How Do AI Agents Work?
AI agents work by taking a goal, forming a plan, calling tools, reviewing the results, and choosing whether to continue or stop. The phrase how do AI agents work? The agent loop, explained captures this cycle: the model uses feedback from each action to guide its next step. Their results depend on clear tool instructions, useful stopping conditions, and human review when uncertainty is high.
An AI agent works by running a loop, not by answering once. It takes a goal, drafts a plan, calls tools to gather information or take action, checks the result against the goal, and adjusts before trying again. Anthropic, the AI lab behind the Claude model family, defines agents as "systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks" — as opposed to a fixed script a human wrote in advance (Anthropic, "Building Effective Agents," 2024).
That loop is the entire mechanism. Everything else — which tools it can call, how long it runs, how it decides it's done — is a variation on that same goal, plan, act, review cycle. The sections below follow the loop in the order it actually runs, using a lead-generation task as the worked example throughout.
What Is an AI Agent?
An AI agent is a large language model (LLM) — the type of AI system, such as GPT-4 or Claude, trained to predict and generate text — wired into a loop where it can call external tools, observe what they return, and decide its own next step. It is defined by that loop, not by any single skill. A model that only answers questions is not an agent; a model that plans, acts, and re-plans is.
Anthropic's engineering team, drawing on work with "dozens of teams building large language model (LLM) agents across industries," lists the capabilities that make this loop possible: understanding complex, multi-part instructions; reasoning and planning across several steps; calling tools reliably; and recovering when a step fails (Anthropic, 2024). Each of those four capabilities maps directly onto one stage of the loop covered further down.
Anthropic also names the base ingredient underneath all of this: what it calls "the augmented LLM," a model given three specific additions on top of raw text generation — retrieval (the ability to search a knowledge source for facts it wasn't trained on), tools (the ability to call external functions), and memory (the ability to keep track of what happened earlier in the same task) (Anthropic, 2024). An agent is what you get when a model with those three additions is also allowed to decide, on its own, when and how to use them.
AI vs. AI Agents: What's the Difference?
A plain LLM call takes one input and returns one output, with no memory of what happens after and no way to act on the world. An AI agent wraps that same model in a loop that lets it call tools, read the results, and choose what to do next — for as many rounds as the task takes, not just one.
OpenAI, the AI lab behind ChatGPT and GPT-4, documents this as a five-step "tool calling flow" that a single chat response never goes through:
- The application sends the model a request along with a list of tools it is allowed to call.
- The model examines the request and returns a tool call instead of a final answer, if it decides one is needed.
- The application executes that tool call in real code, using the arguments the model supplied.
- The application sends the tool's output back to the model as a second request.
- The model returns a final answer, or issues another tool call and repeats the cycle.
(Source: OpenAI, "Function calling," platform docs.) A chatbot answering a question stops after step one. An agent keeps looping through steps two to five until the goal is met or it hits a stopping condition.
The distinction is not about which model is running underneath — the same GPT-4 or Claude model can power either a single-turn chatbot or a looping agent. It is about how much of the application's control flow is handed to the model. A chatbot's next message is written by a human clicking send; an agent's next action is chosen by the model itself, based on what its last tool call returned.
The AI Agent Loop
The agent loop has four repeating stages: form or refine a plan against the goal, call a tool, observe what the tool returns, and decide whether to act again or stop. Anthropic summarizes the mechanism plainly: agents are "typically just LLMs using tools based on environmental feedback in a loop" (Anthropic, 2024). Nothing about the definition requires exotic architecture — it requires the model to see the outcome of its own actions and respond to it.
Four properties distinguish this loop from a single model call:
- It has a stopping condition. The agent keeps going until the goal is met or a limit — a maximum number of steps, a time budget, or a human check-in — is reached.
- It reads its own output. Each tool result becomes part of the next prompt, so the model's next decision is grounded in what actually happened, not just what it predicted would happen.
- It can recover from a failed step. A tool call that errors or returns nothing useful becomes new information the model reasons over, rather than a dead end.
- It can ask for help. Anthropic notes that agents "can then pause for human feedback at checkpoints or when encountering blockers" rather than guessing past a point of real uncertainty (Anthropic, 2024).
Goal and Planning
Planning is the step where the model turns an open-ended goal into an ordered sequence of smaller actions it can actually execute. This works because reasoning and acting reinforce each other: a 2022 paper from Shunyu Yao and coauthors, called ReAct (short for "Reasoning + Acting"), found that interleaving a model's reasoning traces with its actions lets the model "induce, track, and update action plans" as new information arrives, rather than committing to one plan upfront and never revising it (Yao et al., "ReAct: Synergizing Reasoning and Acting in Language Models," arXiv:2210.03629, 2022).
Take the lead-generation example: the stated goal is "find 20 qualified leads in the fintech space this week." Planning breaks that into concrete sub-tasks — define what "qualified" means, pick a source to search, decide a scoring rule, and decide where the results should land. None of that plan is fixed in advance; the agent drafts it, and revises it once the first tool call comes back with real data.
Using Tools and Taking Action
Taking action means the agent calls a real tool — a search API, a CRM, a spreadsheet, an email sender — with structured arguments, and receives a structured result back in return. This is the step, described above as OpenAI's tool-calling flow, where the loop actually touches the outside world instead of just reasoning about it in text.
