What is an LLM agent and when did these start to emerge?

What is an LLM agent?

LLM agents are increasingly common in AI discussions, yet their definition is still evolving. At the most basic level, an LLM agent is an AI system built on a large language model that can take action on behalf of a user—not just respond to questions. These agents can follow instructions across multiple steps, select actions based on defined reasoning processes, and complete tasks with little to no ongoing supervision.

This makes them distinct from basic chatbots, which typically answer prompts in a single exchange. LLM agents can maintain context, use external tools, and work toward a defined goal over a sustained interaction. In practice, they can:

• Use external tools to perform calculations, search the web, or interact with APIs
• Access and process outside data from databases, documents, or live feeds
• Follow multi-step processes to reach a target outcome
• Persist information across interactions for continuity

The growing interest in LLM agents stems from a broader shift in AI’s role. Early large language models primarily generated text based on prompts. As their reasoning capabilities improved, developers began using them as active problem-solvers.

Where did LLM agents come from?

LLM agents emerged as researchers and developers looked for ways to make language models useful beyond simple text output. The concept gained momentum in late 2022 and throughout 2023, when breakthroughs in connecting models to external tools and APIs opened up new automation possibilities.

Early agents focused on executing single tasks with a connected tool. Over time, they evolved into systems that can reason, plan, and coordinate more complex workflows with reduced human intervention. This expansion transformed them from experimental projects into practical business tools capable of delivering measurable time savings and operational improvements.

How does an LLM agent work, and how is it used today?

LLM agents combine the reasoning ability of a language model with the ability to act—using tools, gathering data, and iterating toward a result. A typical workflow looks like this:

1. Goal input: The user describes an objective in natural language
2. Task breakdown: The agent divides the goal into smaller, manageable steps
3. Tool selection and execution: It chooses which tools to use and carries out the steps
4. Evaluation: The agent checks results, makes adjustments, and continues until the task is complete

Most LLM agents are built from a set of foundational components:

• A base language model for reasoning and interpretation
• A memory system for retaining relevant context over time
• A planning module to determine the sequence of actions
• Tool integrations such as search engines, calculators, or database connectors
• Feedback mechanisms to improve output over repeated cycles

Modern frameworks make assembling these components easier, so teams can adapt agents to specific needs. For example, a marketing department might deploy an LLM agent to analyze customer sentiment by gathering data from social media, reviews, and support tickets. The agent could summarize key patterns, flag urgent issues, and recommend draft responses aligned with the brand’s tone.

Do LLM agents matter?

LLM agents allow organizations to automate cognitive tasks that previously required dedicated human effort. They can rapidly process large datasets, identify trends, and recommend actions—all with consistency and speed that scales.

For businesses, the benefits often include:

• Scaling operations without adding headcount
• Reducing costs tied to repetitive analysis or research tasks
• Maintaining consistency across outputs and processes

By offloading the repeatable and time-intensive work, teams can focus on strategic decisions, creative problem-solving, and relationship-building.

TL;DR

LLM agents combine the reasoning skills of large language models with the ability to use tools, work across multiple steps, and execute tasks toward a goal with minimal human input. They’re especially valuable for scaling analytical work, automating complex processes, and freeing human teams to focus on higher-value activities.

Even if you don’t plan to build one yourself, understanding what they can do makes it easier to spot opportunities for automation and better allocate resources.