What Are AI Agents and How Do They Work?
AI agents can plan, use tools, and take action autonomously. Here's what they are, how they work under the hood, and what separates useful agents from overhyped demos.
A chatbot answers questions. An agent does things.
Ask ChatGPT to write an email and it generates text. An agent writes the email, sends it, checks for a reply, and follows up if there’s no response within 24 hours. That’s the difference. Agents don’t just generate text. They take action, observe results, and decide what to do next.
What Makes Something an “Agent”
An AI agent is a system where a language model decides what actions to take, executes those actions using external tools, observes the results, and iterates until a task is complete. Four components make this work:
A language model that acts as the reasoning engine. It interprets the task, decides what to do next, and reasons about results. The model doesn’t just answer a question. It generates a plan.
Tools that the agent can call. These might be APIs, databases, web browsers, code interpreters, file systems, or other software. The model doesn’t browse the web itself. It decides to call a browsing tool, receives the results, and reasons about what to do next.
A loop. Unlike a single prompt-response exchange, agents run in a cycle: think, act, observe, repeat. The model generates a plan, takes an action, sees what happened, and decides the next step. This continues until the task is done or the agent determines it can’t proceed.
Memory to track what’s been tried and what worked. Without memory, agents repeat themselves, get stuck in loops, or lose track of progress on multi-step tasks.
How the Loop Actually Works
The core agent loop is surprisingly simple conceptually. You send the model a message that includes: the task, the available tools (described in a structured format the model understands), and any results from previous actions. The model responds with either a final answer or a tool call. If it’s a tool call, you execute it, feed the result back, and repeat.
The tool descriptions matter enormously. The model decides which tool to use based on its description. A vague description like “search function” produces worse tool selection than “Searches the company knowledge base by semantic similarity. Input: a natural language query. Returns: the 5 most relevant document excerpts with source URLs.” The model is pattern-matching your tool descriptions against the task, so precision in the description directly translates to better decisions.
The ReAct Pattern
The dominant agent architecture is ReAct (Reasoning + Acting), introduced by Yao et al. in 2022. The model alternates between reasoning steps (thinking about what to do) and action steps (calling tools).
A ReAct trace looks something like:
Thought: The user wants to know the current stock price of Apple. I should use the financial data tool.
Action: get_stock_price(ticker="AAPL")
Observation: AAPL is currently trading at $187.42, up 1.3% today.
Thought: I have the current price. The user also asked about the 52-week range, which I don't have yet.
Action: get_stock_history(ticker="AAPL", period="52w")
Observation: 52-week low: $143.29, 52-week high: $199.62
Thought: I now have all the information needed to answer.
Answer: Apple (AAPL) is currently trading at $187.42...The key insight is that the reasoning steps aren’t decoration. They actually improve the model’s decision-making. When the model “thinks out loud” before acting, it makes better tool choices and catches its own errors. Without explicit reasoning steps, agents are more likely to call the wrong tool or misinterpret results.
Function Calling: How Models Use Tools
Modern LLMs support “function calling” natively. You define tools as structured schemas (name, description, parameters with types), and the model returns structured tool calls instead of plain text. The model doesn’t execute anything. It outputs a JSON object saying “I want to call this function with these arguments.” Your code executes the function and feeds the result back.
This is a critical architectural boundary. The model decides. Your code executes. The model never has direct access to your systems. It can only interact through the tools you expose. This gives you a control layer: you can validate arguments before execution, rate-limit dangerous operations, log everything, and reject calls that don’t make sense.
Where Agents Actually Work Today
Agents are most useful when tasks are:
Multi-step but well-defined. Research a topic across multiple sources, synthesize, and summarize. Process an inbox and draft responses. Run a series of data transformations where each step depends on the result of the previous one.
Repetitive with variations. The same workflow, but the details change each time. Customer onboarding (same steps, different customer data), report generation (same structure, different metrics), code review (same checklist, different code).
Tool-heavy. The value comes from connecting multiple tools and services, not from the model’s knowledge alone. An agent that can query a database, call an API, write to a file, and send a notification is doing something no single tool can do.
Where Agents Struggle
Error Compounding
Each step in an agent loop introduces some probability of error. If each step has a 95% chance of being correct, after 10 steps you’re at 0.95^10 = 60% overall accuracy. After 20 steps, it’s 36%. This is why long autonomous chains are unreliable. The most effective agent systems keep loops short (3-7 steps) or include human checkpoints at key decision points.
Open-Ended Goals
“Make my business more profitable” has too many possible paths. Agents need goals that are clear enough to evaluate progress. “Find the three cheapest flights from London to Tokyo in April” is a good agent task. “Optimize our marketing strategy” is not, at least not as a single agent run. Complex goals need to be decomposed into specific, evaluable subtasks.
Cost
Every step in the loop is an LLM call. A 15-step agent execution with GPT-4o can cost $0.50-2.00 per run. At 1,000 runs per day, that’s $500-2,000 daily just for the reasoning layer. Production agent systems often use a tiered approach: a cheap, fast model (GPT-4o-mini, Claude 3.5 Haiku) for routine steps, and an expensive model only for steps that require complex reasoning.
Reliability and Debugging
When an agent produces a wrong result, debugging is hard. Was the tool call wrong? Was the result misinterpreted? Did the model hallucinate a fact that led it down the wrong path? Agent traces (the full sequence of thoughts, actions, and observations) are essential for debugging. Log everything. Without traces, you’re debugging a black box.
Multi-Agent Systems
For complex tasks, multiple specialized agents can collaborate. Each agent has a narrow role, its own tools, and its own system prompt. One agent researches, another analyzes, a third writes. The output of one becomes the input of the next.
This works better than a single agent for the same reason microservices work better than monoliths for complex systems: each component is simpler, easier to test, and easier to debug. A research agent that’s great at finding information doesn’t need to also be great at writing reports.
The hard part is coordination. How do agents pass information? How do you handle failures? What happens when Agent 2 needs information that Agent 1 didn’t collect? These are system design problems, not AI problems, and they’re where most multi-agent projects actually get stuck.
Frameworks vs. Building Your Own
Frameworks like LangChain, CrewAI, and AutoGen provide abstractions for building agents. They handle the tool call loop, memory management, and multi-agent coordination. They’re useful for prototyping.
But they also add complexity, abstraction layers that make debugging harder, and opinions about architecture that might not match your needs. The core agent loop is simple enough to implement yourself in under 100 lines. If you understand the loop, you can build exactly what you need without framework overhead. If you don’t understand the loop, a framework won’t save you.
Start by building a simple agent from scratch. Once you understand the mechanics, evaluate whether a framework saves you time or just adds dependencies.
What Actually Matters
Agents are powerful, but they’re not magic. The people building useful agent systems understand the underlying mechanics: how LLMs make decisions, why tool descriptions matter, how to design loops that converge instead of spiral, and when a simpler approach (a well-crafted prompt, a deterministic script, a human in the loop) is actually the better solution.
Chapter 6 of Get Insanely Good at AI covers agent architectures in depth, from single-agent tool use to multi-agent coordination, with practical patterns for building systems that work reliably in production.