the model writes a draft’ to ’the model does the task’, which changes the risk profile entirely. Anything that can act can act wrongly, at speed. In practice: The difference between a model describing how to file the expense and one filing it. ...
The agentic loop is the control structure that turns a stateless model into something that gets work done. Each pass feeds the outcome of the last action back into the next decision, so the agent can recover from failures. Loops need explicit stopping conditions, or an agent will happily retry forever and bill you for it. In practice: Search → read result → realise it is wrong → refine query → try again. ...
An agent is given an objective, not a script. It decides what to do, calls tools to do it, observes the result, and adjusts. The word carries two senses: in reinforcement learning it is the entity acting in an environment, while in current product usage it means an LLM wired up to tools and a loop. Both are correct; check which one a document means. In practice: ‘Find the three cheapest flights and put them in the sheet’ — the agent chooses the steps. ...
Autonomy ranges from suggesting, to acting with approval, to acting and reporting, to acting silently. It is the single most important design decision in an agent product, because it sets who is accountable when something goes wrong. The EU AI Act’s definition of an AI system explicitly turns on operating with varying levels of autonomy. In practice: Draft the reply (low) vs. send the reply (high) — same model, very different risk. ...
Computer use lets an agent work with software that has no API by driving the interface directly. It unlocks legacy systems and long-tail tools at the cost of being slower and more brittle than a proper integration. Because it inherits the user’s full access, sandboxing is not optional. In practice: Filling a form in an internal portal that was built in 2009 and has no API.
A copilot works alongside you inside the application — suggesting the next line, drafting the paragraph, proposing the formula. The distinction from an agent is who holds the wheel: a copilot proposes, you accept. The name is now generic despite also being a product brand. In practice: Autocomplete that finishes the function you were about to write.
You describe your functions and their parameters; the model returns a structured request naming one and supplying arguments. Critically, the model does not execute anything — your application does, which is where validation and authorisation belong. It is the plumbing under almost every agent product. In practice: The model emits get_order_status(order_id=‘A-4471’); your backend decides whether to run it.
Human-in-the-loop puts a checkpoint at the consequential step — sending, paying, publishing, deciding. It is the main practical control on agent risk and, for high-risk systems under the EU AI Act, a version of it is a legal requirement rather than a nice-to-have. The failure mode is rubber-stamping: a review that nobody actually performs is not a control. In practice: The agent prepares the refund; a person clicks approve.