AI Agents Explained: What They Are and How They Work (2025)
Plain-English explainer — what an AI agent is (LLM + tools + action loop vs. chatbot), the ReAct loop (Reason+Act: Thought—>Action—>Observation), types, real production examples, frameworks, and what makes agents fail today.
1. What is an AI agent?
An AI agent is an LLM (like GPT-4 or Claude) combined with three things that turn it from a text generator into an autonomous worker:
Tools
Functions the LLM can call — web search, code execution, sending emails, querying databases, reading files. Each tool has a name, description, and typed parameters. The LLM decides when and how to call them.
A loop
The ability to take an action, see the result, then decide the next action — rather than generating one-shot output. The loop keeps running until the goal is complete or the agent gives up.
Memory
Access to past context, previous tool results, or stored information. Without memory, every loop iteration would start from scratch — memory lets the agent build on what it already learned.
Chatbot vs. AI agent
- You ask a question
- It generates a text answer
- Done — one round trip
Example: “What are the steps to deploy a Next.js app?” — returns a numbered list.
- You give a goal
- It plans steps and executes tools
- Reads results, adjusts, repeats
- Stops when goal is complete
Example: “Deploy my Next.js app” — runs npm build, reads errors, fixes them, pushes to Vercel.
2. How AI agents work — the ReAct loop
Most agents follow the “ReAct” pattern (Reason + Act). The LLM alternates between thinking and doing:
Thought
“To answer this, I need to search for current information.”
Action
Calls search tool with a query. The system executes it and returns results.
Observation
Reads the search results. Feeds them back into the LLM context.
Thought
“I found X. Now I need to check Y to complete the answer.”
Repeat
Calls another tool, reads the result, adjusts — until the agent has enough to respond with a final answer.
This loop runs entirely inside the LLM — the model generates structured output (like “call tool: search”) and the system executes the tool call and feeds the result back. Here is a minimal Python skeleton of how it works:
while not done:
# LLM decides what to do
response = llm.complete(system + history + tools)
if response.is_tool_call:
# Execute the tool
result = tools[response.tool_name](**response.tool_args)
history.append(result) # feed result back
else:
# Final answer
done = True
return response.text Real frameworks abstract this loop for you — but every agent framework is essentially this pattern under the hood.
3. Types of AI agents
Not all agents are the same. The architecture you use depends on how complex the task is and whether specialized roles are needed.
Single-agent (ReAct)
One LLM in a loop with a set of tools. The simplest and most common pattern.
Example: “Find the top 5 competitors of this company, their pricing, and key differentiators” — agent searches, reads pages, compiles table.
Multi-agent systems
Multiple specialized agents working together, each with a specific role. An orchestrator agent decides which specialist to call.
Frameworks: LangGraph, CrewAI, AutoGen, Microsoft Magentic-One.
Browser / computer agents
Agent controls a real browser or computer to complete tasks — takes screenshots, identifies UI elements, clicks buttons, fills forms, reads page content.
Coding agents
Agents that write code, run it, read the output, fix bugs, and iterate. They can generate an entire feature across multiple files, run tests, and fix failures — autonomously.
4. Real examples of AI agents in production
These are widely used, real products — not research demos.
Cursor Composer / Windsurf Cascade — coding agents
Read your codebase —> write new code —> run terminal commands —> fix errors —> verify tests. Used by millions of developers for feature implementation. The agent reads diffs, proposes changes, and iterates when builds fail.
Claude Computer Use — computer-controlling agent
Takes screenshots —> identifies UI elements —> clicks, types, navigates. Can fill out forms, do research in a browser, operate any desktop app. Anthropic's experimental feature — available via the API with the computer_use_20241022 tool.
Devin (Cognition AI) — autonomous software engineer
Given a GitHub issue —> plans implementation —> writes code —> submits PR —> responds to code review feedback. $500/month enterprise tool for engineering teams. Full VM access, terminal, browser — the most complete coding agent in production.
n8n AI Agent node — workflow automation agent
Node in an n8n workflow: LLM + tools defined as other n8n nodes. “Process all new emails, categorize them, respond to urgent ones, create Jira tickets for bugs” — each capability is a tool the agent can invoke. Open-source, self-hostable.
Perplexity — research agent
Decomposes a query —> searches multiple sources —> synthesizes —> cites. One of the most widely used agent-style products for consumers. Every answer is the result of a multi-step search loop, not a single LLM call.
5. How to build a simple AI agent
Frameworks in order of simplicity — start at the top if you're new, work down for more control:
LangGraph (LangChain)
Graph-based, stateful, best for production multi-agent. Checkpointing, branching, human-in-the-loop approval steps built in.
