LLM agents explained: a complete guide

An LLM agent is a system that uses a large language model to decide what to do next, calls tools such as APIs, databases or search to act, reads the results and repeats until it reaches a goal. A chatbot answers one message at a time; an agent plans multi-step work, uses your systems and data, and keeps track of context. In enterprises, LLM agents now resolve customer requests, prepare back-office cases, research questions across documents and review files, with guardrails and human approvals around the steps that carry risk.

This guide covers how LLM agents work, their components and types, where they are used, the challenges, and the frameworks for building them.

What is an LLM agent?

An LLM agent combines three things: a language model that reasons in natural language, tools that let it act on the world, and a loop that feeds each result back to the model until the task is done. The model supplies judgment, such as understanding a request, choosing a tool or deciding an answer is good enough; the tools supply reach.

The difference from a plain LLM is action. Ask a model about a customer's order and it can only guess. Give an agent an order lookup tool and it checks the order, notices a delayed shipment, looks up the carrier status and tells the customer what happened.

How does an LLM agent work?

Most LLM agents follow the same loop, often called reason, act, observe:

1. Receive a goal. A user message, an event or a scheduled trigger starts the run.
2. Gather context. The agent loads its instructions, relevant memory and, if needed, retrieved documents.
3. Reason. The model decides the next step: answer directly, call a tool or ask a clarifying question.
4. Act. The agent calls the chosen tool with structured arguments, for example an API request or a database query.
5. Observe. It reads the tool's result or error and adds it to its context.
6. Repeat or finish. It continues until the goal is met, a step limit is reached, or a person must decide.

Modern models support tool calling natively: they return structured requests to call a function, which the runtime executes. Many tools are now exposed through the Model Context Protocol, an open standard for connecting agents to tools and data sources.

What are the key components of an LLM agent?

What types of LLM agents are there?

Most production systems mix types. A support workflow might pair a conversational agent with a knowledge agent and a fixed approval step. Our guide to multi-agent systems covers coordination patterns.

What are LLM agents used for?

• Customer service. Resolving routine requests and handing complex cases to a person with the full context. An Asian neo-bank automated about 85% of support inquiries with a single agent that uses tools, memory and a knowledge base.
• Back-office operations. Matching, reconciling and preparing exceptions for approval in finance and operations teams.
• Document review. Extracting and checking fields in loan files, claims and contracts, and flagging what does not match policy.
• Compliance. Gathering evidence and drafting KYC and AML case summaries for analysts to decide.
• Research and knowledge. Answering questions across internal documents with citations and permission-aware retrieval.
• Software engineering and data analysis. Writing code, tests and analyses that people review.

What are the main challenges with LLM agents?

• Reliability. Agents can misread a request, pick the wrong tool or stop too early. Narrow scopes, clear tool descriptions and step limits help.
• Security. Prompt injection hidden in emails, documents or web pages can try to redirect an agent, and an agent with broad permissions can do real damage. Least-privilege tools, input detectors and approvals on consequential actions contain the risk.
• Evaluation. Answers vary between runs, so quality must be measured on test sets and on samples of live traffic, not judged from a demo.
• Cost and latency. Each loop is a model call. Routing simple steps to smaller models and capping loops keeps cost per task predictable.
• Memory. Deciding what to remember, for how long and for whom is a design and privacy decision, not just a storage choice.
• Governance. Regulated teams need to show what an agent read, did and who approved it, which requires traces and versioned configurations.

Which frameworks are used to build LLM agents?

Frameworks handle the loop, tool calling, memory and orchestration so teams write business logic instead. Widely used options include:

A framework gives you code. Production also needs deployment, access control, guardrails, evaluations and tracing, which is where a platform comes in. Our comparisons show how Dynamiq differs from common frameworks and platforms.

How do you build an LLM agent?

1. Define one narrow job and how you will measure success.
2. Choose a model and keep it swappable.
3. Write specific instructions, including what the agent must never do.
4. Give it the tools and knowledge the job needs, read-only first.
5. Add memory scoped to the user and session.
6. Add guardrails and require approval before irreversible actions.
7. Test on real cases, then deploy with tracing and ongoing evaluation.

Our step-by-step guide to creating an AI agent walks through each step with working Python code.

How does Dynamiq help teams build LLM agents?

Agent Builder lets business teams design agents on a visual canvas and engineers build them with the open-source Python SDK, both on one engine. Agents get tools from a large integrations catalog, knowledge bases with permission-aware retrieval, session memory scoped by user and session, and approvals with editable fields on any tool step. Guardrails detect PII and prompt injection, evaluations run on test sets and live deployments, and every run is traced with cost and latency.

For work that does not need a custom workflow, AI Coworker is a ready-made agent with its own cloud sandbox, connectors, skills, scheduled tasks and subagents, available in the browser, Slack, Microsoft Teams and Telegram. The platform runs in Dynamiq Cloud or self-hosted in your own cloud or data center.

FAQ

What is the difference between an LLM and an LLM agent?

An LLM generates text from a prompt. An LLM agent wraps the model in a loop with tools, memory and instructions, so it can take actions, check the results and keep working until a task is done.

What is the difference between an LLM agent and a chatbot?

A chatbot responds to each message, usually from a script or a model's knowledge. An LLM agent plans multi-step work, calls tools and APIs to get real data or take actions, and can hand off to a person when a decision needs one.

Are LLM agents fully autonomous?

They can be, but most enterprise agents are not. Teams set the level of autonomy per step: the agent drafts and looks things up freely, while actions such as payments, account changes or outbound messages wait for a person's approval.

What is agent memory?

Agent memory is the context an agent keeps. Session memory holds the current conversation; long-term memory stores durable facts about a user, such as preferences, and recalls them in later sessions. Both should be scoped to the right user and governed like any other personal data.

What is the Model Context Protocol?

The Model Context Protocol (MCP) is an open standard for connecting AI agents to tools and data sources. A system that exposes an MCP server can be used by any MCP-compatible agent without a custom integration for each one.