How LLM Agents Work
AdvancedAn LLM agent is a language model given tools and a loop so it can act, not just answer. On its own, an LLM only produces text. Turn it into an agent by letting it call tools — search, code execution, APIs, databases — and wrapping it in a reason-act-observe loop: the model decides what to do, the system runs the chosen tool, feeds the result back, and the model decides the next step until the goal is complete. Add memory and planning and it can tackle multi-step tasks autonomously — powerful, but requiring careful guardrails.
Think of a capable intern with a phone and a to-do list
A plain LLM is like an intern who can only answer from what they already know. An agent is that intern given a phone, a computer, and a task: "book the venue." Now they can look things up, call vendors, check a calendar, and decide the next action based on what they learn — repeating until the job is done. They are far more useful, but you also need clear boundaries on what they are allowed to do, because now they can take real actions in the world.
Step by Step
Key Concepts
Tool Use / Function Calling
The mechanism by which an LLM requests an external action — search, code, an API call — by emitting a structured call the system executes. Tools are what let an agent affect the world.
Reason-Act-Observe Loop
The agent core cycle: the model reasons about the next step, acts via a tool, observes the result, and repeats. This loop is what turns one-shot answering into iterative problem-solving.
Planning
Decomposing a goal into ordered sub-tasks before or during execution, so the agent can tackle complex work systematically rather than reacting one step at a time.
Memory
Short-term (the running context) and long-term (external stores the agent can read/write) memory that let it remember earlier steps, facts, and results across a long task or between sessions.
Key Facts
- The difference between a chatbot and an agent is tools plus a loop: the ability to take actions and iterate based on their results, not just produce one answer.
- Reliability is the hard part — agents can loop, misuse tools, or make mistakes, so step limits, validation, and human-in-the-loop approval for risky actions are essential.
- Multi-agent systems split work among specialised agents (a planner, a coder, a reviewer) that collaborate, which can improve results on complex tasks.
Real-World Applications
A coding agent
An agent given file-editing, test-running, and shell tools can implement a feature: it reads code, writes changes, runs tests, observes failures, and fixes them in a loop until the tests pass.
A research or ops assistant
An agent with search and API tools gathers information across sources, or an operations agent queries systems and takes remediation steps within tightly scoped permissions and human approval for anything risky.
Frequently Asked Questions
What is an LLM agent?
An LLM agent is a large language model equipped with tools and wrapped in a control loop so it can take actions to accomplish a goal, rather than just producing a single text answer. The model can call tools — like web search, code execution, or APIs — and then reason over the results to decide its next action, repeating until the task is complete. This turns a passive text generator into a system that can plan, act, and iterate autonomously.
How does an LLM agent use tools?
Through function calling. You describe the available tools and their inputs to the model, and instead of answering directly, the model can emit a structured request to invoke a specific tool with specific arguments. The surrounding system executes that tool, captures the result, and feeds it back into the model context as an observation. The model then decides the next step based on that result — this is how the agent interacts with the outside world.
What is the reason-act-observe loop?
It is the core cycle of an agent. The model reasons about what to do next given the goal and what it knows so far, acts by choosing and calling a tool, and then observes the tool result, which is added to its context. It repeats this loop — reasoning, acting, observing — building toward the goal until it decides the task is done or a stopping limit is reached. This iterative loop is what distinguishes an agent from a single-turn chatbot.
What are the risks of LLM agents and how do you control them?
Because agents take real actions, they can loop indefinitely, misuse tools, act on incorrect reasoning, or cause unintended side effects. Controls include scoping each tool permissions tightly (least privilege), requiring human approval for risky or irreversible actions, capping the number of steps, validating tool inputs and outputs, and monitoring the agent behaviour. Reliability and safety, not raw capability, are usually the hardest part of building production agents.