What Are AI Agents? Concepts, Architecture & How They Work (2026)

An AI agent is a system that perceives its environment, reasons about it, and takes actions to achieve a goal — repeatedly, in a loop.

What Makes Something an “Agent”?

The minimal definition: a model + a loop + tools.

while not done:
    observation → LLM → action → execute → observation

Most LLM chat interfaces are not agents — they’re single-turn request/response. An agent persists across turns, maintains state, and uses tools to affect the world.

The Agent Loop

Every agent runs some variation of the ReAct loop:

1. Observe — receive input (user message, tool result, environment state)
2. Think — reason about what to do next (the LLM’s job)
3. Act — call a tool, execute code, or produce output
4. Observe — receive the result and loop

# Simplified agent loop
while not agent.is_done():
    thought = llm.think(agent.context)
    action = thought.next_action
    result = tools.execute(action)
    agent.context.append(result)

Core Architecture

┌─────────────────────────────────────────┐
│              Agent System               │
│                                         │
│  ┌─────────┐    ┌──────────────────┐   │
│  │  Input  │───▶│    LLM (Brain)   │   │
│  └─────────┘    └────────┬─────────┘   │
│                          │             │
│              ┌───────────▼──────────┐  │
│              │   Tool Dispatcher    │  │
│              └─┬──────┬──────┬─────┘  │
│                │      │      │         │
│           ┌────▼┐ ┌───▼─┐ ┌─▼────┐   │
│           │ Web │ │Code │ │ API  │   │
│           │ Srch│ │Exec │ │Calls │   │
│           └────┘ └─────┘ └──────┘   │
│                                         │
│  ┌──────────────────────────────────┐  │
│  │         Memory / State           │  │
│  └──────────────────────────────────┘  │
└─────────────────────────────────────────┘

See Also

• Agent Patterns — Common design patterns for agent systems
• Tokens & Context — Managing context windows effectively