Short answer: An AI agent works by running a loop — it perceives input, reasons about what to do next using an LLM, takes an action (like calling a tool or API), observes the result, and repeats until the task is done.
If you've used ChatGPT or Claude, you've interacted with an LLM. But how do AI agents work differently from a simple chatbot? The difference is the loop. A chatbot responds once. An agent keeps going — planning, acting, observing, and adapting across multiple steps until it completes a goal autonomously.
Understanding this architecture is essential whether you're building agents, evaluating platforms like cowork.ink, or just trying to separate hype from reality. In this guide, we'll unpack the core loop, walk through every component, and show real examples of agents in action.
The perceive-reason-act loop that powers every AI agent, the five core components (perception, reasoning, memory, tools, orchestration), the ReAct pattern with a concrete example, and how different frameworks implement these ideas.
What Makes an AI Agent Different from a Chatbot?
An AI agent is a software system that autonomously performs multi-step tasks by combining an LLM with tools, memory, and a control loop. A chatbot generates one response per message. An agent generates a plan, executes it step by step, and adjusts along the way.
Here's the key distinction:
| Chatbot | AI Agent | |
|---|---|---|
| Interaction | Single turn: question → answer | Multi-step: goal → plan → actions → result |
| Tool use | None or limited | Calls APIs, databases, file systems, browsers |
| Memory | Conversation history only | Short-term + persistent long-term memory |
| Autonomy | Waits for each user message | Runs independently until task is complete |
| Error handling | Returns best-effort text | Retries, adjusts strategy, escalates |
For a deeper comparison, see our AI agents vs. chatbots breakdown.
The leap from chatbot to agent isn't about a smarter model — it's about wrapping that model in a loop with tools. That loop is the single most important concept to understand.
The Agent Loop: Perceive, Reason, Act, Observe
Every AI agent — from a simple ReAct bot to a sophisticated multi-agent system — runs some version of the same fundamental cycle:
- Perceive — gather input (user message, tool output, environment state)
- Reason — use the LLM to analyze context and decide what to do next
- Act — execute the chosen action (call a tool, generate a response, delegate)
- Observe — read the result and feed it back into the loop
The loop repeats until the agent determines the task is finished, it hits a failure condition, or it needs human input. As Braintrust's engineering team notes, many of the most successful agents in production — including Claude Code and the OpenAI Agents SDK — share this exact architecture: a while loop that makes tool calls.
In pseudocode, the canonical agent looks like this:
while not done:
context = build_context(goal, history, memory)
response = llm.generate(context, tools)
if response.is_final_answer:
done = True
else:
result = execute_tool(response.tool_call)
history.append(result)
That's it. The power isn't in any single step — it's in the iteration. Each cycle adds new information, refines the plan, and moves closer to the goal.
Without proper guardrails, an agent can loop forever — burning tokens and money. Production agents need max-iteration limits, timeout policies, and cost controls. Most frameworks default to 10-25 maximum iterations per task.
The Five Core Components of an AI Agent
While the loop is the engine, five components make up the full architecture. Think of these as the organs of the agent — each one is necessary, and they work together.
1. Perception Layer
The perception layer is the agent's interface with the outside world. It transforms raw input into structured data the reasoning engine can process.
What it handles:
- User messages (text, voice, images)
- Tool outputs (API responses, database results)
- Environment signals (file changes, webhooks, sensor data)
- System events (errors, timeouts, notifications)
In most LLM-based agents, the perception layer serializes everything into the model's context window — a sequence of text messages the LLM can read. The quality of this serialization directly affects agent performance.
2. Reasoning Engine (the LLM)
The reasoning engine is where decisions happen. In modern AI agents, this is almost always a large language model — GPT-4, Claude, Gemini, or an open-source alternative.
The LLM receives the full context (goal, conversation history, available tools, memory) and outputs one of two things:
- A tool call — "I need to search the database for X"
- A final response — "Here's the answer to your question"
The model doesn't just generate text. It selects actions from a defined set of tools, often using structured output (JSON function calls) to ensure the agent can parse and execute the decision reliably.
According to IBM's research on AI agents, this reasoning step is what separates agents from traditional automation — the LLM can handle novel situations it hasn't been explicitly programmed for.
3. Memory Systems
Memory gives agents context beyond the current conversation. Without it, every loop iteration starts from scratch.
Short-term memory is the conversation history — messages, tool calls, and results within the current session. It lives in the LLM's context window and gets cleared when the session ends.
Long-term memory persists across sessions. This includes:
- User preferences and past interactions
- Learned facts and domain knowledge
- Procedural memory (how to perform recurring tasks)
- Episodic memory (what happened in past sessions)
Long-term memory is typically stored in a vector database or structured files, retrieved via RAG (retrieval-augmented generation) when relevant context is needed.
For a deep dive, see our AI agent memory guide.
4. Tools and Actions
Tools are what give agents their power. Without tools, an agent is just a chatbot that thinks harder.