LlamaIndex agents
Simpler, good for document retrieval agents. Best-in-class RAG defaults — great if your tools involve reading documents or knowledge bases.
CrewAI
Role-based multi-agent framework, more opinionated. Define agents with roles and goals, assign tasks — crew handles coordination.
OpenAI Assistants API
Managed agent infrastructure — threads, file retrieval, code interpreter built in. Lower infra burden, but tied to OpenAI.
Anthropic Tool Use (Claude)
Claude's native tool calling — lower level, maximum control. Define tools as JSON schemas, parse tool_use stop reason, execute, feed results back.
from langchain.agents import create_react_agent, AgentExecutor
from langchain_anthropic import ChatAnthropic
from langchain_community.tools import DuckDuckGoSearchRun
llm = ChatAnthropic(model="claude-3-5-sonnet-20241022")
tools = [DuckDuckGoSearchRun()]
agent = create_react_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools)
result = executor.invoke({"input": "What is the current state of AI agents in 2025?"}) This spins up a Claude-powered agent with DuckDuckGo search as its only tool. Add more tools as functions; the agent decides when to call each one.
6. Limitations of current AI agents
Agents are powerful but the field is still maturing. Know these limitations before deploying autonomously.
Reliability — agents fail non-deterministically
The same goal may succeed on one run and fail on the next. Small prompt variations, tool output differences, or context window effects can change behavior unpredictably.
Long-horizon task coherence
Agents lose coherence on very long tasks (typically more than 20—30 steps). They can drift from the original goal, forget earlier constraints, or get stuck in loops.
Tool errors cascade
One tool failure can derail the entire chain. If the agent's search returns bad results, every subsequent reasoning step is built on a wrong foundation.
Cost — each step is an LLM call
Each agent step requires one or more LLM API calls. Complex tasks with 10—20 tool calls can cost $0.50—$5+ per run at current prices. Set max-iteration budgets.
Context limits
Even a 200k context window has limits. Very long agent runs accumulate tool outputs, history, and reasoning that eventually overflow the context. Agents need memory management strategies for long tasks.
7. Agent safety and oversight
Autonomous agents can take real-world actions — sending emails, deleting files, making API calls, spending money. Human oversight is essential, especially early on.
Never give an agent irreversible tool access without approval steps
Sending emails, deleting files, making purchases, writing to production databases — all require a human confirmation gate before the agent can proceed.
Run agents with “human in the loop” checkpoints
Agent proposes the next action — human approves or rejects before execution. LangGraph's interrupt() node makes this easy to wire up.
Log every tool call and result
Full auditability is essential. Use LangSmith, Langfuse, or a custom log store to record every step — you need this to debug failures and detect unintended behavior.
Start with read-only tools, then add write access incrementally
Let the agent prove it understands the task before granting it the ability to change state. Search —> read —> write —> delete is the right progression of trust.
Set budget limits on LLM calls
A runaway loop or stuck agent can burn through API credits fast. Set max_iterations and max_cost_usd limits. Alert on spend, not just failures.
Monitor the AI APIs powering your agents at Prismix
Agent loops depend on stable API availability — a single OpenAI, Anthropic, or Groq outage silently breaks an entire automated workflow. Prismix detects API degradations in real time and alerts you before they cascade into agent failures.
FAQ
What is an AI agent?
An AI agent is a language model (like GPT-4 or Claude) combined with tools (web search, code execution, email, database access) and a decision loop: the model takes an action, observes the result, and decides the next action — repeating until a goal is complete. Unlike a chatbot that generates one response, an agent can complete multi-step tasks autonomously.
How are AI agents different from chatbots?
Chatbots respond to questions with text. AI agents execute multi-step tasks using tools — searching the web, writing and running code, clicking buttons in a browser, sending emails. A chatbot answers “what are the steps to deploy a Next.js app?” An agent actually deploys it.
What are examples of AI agents?
Cursor Composer and Windsurf Cascade (coding agents that write, run, and fix code), Devin (autonomous software engineer from Cognition AI), Claude Computer Use (controls a browser/desktop), Perplexity (research agent with search tools), n8n AI Agent node (workflow automation), and OpenAI's Operator (browser-controlling agent).
How do I build an AI agent?
Use a framework: LangGraph (LangChain) for stateful multi-agent workflows, LlamaIndex for document retrieval agents, CrewAI for role-based multi-agent, or the OpenAI Assistants API for managed infrastructure. The core pattern is: define tools as functions, give the LLM access to them, run a loop where the LLM calls tools and reads results until the goal is complete.