LLM agents are AI systems that use large language models (LLMs) to carry out tasks based on language input. They maintain task context across multiple steps, allowing them to decide what should happen next based on earlier input. In finance, for example, an agent might review a flagged payment and notify a compliance officer if the pattern breaks internal rules.

They are a type of AI agent, which describes systems that carry out multi-step tasks by deciding what to do next based on evolving input. Unlike typical LLM applications that respond to prompts in isolation, an agent continues across steps using context from previous actions.

To build an agent, developers combine a language model with control logic and tracking functions using AI agent frameworks. The result is a system that works through complex processes with minimal oversight. In enterprise settings, agents are often used where each step depends on changing data or prior activity.

How do LLM agents work? 

LLM agents move through a sequence of steps, beginning with input and adapting based on earlier actions or outcomes. Each stage builds on previous decisions, allowing the agent to adjust its behavior as the task unfolds. The steps below outline how an agent moves from instruction to action.

Receiving and processing input

An LLM agent begins by receiving a prompt written in natural language, such as a question or instruction. It interprets the input using the underlying language model, drawing on stored memory or recent context if available. Instead of relying on fixed commands, it adapts to the phrasing and structure of each new instruction.

Analyzing and structuring tasks

After understanding the input, the agent determines what needs to happen next. It breaks down the request into smaller steps, which may be handled in order or adjusted as new information appears. The agent forms a step-by-step plan based on prior inputs, rather than following a pre-written script.

Calling tools and executing actions

If the task requires access to external systems, the agent selects the appropriate tool and uses it. The action might involve interacting with a database or calling a function in a connected system. What it does next depends on the plan formed earlier and any constraints set by the system.

Reviewing results and iterating

After completing an action, the agent evaluates the result to determine what should happen next. It can revise its behavior partway through the task based on the outcome of earlier steps. The feedback loop enables the system to remain flexible as the process continues.

Generating and returning output

When the task reaches a suitable endpoint, the agent produces a response. This might confirm that the task is complete or provide information requested earlier. Output reflects earlier decisions, not just the original prompt.

What are the types of LLM agents? 

LLM agents differ in design and behavior depending on how they are applied within a task.

The following are key types commonly used in real-world applications.

Task-specific

A task-specific agent is designed for a narrow function. It might handle customer service tickets by pulling relevant case notes and preparing draft replies. The agent works within a structured process and does not shift from its assigned purpose, even when input varies.

Autonomous

Autonomous agents continue operating without waiting for human input after each step. In finance, one might investigate a flagged account and collect supporting data before taking the next action. Its decisions depend on previous actions rather than predefined sequences or prompts.

Tool-using

Some agents work by connecting to external systems. For example, an agent built for order fulfilment may pull data from inventory software or place an internal delivery request. The ability to use tools expands what the agent can act on beyond text alone.

Multi-agent

A multi-agent system involves more than one LLM agent contributing to a shared task. One agent might extract relevant facts while another formats the result for a report. Each one plays a different role, but they interact as the task progresses.

What are some common LLM agent use cases?

LLM agents are used in applied settings where a process unfolds across multiple steps, often structured as agentic AI workflows, with each decision informed by earlier actions or changing input.

The examples below reflect how they operate inside real-world enterprise systems.

Clinical decision support

Healthcare providers are increasingly adopting LLM agents to streamline clinical documentation through workflow automation. These agents transcribe spoken doctor-patient interactions into structured content, reducing the time clinicians spend on administrative tasks. They organize this information into notes formatted for electronic health records. Clinicians review draft notes and make necessary adjustments instead of entering data manually. Using agents reduces administrative burden and frees clinicians to engage more deeply with patients. 

Personalized product recommendations

In retail, LLM agents adjust recommendations based on a customer’s recent browsing and preferences, considering which products are currently available. An agent might respond to a shopper’s browsing path by selecting items that match previous views and remain available in nearby stock. It can update the displayed results when filters change or inventory shifts mid-session. Recommendations stay current throughout the session as the system avoids interruptions from refreshes or resets.

Automated risk assessment

Financial institutions apply LLM agents during case review for account applications or loan approvals. After collecting form data, an agent might retrieve internal policy references and check for missing fields that delay processing. If the applicant’s record falls outside standard parameters, the agent can mark it for manual review and prepare a case summary that links flagged sections to the relevant policy. As a result, compliance teams spend less time on routine checks and focus on the applications that require escalation.

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