What Is Agentic AI? Definition and How It Works

In one line: AI that plans, uses tools and acts, rather than only answering
Read this if: The vocabulary is new, or the terms are being used loosely
Next: AI agents vs chatbots, for the practical difference

Agentic AI, defined

Agentic AI is artificial intelligence that can plan, use tools, and take multi-step actions toward a goal instead of only answering a single prompt.

A language model on its own maps an input to an output and stops. An agentic system is given an objective and a set of tools. It decides at run time which one to use next.

Agentic AI is a system, not a model. The model supplies judgment and language. The surrounding system supplies memory, access to your data, permission to act, and a way to score the result. Building that surrounding system is what custom AI agent development means.

Agentic AI vs a single LLM prompt

A single prompt produces one answer. An agentic AI system loops: gather data, act on external systems, re-evaluate, repeat.

Number of steps. A prompt is one turn. An agent may take dozens for one request.
Access. A prompt uses only what you paste in. An agent can query a database, call an API, and write a record back.
Recovery. A wrong prompt returns a wrong answer. An agent can notice a failed tool call and retry, reroute, or escalate.
Accountability. A prompt leaves no trail. An agentic system logs which steps ran and what changed, which makes review and closed-loop evaluation possible.

Agentic systems do more and cost more to build and operate. If one prompt answers the question, use one prompt.

The core components of an agentic AI system

Almost every production agentic AI system has five parts: planning, tool use, memory and retrieval, orchestration, and evaluation.

1. Planning

Planning is the system deciding how to break a goal into steps. It turns "grade this report" into parse, segment, retrieve criteria, assess, and assemble. Weak planning is why an agent wanders or stops early.

2. Tool use

Tools are the functions, APIs, and integrations an agent is allowed to call. Tool use is what performs the action: creating the record, sending the quote, writing to the queue. Every tool is also a permission boundary.

3. Memory and retrieval

Memory is what the agent carries within a task and across tasks. Retrieval pulls the right passage out of your documents. That is where vector databases and retrieval come in. Searching by meaning grounds an answer in a corpus larger than any context window.

4. Orchestration

Orchestration is the control layer that routes work between steps and agents. It defines what happens when a step fails. Once a job needs several specialized agents, multi-agent orchestration keeps them from colliding.

5. Evaluation

Evaluation is scoring the output against criteria defined before the build. Failures then feed back into the prompts, retrieval, tools, and routing.

Worked example: grading a document

A school submits a report and asks for it to be assessed against a published standard.

A single prompt cannot do this well. The document is too long to reason about in one pass. The criteria live in a separate corpus, and the result has to be defensible to a reviewer.

Intake. A DOCX or PDF is parsed into structured text.
Plan. Segment the document, find the applicable criteria, assess each segment, assemble the feedback.
Segment. The text is split into sections that can be assessed independently. The next step then runs in parallel.
Retrieve. For each segment it pulls the relevant passages from the corpus of review criteria. The assessment is grounded in the standard.
Assess concurrently. Many small, token-efficient model calls run at once, each judging one segment.
Assemble and check. The judgments are merged into one report, then evaluated against the same criteria.
Hand to a human. A reviewer sees the assessment with its criteria and can override it.

That is the architecture behind Report Grader, an education product Fig AI Systems built and owns. It runs over 1,000 token-efficient LLM calls per report analysis. The Report Grader product page covers it in more detail.

Where agentic AI does not fit

Agentic AI is the wrong choice whenever a task is single-step, fully deterministic, or has no checkable definition of a correct result.

The rules are already known and stable. If the logic can be written as conditions and thresholds, write it as code. Conventional software is cheaper and exactly repeatable.
The task is one question with one answer. Summarising a paragraph or drafting a reply needs a model call, not an agent loop.
Nobody can say what "correct" means. If the team cannot describe how it would grade the output, there is nothing to evaluate against. Define the criteria first.
Mistakes are irreversible and unreviewable. Where an error cannot be caught or undone, keep the decision with a person. Let the system prepare the work, not complete it.
The volume does not justify the build. A workflow that runs a few times a month rarely repays the build.
The data is not reachable. An agent with no route into your systems is a chat window. Integration work has to happen first.

Sometimes the answer is a conversational assistant that can do a few concrete things. Our comparison of AI agents and chatbots walks through which one a workflow needs.

Agentic AI glossary

Eight terms you will meet in any agent project, with plain definitions.

AI agent: An AI agent is a software system that pursues a goal by deciding its own next step. It plans, calls tools, reads the result, and repeats until the goal is met.
Multi-agent system: A multi-agent system splits a job across several specialized agents, each owning a narrow task. An orchestrator routes work between them and assembles the result. The pattern is used when one agent with one long prompt becomes unreliable or too slow.
Orchestration: Orchestration is the control layer that decides which agent, tool, or model call runs next.
Tool use: Tool use is a model calling an external function, API, database, or application on your behalf.
Retrieval-augmented generation (RAG): Retrieval-augmented generation fetches passages from your own documents and passes them to the model as context. RAG grounds an answer in source material, which is what makes citations possible.
Vector database: A vector database stores text, images, or records as numeric embeddings. They can then be searched by meaning, not just by exact keyword.
Human-in-the-loop: Human-in-the-loop means a person reviews, approves, or overrides the agent at a checkpoint before an action becomes final.
Closed-loop evaluation: Closed-loop evaluation tests an agent against explicit review criteria and feeds the results back into the system. The loop closes when that output changes the prompts, tools, or routing.

Cost, timelines, integration, and security are covered in the agentic AI and AI agent development FAQ.

Frequently asked questions

Is agentic AI the same thing as generative AI?

No. Generative AI describes models that produce text, images, code, or audio. Agentic AI describes systems built around those models. Those systems plan steps, call tools, and act on other software to finish a task.

Does agentic AI mean the AI runs without supervision?

No. Autonomy is a design choice, set per step. A well-built agent runs the low-risk, high-volume steps on its own. It stops at a human-in-the-loop checkpoint before anything expensive, irreversible, or subject to review.

How do you know an agentic AI system is working correctly?

You define the review criteria before the build. You run the agent against cases where the correct outcome is known. Closed-loop evaluation then feeds the failures back into the prompts, retrieval, tools, and routing.

What does an agentic AI project usually start with?

It usually starts with one workflow that is repeatable, high volume, and has a checkable definition of a correct result. Fig AI Systems scopes that workflow first, gets the evaluation loop around it, then adds more agents.

What is agentic AI?