Agentic AI & agents
What Is Agentic AI?
The shift from AI that answers questions to AI that gets things done.
Agentic AI describes systems that can plan a sequence of steps, use tools, and take actions toward a goal with some degree of autonomy, rather than simply answering a single question and stopping.
Where a chatbot responds to one message at a time, an agent might break a goal into steps, call several tools, check its own progress, and adjust its plan — all without a human directing each individual step.
Key takeaways
From single response to multi-step action
A standard LLM call takes an input and produces one output. Agentic AI wraps that basic capability in a loop: the model decides what to do next, takes an action (often by calling a tool), observes the result, and decides again, repeating until the goal is met or a limit is reached.
This loop is what allows an agent to handle tasks too complex for a single prompt-response exchange, like researching a topic across multiple sources and compiling a summary.
The building blocks of an agent
A working agent typically needs a way to reason about the next step, access to tools that can take real actions (search, calculations, database writes), memory to track progress across steps, and some mechanism to know when to stop.
Missing any of these leads to predictable failures: agents that loop endlessly, forget earlier context, or take actions with no way to verify they worked.
Autonomy is a dial, not a switch
Fully autonomous agents that operate with zero human oversight are rare in production for good reason — the risk of costly or harmful mistakes is real. Most deployed agentic systems use bounded autonomy: clear limits on what actions are allowed, budgets on steps and cost, and checkpoints for human review on high-stakes actions.
Designing the right level of autonomy for your use case is a core AI engineering decision, not an afterthought.
Put this into practice