A "tool" is any function the agent can call — an API endpoint, a database query, a web search, a file operation, a code executor. The agent receives a list of tool definitions (name, description, parameters) and the LLM decides which tool to call and with what arguments.
Common tool categories:
- Information retrieval — web search, database queries, document lookup
- Computation — code execution, calculators, data transformers
- Communication — email, Slack, notifications
- System operations — file read/write, deployments, infrastructure
- External APIs — CRMs, payment systems, third-party services
Tool design is critical. As research from Braintrust shows, tool responses make up roughly 67% of total tokens in a typical agent session — far more than the system prompt (just 3.4%). Well-designed tools with clear descriptions and focused parameters dramatically improve agent accuracy.
For more on this, read our AI agent tool calling guide.
5. Orchestration Layer
The orchestration layer ties everything together. It manages the loop, routes between components, handles errors, and enforces guardrails.
Key responsibilities:
- Loop control — start, stop, max iterations, timeouts
- Context assembly — building the prompt from history, memory, and tool results
- Error recovery — retries, fallbacks, escalation to humans
- Multi-agent routing — delegating subtasks to specialized agents
- Guardrails — input/output validation, safety checks, cost limits
In simple agents, the orchestration layer is just a while loop. In production systems, it's a full framework — LangGraph, CrewAI, OpenAI Agents SDK, or a custom solution. Teams using cowork.ink get orchestration built in, with shared workspaces where multiple agents can coordinate without custom infrastructure.
The ReAct Pattern: Think, Act, Observe
The most widely used reasoning pattern in AI agents is ReAct (Reasoning + Acting). Introduced in a 2022 paper by Yao et al., it interleaves chain-of-thought reasoning with tool use.
Here's how it works in practice. Imagine you ask an agent: "What's the population of the country where the Eiffel Tower is located?"
Iteration 1:
- Thought: I need to find which country the Eiffel Tower is in. I'll search for it.
- Action:
search("Eiffel Tower location") - Observation: "The Eiffel Tower is located in Paris, France."
Iteration 2:
- Thought: The Eiffel Tower is in France. Now I need the population of France.
- Action:
search("population of France 2026") - Observation: "France has a population of approximately 68.4 million as of 2026."
Iteration 3:
- Thought: I have the answer. France, where the Eiffel Tower is located, has a population of about 68.4 million.
- Action:
final_answer("The Eiffel Tower is in France, which has a population of approximately 68.4 million.")
Each step is explicit and traceable. You can see why the agent made each decision, which makes debugging and auditing straightforward.
ReAct is the default pattern in LangChain, LangGraph, CrewAI, and most agent frameworks because it balances simplicity with power. The explicit thought-action-observation trace makes agents interpretable — you can see exactly where things went wrong when they fail.
ReAct vs. Other Reasoning Patterns
ReAct isn't the only approach. Here's how it compares:
| Pattern | How it works | Best for |
|---|---|---|
| ReAct | Interleaves reasoning and tool calls in a loop | General-purpose agents, debugging-friendly |
| ReWOO | Plans all steps upfront, then executes in batch | Latency-sensitive tasks, predictable workflows |
| CodeAct | Agent writes and executes code as its action | Data analysis, computation-heavy tasks |
| Plan-and-Execute | Creates a full plan first, then executes step by step | Complex multi-step tasks with clear subtasks |
| Reflexion | Adds self-critique after each action, learns from mistakes | Tasks requiring high accuracy and self-correction |
For a detailed breakdown of ReAct implementation, see our ReAct pattern guide.
Real-World Examples: Agents in Action
Understanding the theory is one thing — seeing agents work in practice brings it to life. Here are three concrete examples across different domains.
Example 1: Code Review Agent
A code review agent monitors pull requests and provides automated feedback.
The loop in action:
- Perceive — PR webhook triggers the agent with diff data
- Reason — LLM analyzes the code changes against style guides and best practices
- Act — posts inline comments on specific lines, flags security issues
- Observe — checks if the developer responded or pushed fixes
- Repeat — re-reviews updated code until approval criteria are met
This is exactly how AI code review works on platforms like cowork.ink — the agent doesn't just scan once and walk away. It stays in the loop, responding to changes and iterating with the developer.
Example 2: Research Agent
A research agent gathers and synthesizes information from multiple sources to answer a complex question.
The loop in action:
- Perceive — user asks: "Compare the pricing of the top 3 cloud GPU providers"
- Reason — plans a search strategy: identify top providers, then fetch pricing for each
- Act — searches the web for "top cloud GPU providers 2026"
- Observe — gets a list: AWS, Google Cloud, Lambda Labs
- Act — fetches pricing pages for each provider
- Observe — extracts pricing data
- Reason — synthesizes findings into a comparison table
- Act — returns the final structured answer
The agent might take 6-10 loop iterations to complete this task — each step building on previous observations.