In the lead-generation example, the tools are specific and swappable. The agent might call a search API such as Exa or Tavily, two APIs purpose-built for feeding real-time web results to LLM agents, to pull recent company data. It might then write matches into a CRM platform like HubSpot, Salesforce, or Attio to hand the list to a salesperson. AI Agent maintains a directory of 59 such integrations spanning CRM, search, email, and analytics tools that an agent can be wired to call in exactly this way (AI Agent connections directory).
Every one of those tools has to be described to the model before it can be called. OpenAI's docs draw a precise line here: a function is a specific kind of tool "defined by a JSON schema," meaning the tool's name, its required arguments, and their types are all spelled out in a machine-readable format the model reads before deciding whether to call it (OpenAI, "Function calling," platform docs). This is why tool descriptions matter as much as the tools themselves — a model can only call a search API or a CRM correctly if it has an accurate, unambiguous description of what arguments that tool expects.
The ReAct paper's own benchmarks show why interleaving reasoning with these tool calls matters: on the ALFWorld and WebShop interactive decision-making benchmarks, ReAct-style agents beat pure imitation- and reinforcement-learning baselines by an absolute success-rate margin of 34% and 10% respectively (Yao et al., 2022). Reasoning without action, or action without reasoning, both underperform the combination.
Reviewing and Adjusting
Reviewing means comparing the tool's actual output against the goal and deciding whether to continue, retry, or stop — it's what keeps a loop from running forever or quietly drifting off-task. This step is also where most of an agent's real-world limitations show up, because reviewing gets harder the longer and more open-ended a task becomes.
METR, a nonprofit that benchmarks frontier AI systems, measured this directly: as of March 2025, the best available models succeeded on almost 100% of tasks that take a human expert under 4 minutes, but succeeded less than 10% of the time on tasks taking a human expert around 4 hours (METR, "Measuring AI Ability to Complete Long Tasks," 2025). The same research found that the length of task an agent can reliably complete has been doubling roughly every 7 months for the past six years — meaning the review-and-adjust step is the current bottleneck on longer, multi-stage work, not the planning or tool-calling steps (METR, 2025).
In the lead-generation example, reviewing looks like this: after the search tool returns 40 companies, the agent checks each one against the scoring rule from the planning step, discards the ones that don't fit, and only then decides whether it has 20 qualified leads or needs to search again with adjusted criteria.
This is also where stopping conditions matter most. Anthropic recommends that agent designers set an explicit limit — "such as a maximum number of iterations" — precisely because a review step with no ceiling can loop indefinitely on a task it keeps judging as unfinished (Anthropic, 2024). A well-built agent doesn't just know how to try again; it knows when to stop trying and hand the result, however incomplete, back to a person.
A Real-World Example: AI Agents for Lead Generation
Running the full loop end-to-end on the lead-generation task looks like this: the agent takes the goal, plans a search-and-score approach, calls a search tool for company data, calls a CRM tool to check for existing contacts, scores and filters the results, and either delivers the finished list or loops back to search again with tightened criteria.
- Goal: find 20 qualified fintech leads this week, where "qualified" means a defined company size, industry, and funding stage.
- Plan: search for companies matching the profile, cross-check against existing CRM records to avoid duplicates, then score and rank the remainder.
- Action: call a search tool for the initial company list, then call the CRM's API to check which companies are already contacts.
- Review: compare the surviving list against the "20 qualified leads" target; if short, adjust the search terms and run the loop again.
The mechanism doesn't change for a different goal — only what the tools are and what "qualified" or "done" means. That's the same loop covered in every section above, run once with real inputs.
What each part does
| Component | What it does | What breaks if it is missing |
|---|---|---|
| Goal and planning | Turns the desired outcome into workable actions and criteria | The agent may pursue irrelevant actions or lose track of the goal |
| Using tools and taking action | Sends structured requests to external systems and receives results | The agent can reason about work but cannot gather information or change systems |
| Reviewing and adjusting | Compares results with the goal and revises the next action | Errors, weak results, and drift can continue without correction |
Frequently asked questions
How much does it cost to use an AI agent?
AI Agent pricing starts at $49 for the Start tier, while Pro is $149. The cost of an agent also depends on the tools it needs and the complexity of the work it performs.
How much effort does it take to set up an AI agent?
Setup requires defining the goal, breaking the work into actions, connecting the needed tools, and describing those tools accurately. You also need rules for reviewing results, stopping, retrying, and asking a person for help.
What risks come with using an AI agent?
An agent can choose an unsuitable action, misunderstand a tool result, or continue working with incorrect assumptions. Human checkpoints, clear stopping conditions, and review of important outputs help control those risks.
What can cause an AI agent to fail?
Failures can come from tool errors, incomplete results, inaccurate tool descriptions, or plans that drift away from the original goal. Long tasks are harder because each review depends on the quality of earlier actions and observations.
What does an AI agent replace?
An AI agent can replace parts of a fixed workflow or a single-turn chatbot when the work requires repeated searching, checking, updating, or writing to another system. A person still defines the goal and may need to review the result or handle blockers.