What Are LLM Agents? The 4 Components That Take You From POC to Production

Large language models can answer questions. LLM agents — also called AI agents — can do things.

That distinction matters more than it sounds. A language model responds to a prompt and stops. An LLM agent takes a goal, breaks it into steps, uses external tools to gather information and execute tasks, checks its own progress, and keeps going until the job is done.

This is why LLM agents have moved from research curiosity to serious enterprise investment in under two years. LLM based agents are the first AI systems capable of handling multi-step business processes end to end — without a human shepherding every decision.

But there's a gap that most teams don't see until they're standing in it: the POC works. The production system doesn't. Not because the model got worse. Because the POC only tested one of the four components that a real LLM agent requires.

This guide explains all four LLM agent components — what they are, how they fit together, and what each one demands when you move beyond the demo.

What Are LLM Agents?

An LLM agent is a system that uses a large language model as its reasoning core, then wraps it with the ability to take actions in external environments.

A standard LLM sits inside a request-response loop. You send a prompt, it generates text, it stops. LLM agents break that loop. They can make API calls, query databases, run code, search the web, and call other agents — then use the results of those actions to decide what to do next.

LLM agents are capable of processing natural language instructions and translating them into structured actions against external systems. This natural language understanding is what makes LLM based agents accessible to non-technical users — you describe the goal in plain language, and the agent figures out how to pursue it.

Here's how LLM agents differ from standard large language models: the defining characteristic is autonomy over time. LLM agents don't just answer questions. They pursue goals across multiple steps, adapting based on what they find. An LLM agent tasked with analyzing stock market trends doesn't just describe what trends are — it retrieves current data, runs analysis, identifies patterns, and synthesizes findings, making dozens of intermediate decisions along the way.

This is what makes AI agents different from prompt-chained pipelines. A pipeline follows a fixed sequence. An LLM agent reasons about what to do next.

How LLM Agents Differ from Language Models

Understanding how LLM agents differ from the large language models underneath them is essential before looking at the architecture.

Language models are stateless. They have no memory between calls, no ability to take actions, and no way to interact with external systems. Every prompt starts fresh. Every response ends the interaction.

LLM based agents change all three of these properties. They maintain state across steps. They interact with external tools and systems. And they operate over extended time horizons — an LLM agent's workflow may span dozens of tool calls, multiple reasoning steps, and minutes or hours of wall-clock time.

The model is still doing the reasoning. But the agent architecture is what allows that reasoning to accomplish something in the real world. Natural language is the interface; action is the output.

The LLM Agent's Workflow

Before breaking down the core components, it helps to understand the loop that governs an LLM agent's workflow:

Perceive — The LLM agent receives input: a natural language instruction, a task specification, or the result of a previous tool call.

Plan — The agent reasons about what to do next. What relevant data do I need? Which external tools should I use? This happens inside the language model using the current context and anything retrieved from memory.

Execute — The LLM agent makes tool calls, runs code, retrieves data, or calls multiple agents. Tool use at this stage is what separates agents from plain language models.

Observe — The agent receives results and evaluates them. Did the tool call succeed? Does the plan need to change?

Remember — Relevant information is stored for future steps — updating the agent's internal logs, writing to memory, or passing state forward.

This is the LLM agent's workflow in every implementation, regardless of framework or use case. The four LLM agent components are what make each stage work reliably.

The 4 Core LLM Agent Components

Every LLM agent — regardless of what it's built to do — shares the same four core components. Understanding them is the difference between building an agent that works in a demo and one that works in production.

1. The Agent Core

The agent core is the language model itself — the reasoning engine that interprets goals, plans steps, evaluates results, and decides what to do next. Every other LLM agent component serves the core: providing it with context, giving it capabilities, and storing what it learns.

The core communicates through a system prompt — a persistent set of instructions that defines the LLM agent's role, constraints, and available tools. Prompt engineering at the system level is one of the highest-leverage investments in LLM agent development.

The agent core is the component that POCs get right almost by default. You pick a capable model, write a good system prompt, and it reasons well in natural language. This is why the POC impresses. It's also why teams underinvest in the other three LLM agent components — the core makes everything look easy.

2. The Memory Module

Memory is where most LLM agent implementations fall short, because there are two distinct types — and a POC usually only tests one.

Short term memory is the context window: everything the LLM agent can see in a single inference call. This includes the current conversation, recent tool use results, and retrieved context. Short term memory is fast and immediately accessible, but it's finite and ephemeral.

Long term memory is everything the LLM agent needs across sessions: past conversations, user preferences, historical data from previous runs, internal logs of what the agent tried and what worked. Long term memory requires external storage — a database the LLM agent can write to and query against.

This is the LLM agent component that breaks first in production. POCs run short sessions with clean inputs. Production means users returning after days, LLM agents handling hundreds of simultaneous sessions, and complex tasks that span hours. Without long term memory, every session starts from zero and past conversations are permanently lost.

Data retrieval from long term memory is also not a simple lookup. Deciding which relevant data and past conversations matter for the current task is a genuine information retrieval problem. LLM agents that get this wrong lose context at exactly the moments they need it most.

3. External Tools and Tool Use

External tools are what give LLM agents the ability to act on the world rather than just reason about it. Tool use is the mechanism that separates LLM based agents from glorified chatbots. An LLM agent with the right external tools can automate complex tasks that couldn't be automated before.

