How AI Agents Work: A Plain-English Explanation (2026)
March 28, 2026 · 6 min read
TL;DR
An AI agent runs a Perceive → Plan → Act loop until a goal is complete. It uses tools (web search, email, file access) to interact with the real world, persistent memory to build context over time, and can run multiple sub-agents in parallel for complex tasks. The difference from a chatbot: agents act across multiple steps autonomously; chatbots respond once and stop.
The core loop: Perceive → Plan → Act
Every AI agent, regardless of complexity, runs some version of the same three-step loop. Understanding this loop explains almost everything about how agents behave — and why they are different from simpler AI tools.
Perceive
The agent reads everything available to it: your instructions, its memory of past sessions, tool outputs from previous loop iterations, and any external data (search results, files, emails). This is the agent's "situation awareness" — what it knows before deciding what to do.
Plan
The underlying language model (Claude, GPT-5, etc.) processes everything from the perception step and decides: Is the goal complete? If not, what is the next action? Which tool should I call? What should I write or say? This planning step is where the intelligence lives — the model reasons about what to do next based on the goal and current state.
Act
The agent executes the plan from the previous step. It might call a tool (search the web, send an email, write a file), generate text output, or update its own memory. After the action, the results feed back into the next Perceive step — and the loop continues until the goal is achieved or the agent determines it cannot proceed.
A chatbot runs exactly one cycle of this loop and stops. An AI agent runs it continuously — adjusting its plan based on what each action produces — until the task is done or the agent hits a blocker it cannot resolve alone.
The four types of AI agent memory
| Memory type | What it stores | Survives session? | Example |
|---|---|---|---|
| In-context | Current conversation and task details | No | ChatGPT, Claude.ai |
| External (persistent) | User profile, preferences, past work | Yes | Happycapy memory system |
| Procedural | How to use tools, task patterns | Yes (in model weights) | Built into all agents |
| Episodic | Records of past actions and outcomes | Yes (if stored) | Happycapy session history |
How tools extend what agents can do
Without tools, an AI agent can only read text and generate text. Tools are what let agents interact with the real world — and they are the core of what makes agent platforms powerful.
When an agent's planning step decides it needs information it does not have, it calls a tool. The tool executes, returns a result, and the agent incorporates that result into its next planning step. Tools are modular — any capability that can be expressed as an input/output function can be a tool.
🔍
Web search
Live information retrieval
✉️
Email send
Deliver output to inbox
📄
File read/write
Access local documents
⚡
Code execution
Run scripts, check output
🔗
API calls
Connect external services
🖼️
Image generation
Create visual assets
Happycapy has 150+ skills — each is a tool the agent can call autonomously to complete a task.
Multi-agent systems: parallel work
Complex tasks can be decomposed and run in parallel by multiple agents working simultaneously. An orchestrator agent breaks the task into sub-tasks and assigns each to a specialized sub-agent. Each sub-agent runs its own Perceive-Plan-Act loop. Results are collected and synthesized by the orchestrator.
Happycapy Agent Teams implements this pattern: you describe a complex goal, Capy spawns a team of agents with defined roles (researcher, writer, editor, publisher), they work in parallel, and the orchestrator delivers the final output. A task that takes one agent 40 minutes takes a 4-agent team 10 minutes.
Summary
AI agents work by looping through Perceive, Plan, and Act until a goal is complete. They use persistent memory to build context over time, tools to interact with the real world, and multi-agent coordination for complex parallel tasks. The difference from a chatbot is not intelligence — it is architecture. Agents are designed to do work across multiple steps. Chatbots are designed to respond to one message.
Frequently asked questions
How do AI agents work?
AI agents work by running a continuous loop of three steps: (1) Perceive — the agent reads inputs like your message, its memory, search results, or file contents. (2) Plan — the underlying language model decides what to do next: which tool to use, what to write, or whether the task is complete. (3) Act — the agent executes the plan: writes a file, sends an email, calls an API, searches the web, or generates text. This loop repeats until the task is done. The agent remembers past loops through its memory system and stops when the goal is achieved.
What makes an AI agent different from a chatbot?
A chatbot runs one cycle: receive message → generate response → stop. An AI agent runs multiple cycles to complete a goal. It plans multi-step actions, uses tools (web search, email, file access), persists memory across sessions, and can run on a schedule without being prompted. The key technical difference is the action loop: chatbots respond once; agents act repeatedly until a goal is reached.
What types of memory do AI agents use?
AI agents use four types of memory: (1) In-context memory — the current conversation and task details, limited by the model's context window. (2) External memory — a persistent database of facts, preferences, and history that survives between sessions (this is what Happycapy's memory system provides). (3) Procedural memory — how to use tools and complete specific task types, built into the agent's training. (4) Episodic memory — records of past actions and their outcomes, used to improve future decisions. Most chatbots only have in-context memory. Agent platforms add external memory to make the AI genuinely improve over time.
How do AI agents use tools?
AI agents use tools by calling functions during their planning step. When the agent decides it needs to search the web, it calls the web_search tool with a query and receives the results back. When it needs to send an email, it calls the send_email tool with the recipient and content. Tools extend what the agent can perceive and act on — without tools, an agent can only read text and generate text. With tools (search, email, file access, code execution), it can interact with the real world. Happycapy has 150+ tools as 'skills' the agent can call.
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