Example 3: Customer Support Agent
A support agent handles incoming tickets by understanding the issue, looking up relevant information, and resolving or escalating.
The loop in action:
- Perceive — customer message: "My payment failed but I was charged"
- Reason — this is a billing issue, I need the customer's payment history
- Act — calls the billing API with the customer ID
- Observe — sees a failed transaction with a pending charge
- Reason — the charge is pending, not completed. I should initiate a refund.
- Act — calls the refund API, then generates a response to the customer
- Observe — refund confirmed
- Act — sends the resolution message to the customer
At any point, if the agent encounters an edge case it can't handle, it escalates to a human — that's the guardrail working correctly.
Agent Frameworks Compared
If you're building agents, you'll likely use a framework. Here's how the major options implement the architecture we've discussed:
| Framework | Loop Pattern | Built-in Memory | Multi-Agent | Best For |
|---|---|---|---|---|
| LangGraph | Graph-based (nodes + edges) | Checkpointing | Yes | Complex stateful workflows |
| CrewAI | Role-based agent delegation | Short + long-term | Yes (crews) | Multi-agent collaboration |
| OpenAI Agents SDK | While loop with handoffs | Context-based | Yes (handoffs) | OpenAI ecosystem projects |
| AutoGen (AG2) | Conversation-based | Per-agent context | Yes (GroupChat) | Research and prototyping |
| cowork.ink | Orchestrated workspace | Shared team memory | Yes (workspace) | Team AI collaboration |
Each framework makes different trade-offs, but they all implement the same fundamental loop. For a detailed comparison, see our agent framework comparison.
How Context Engineering Drives Agent Performance
The most impactful factor in agent quality isn't the model — it's the context you feed it. Context engineering is the practice of carefully constructing what the LLM sees at each iteration of the loop.
What goes into the context window:
- System prompt — agent persona, rules, and available tools (~3-5% of tokens)
- Conversation history — user messages and agent responses (~25-30%)
- Tool results — outputs from API calls, searches, and computations (~60-70%)
- Memory retrieval — relevant facts pulled from long-term storage (~5-10%)
That ratio is striking: tool results dominate. This means the quality of your tool outputs matters far more than a perfectly crafted system prompt. If your search tool returns garbage, no amount of prompt engineering will save the agent.
Practical tips for better context:
- Return structured, concise tool outputs — not raw API dumps
- Include only relevant fields in tool responses
- Summarize long results before feeding them back to the model
- Use memory retrieval sparingly — only pull what's relevant to the current step
For teams running multiple agents, platforms like cowork.ink handle context engineering automatically — agents in a shared workspace can access team-wide context without manual configuration.
Common Pitfalls and How to Avoid Them
Building agents that work in demos is easy. Building agents that work in production is hard. Here are the pitfalls we see most often:
1. No iteration limit. Without a max-step cap, agents can spiral into expensive infinite loops. Always set a hard limit (10-25 iterations for most tasks).
2. Vague tool descriptions. The LLM picks tools based on their descriptions. If your tool description says "gets data," the model can't distinguish it from ten other data tools. Be specific: "Retrieves the current account balance for a given user ID."
3. Ignoring tool output quality. Agents inherit the quality of their tools. If your search API returns noisy, irrelevant results, the agent's reasoning degrades. Invest in tool output formatting.
4. No human escalation path. Agents will encounter situations they can't handle. Without an escalation mechanism, they hallucinate solutions instead of asking for help. Build an explicit handoff to humans.
5. Skipping evaluation. You can't improve what you don't measure. Set up automated evaluation that tests the agent against real scenarios. Track success rate, cost per task, and average loop iterations.
From Theory to Practice: Getting Started
Now that you understand how AI agents work — the loop, the components, the patterns — here's how to put it into practice:
-
Start with a single, well-defined task. Don't try to build an "everything agent." Pick one workflow: code review, customer support triage, or data analysis.
-
Choose 3-5 tools. Agents with too many tools perform worse — the LLM struggles to pick the right one. Start narrow and expand.
-
Use the ReAct pattern. It's the most debuggable and widely supported. You can always switch to more advanced patterns later.
-
Set guardrails early. Max iterations, cost limits, and human escalation from day one. See our guardrails guide for a complete checklist.
-
Evaluate continuously. Build a test suite of real tasks and measure agent performance after every change.
If you're a solo developer, GoGogot lets you deploy a self-hosted agent with 27 built-in tools in one Docker command — perfect for experimenting with the loop pattern we've described.
If you're on a team, cowork.ink gives everyone shared access to orchestrated agents with built-in memory, tool management, and multi-agent coordination — no framework wiring required.
Get Started with AI Agents
The core of every AI agent is surprisingly simple: a while loop that calls an LLM, picks a tool, and feeds the result back in. The complexity comes from doing this reliably at scale — with proper memory, smart context engineering, and robust guardrails.
Visit cowork.ink to set up your team's first AI agent workspace — shared context, built-in orchestration, and agents that actually stay in the loop.