What is an LLM Agent?
LLM agents combine language models with the ability to interact with external systems. Unlike a basic chatbot that only generates text responses, an agent can search databases, call APIs, execute code, browse websites, and perform other actions. The agent decides which actions to take based on the task and the results of previous actions.
How LLM Agents Work
An LLM agent operates in a loop. First, it receives a task or goal. The agent then reasons about what action to take next. It executes that action using one of its available tools. The agent observes the result and decides whether the task is complete or what to do next.
This cycle continues until the agent completes the task or determines it cannot proceed. The key difference from prompt chaining is that the agent decides the sequence of steps dynamically rather than following a predetermined path.
↓
Choose Action
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Execute Tool
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Observe Result
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Task Complete? No → Back to Reasoning
Task Complete? Yes → Done
Why LLM Agents Matter
LLM agents enable AI systems to handle complex tasks that require multiple steps and decision points. Instead of programming every possible path, you give the agent tools and let it figure out how to accomplish the goal.
Agents reduce the need for extensive custom code. If you need to add a new capability, you provide the agent with a new tool rather than rewriting your entire workflow. This makes AI systems more flexible and easier to extend.
Organizations use agents for tasks like research, data analysis, customer support, and automation. An agent can search through documents using RAG, analyze the results, generate a report, and save it to the right location without human intervention at each step.
Example of an LLM Agent
Consider an agent tasked with researching a competitor. Here is how it might work:
Task: "Research Acme Corp and create a summary of their recent product launches"
Agent reasoning: "I need to find recent information about Acme Corp products"
Action 1: Search the web for "Acme Corp product launches 2025"
Result 1: Found 5 relevant articles
Action 2: Read the top 3 articles and extract key information
Result 2: Identified 2 new products with launch dates and features
Action 3: Generate structured summary document
Result 3: Summary created
Task complete. The agent determined each step based on what it learned, not from a pre-programmed sequence.
Common Mistakes with LLM Agents
Giving agents too many tools at once reduces performance. The agent wastes time considering irrelevant options. Start with a small set of essential tools and expand only when needed.
Poor tool descriptions confuse agents. Each tool needs a clear description of what it does and when to use it. Vague descriptions cause the agent to misuse tools or miss opportunities to apply them.
No safety checks create risks. Agents can take unexpected actions, especially for complex tasks. Always validate critical actions before execution and set boundaries on what agents can do autonomously.
Related Concepts
LLM agents frequently use prompt chaining internally to break down complex reasoning into steps. The difference is that agents choose the chains dynamically based on the situation.
Retrieval augmented generation serves as a common tool for agents that need to access knowledge bases or documents during task execution.
Fine-tuning can improve agent performance on specific types of tasks by training the underlying model on relevant examples of tool use and decision making.