Common external tools for LLM agents include:

APIs — fetching relevant data, triggering workflows in external systems, sending messages. Code interpreter — writing and executing code, running unit tests, performing data analysis. Database queries — retrieving relevant data from structured sources. Web search — accessing information beyond the model's training data. Multiple agents — in multi-agent systems, one LLM agent calls another as a tool.

Function calling is the mechanism that makes tool use work. The language model outputs a structured specification of which tool to call and with what arguments. The system executes the call and feeds the result back. Modern large language models handle function calling reliably when external tools are well-defined.

Tool use in production is harder than in the POC. More external tools means more edge cases. Function calling produces unexpected arguments on inputs the tool wasn't designed for. LLM agents break in ways that are hard to debug because the failure happens several steps into the workflow.

Model Context Protocol (MCP) is the emerging standard for how LLM agents connect to external tools and data sources — a common interface that makes it possible to build tool use once and reuse it across any MCP-compatible agent.

4. Task Planning

Task planning is the LLM agent's ability to decompose a high-level natural language goal into executable steps. Given "generate project plans for Q2," a well-designed LLM agent doesn't attempt it in one shot — it identifies what relevant data it needs, retrieves it, structures the work, drafts components, and assembles the output.

Planning quality depends on how well the other LLM agent components are working. The agent core has to reason about which external tools to call. The memory module has to surface relevant data for each planning step. The tools have to execute reliably enough that the plan can proceed. A weakness in any component degrades planning quality across all complex tasks.

Some LLM agents use explicit planning — the model produces a written plan before executing. Others use a reactive loop: act, observe the result, decide the next action. The right approach depends on the structure and predictability of the task.

Types of LLM Agents

Understanding the four LLM agent components makes it easier to see why different types of LLM agents exist and where each excels.

Task-specific LLM agents handle one category of complex tasks well — code review, customer support, data extraction. They have a narrow set of external tools, optimized system prompts, and well-defined inputs and outputs. These are the easiest type of LLM agent to build and test reliably.

Conversational LLM agents process natural language and maintain state across a dialogue, using past conversations to inform current responses. Long term memory is critical here — without it, LLM based agents can't apply context from past conversations or remember user preferences.

Autonomous agents capable of end-to-end execution operate with minimal human oversight. Given a goal and external tools, autonomous agents capable of multi-step reasoning execute a full workflow — planning, tool use, intermediate decisions, error handling — and return a result. These LLM agents are the most capable and the hardest to make reliable.

Multi-agent systems distribute complex tasks across multiple specialized LLM agents that collaborate. One LLM agent handles data retrieval, another analysis, another synthesis. Multi-agent systems solve problems that single LLM based agents can't handle — but multiple agents working together need shared state and reliable handoffs, which is an infrastructure problem as much as an AI one.

Choosing the right type of LLM agent for a given problem is itself a design decision. Task-specific LLM agents are easier to test and cheaper to run. Autonomous agents capable of handling open-ended goals are more powerful but require more robust infrastructure underneath them.

LLM Agent Challenges

The gap between a demo agent and a production agent is large. These are the LLM agent challenges teams consistently underestimate:

Memory and context management. Short term memory fills up fast on complex tasks. Long term memory retrieval — deciding which relevant data and past conversations matter right now — is a hard information retrieval problem. LLM agents that can't manage context lose track of complex tasks mid-execution.

Data freshness. Training data has a cutoff. LLM based agents answering questions about current events or system state need access to live relevant data through tool use or database queries. Agents that rely only on what the model knew at training time will confidently give wrong answers about anything that's changed.

Natural language ambiguity. LLM agents accept natural language instructions, which means they must handle ambiguous, underspecified, or contradictory goals. Robust LLM agent implementations clarify ambiguity before executing, not after spending compute on the wrong complex tasks.

Tool use reliability. External tools fail in production. APIs time out. Function calling produces malformed arguments on inputs the tool wasn't designed for. LLM agents break in ways that are hard to debug because the failure happens several steps into the LLM agent's workflow.

Coordination across multiple agents. When multiple agents work together, they need to share state reliably. If one LLM agent's output is another's input, the handoff needs to be structured and queryable. Ad-hoc coordination breaks at scale.

Cost and latency. LLM agents that execute complex tasks with many tool use steps accumulate latency and API costs at every step. Multi-agent systems multiply this across every agent interaction. Cost optimization requires deliberate decisions about which external tools to call and when.

The Infrastructure Behind LLM Agents That Scale

Here's what most LLM agent tutorials skip: LLM agents are also a data infrastructure problem.

The memory module needs somewhere to store and retrieve relevant data. Tool use returns information that needs to be persisted and queried. Multiple agents need shared, consistent state. Users expect LLM agents to remember past conversations and user preferences — which means every LLM agent interaction involves reads and writes against a database.

LLM based agents in the POC get away without solving this because they run short sessions, clean inputs, and one user at a time. Production doesn't. Production means concurrent sessions, long-running complex tasks, and LLM agents that need to retrieve relevant data from hundreds of past interactions.

Teams that close the POC-to-production gap fastest treat the data layer as a first-class LLM agent component from the start — not an integration task to figure out after the model is working. Natural language is the interface. The database is what makes it reliable.