Most of us who use LLMs know that they are probabilistic models, and their power comes from the Transformer architecture. We also know their limitations: LLMs are limited by their training data; they hallucinate and have limited capacity to interact outside their immediate environment.

This limitation matters. Imagine a powerful LLM investigating a complex case or writing a high-stakes government report. A little bit of hallucination can have serious consequences. The risk is even higher when we use LLMs in healthcare, finance, law or other safety-critical domains.

This limitation matters. Imagine a powerful LLM investigating a complex case or writing a high-stakes government report. A little bit of hallucination can have serious consequences. The risk is even higher when we use LLMs in healthcare, finance, law or other safety-critical domains.

Think of James Bond

A useful analogy can be James Bond. In  James Bond movies, James Bond is given a goal. To accomplish the goal, he is provided with all the support from MI6. He can take actions on his own with certain boundaries.
In AI Agents, LLMs are James Bond. We act as MI6, assign a goal to the LLM, and also provide support to accomplish it. We define some guardrails so that the LLM can operate within these boundaries.

Giving Tools to the LLMs

Internet Access: LLMs have a knowledge cutoff date. When training is done, it does not know anything from the future date of the training.  If it needs any information on a recent issue, access to the internet can update its knowledge. For example, “Who won the World Cup 2026?” An LLM can easily do an internet search and give you the info. Even though its training only contains outdated knowledge from 2025.
APIs: APIs allow LLMs to use services from other software. For example, if an LLM needs weather data, it can call an api to get that info. If it needs the stock price of a certain company, it can use a financial api to access market data.

Custom Functions and Tool Calling: You can write custom functions and programs for an LLM. Consider a simple math problem: 

Calculate 1 + 2 + 3+  up to 100

An LLM might do it on its own, but as LLMs are probabilistic, there is a huge chance of a mistake. Even if it can do it, it needs lots of reasoning (aka computational power) to do so. But a simple Python script can do it within a fraction of a second with a guaranteed outcome.

Memory : Access to memory helps LLM to save planning, conversation history etc. so that it does not need to start from scratch each time.

Agents Can Ask for Help
An agent doesn't always have to operate independently. During a task, an agent may encounter unknowns and need help and ask questions for clarification. AI Agents can understand when they need help and can keep humans in the loop to accomplish the task.

The LLM as the Brain of AI Agent

You can think of the LLM as the brain of AI Agents. It can plan, reason, and ask clarifying questions, but cannot directly interact with the outside environment. Tools give it something like arms and legs.

Example: AI Report Writer

"Write a report on recent weather conditions in the UK that could be published by The Guardian.”

A simple LLM might produce a report based entirely on what it already knows. An agent could take several steps.

First, it might call a weather API to obtain the latest weather data. Then it could use an internet search to find recent reporting from the BBC and other reliable sources. It might use another AI model or a fact-checking tool to verify important claims. It could also analyse recent articles from The Guardian to understand the publication's typical structure, tone, and style.

If the report needs charts, the agent could use Python to analyse the weather data and generate graphs.

Finally, the LLM could bring all of this information together and produce the report. The workflow might look something like this:

User → LLM → Search → Weather API → Fact Checking → Data Analysis → Charts → LLM → Final Report

The LLM acts as the coordinator. It determines what information it needs, which tools to use, and how to combine the results.

In conclusion, in AI Agents, the LLM provides the reasoning and decision-making capability. The tools provide the ability to interact with the world to accomplish the task.