Agentic AI: Definition, How It Works, and Examples

Detailed definition

Agentic AI refers to artificial intelligence systems designed to reach a goal across multiple steps, with limited human oversight. IBM defines it as a system able to accomplish a specific goal with limited supervision, by evaluating several possible actions and choosing the most relevant one. Microsoft echoes this, describing AI systems that have agency, meaning the ability to decide and act independently while pursuing a goal.

It isn't a product, it's a category. You can build a chatbot that has nothing agentic about it, and you can build an agent that's fully agentic. What places a system on the agentic side is three ingredients together: a given goal rather than a single question asked, the ability to break that goal down into successive actions, and actually executing those actions through tools, not just a text response.

The notion didn't start with LLMs. It extends an older idea from artificial intelligence, that of the rational agent formalized by Russell and Norvig: an agent perceives its environment through sensors and acts on it through effectors, seeking the best possible outcome given what it knows. What's changed with recent language models is that the agent's "brain" can now reason in natural language, read documentation, and decide to call a tool with no rule coded in advance for every case. See also the Generative AI vs. Agentic AI page for a full comparison of the two notions.

How it works

Technical descriptions converge on a four-step loop. Microsoft sums it up as: perception (ingesting data and signals), reasoning (building a plan), action (execution via tools and APIs), then reflection (evaluating the result and adjusting). Google Cloud describes a similar loop, perception, decision, action, with feedback looping back into the next perception.

Four building blocks make this loop possible:

• Planning: the system breaks a fuzzy goal into a sequence of concrete steps, and can revise that plan if a step fails.
• Tool use: the agent calls APIs, queries databases, sends messages, rather than just describing what should be done.
• Memory: short term to keep track of an ongoing task, long term to remember a project's context from one session to the next.
• Autonomy, which isn't all-or-nothing: it ranges from a simple assistant that proposes an action for approval, all the way to an agent that executes and only escalates the truly critical decisions.

This is where a real dividing line runs through today's market: on one side, asynchronous autonomous agents, which receive an underlying goal, choose their own route to reach it, run continuously in their own execution environment, and only involve a human for a specific decision; on the other, triggered agent-workflows, built on tools like Make, n8n, Zapier, Lindy, Copilot Studio, or Agentforce, where a trigger launches a largely predefined sequence of steps. Both categories are legitimate and serve different needs, it isn't a question of which tool is better, but of what level of autonomy you're looking for.

A concrete example from Atako

At Atako, an agent created by a company runs in its own isolated execution environment, not in a shared worker pool. That persistence changes everything: the agent's memory, files, and scheduled tasks survive its pauses and restarts, exactly what you'd expect from a system pursuing a goal over time rather than answering once and then forgetting.

In practice, the same AI agent receives requests through four channels that all feed the same memory: chat, email, webhooks, and cron. The cron channel illustrates the agentic logic well: the scheduled task isn't configured in a form by a human, it's created by the agent itself when it decides that a regular check is part of its plan for reaching its goal. For complex sub-tasks, the agent can also delegate to an ephemeral sub-agent, whose work shows up as steps in its own activity timeline.

That autonomy stays bounded. Every tool call goes through an explicit grant (agent, connection, specific action, read or read-write scope), denied by default until it has been granted. A use case like CI triage and incident response shows this in practice: the agent monitors a pipeline, correlates the failure with recent commits, notifies the team, and drafts a report, several steps chained toward a goal, with human checkpoints kept where they matter.

Common mistakes

The most common confusion is calling "agentic" any tool that responds intelligently to a prompt. A good chatbot that summarizes a document isn't agentic if it plans nothing and acts on nothing outside the conversation. Conversely, a fixed set of rules automated end to end isn't fully agentic either, even if it runs with no supervision: it lacks decision-making.

A second mistake is believing autonomy is a single on/off switch. In practice it's a dial: a system can plan its actions and propose approval before every sensitive execution, or go much further and only escalate the critical decisions. Mixing up the two extremes leads either to underestimating a genuinely autonomous system, or to overselling a plain copilot as if it acted on its own.

A third pitfall is thinking "agentic" and "generative" are opposites. In reality, as IBM points out, the two notions overlap heavily and often work together: the generative model acts as the reasoning engine inside the agentic system. See the detailed comparison on the Generative AI vs. Agentic AI page.

Going further

If you want to dig into the distinction with plain generative models, the Generative AI vs. Agentic AI page details where one stops and the other begins, with the same care not to pit the two against each other artificially. Our blog article on the topic, Generative AI vs. Autonomous Agents, the Real Differences, covers the same angle with examples broader than Atako's own. The full glossary covers other related notions, such as agent orchestration or the autonomous AI agent, to place agentic AI within the field's wider vocabulary.