Understanding AI Agent Architecture: Components, Types & How They Work

AI agents are more than fancy chatbots. Under the hood, every agent shares a common architecture — a set of interacting components that allow it to perceive its environment, reason about goals, and take autonomous action. In this tutorial, you'll learn exactly how those pieces fit together.

What You'll Learn#

• The four core components every AI agent needs
• How agents differ from traditional chatbots and automation scripts
• The three main agent types: reactive, deliberative, and hybrid
• How to choose the right architecture for your use case

Prerequisites#

• Basic understanding of what AI agents are
• Familiarity with large language models (LLMs) like GPT or Claude
• No coding required — this is a conceptual guide

The Four Core Components#

Every AI agent, regardless of complexity, is built from four fundamental building blocks:

┌─────────────────────────────────────┐
│            AI AGENT                 │
│                                     │
│  ┌───────────┐   ┌───────────────┐  │
│  │  LLM Brain│   │   Memory      │  │
│  │ (Reasoning)│  │ (Short + Long)│  │
│  └─────┬─────┘   └──────┬────────┘  │
│        │                │           │
│  ┌─────┴────────────────┴────────┐  │
│  │       Planning Module         │  │
│  └─────────────┬─────────────────┘  │
│                │                    │
│  ┌─────────────┴─────────────────┐  │
│  │     Tools & Actions           │  │
│  │  (APIs, Search, Code, DB)     │  │
│  └───────────────────────────────┘  │
└─────────────────────────────────────┘

1. The LLM Brain (Reasoning Engine)#

The LLM is the agent's "brain." It interprets inputs, generates plans, and decides which tools to call. Think of it as the decision-making core.

| Capability | What it does | |------------|-------------| | Natural language understanding | Parses user requests and unstructured data | | Reasoning | Applies chain-of-thought logic to break down problems | | Generation | Produces text, code, or structured outputs | | Tool selection | Decides which tools to call and in what order |

Key insight: the LLM alone is not an agent — it becomes one when you connect it to memory, planning, and tools.

2. Memory Systems#

Memory allows agents to retain context across interactions. There are two types:

Short-term memory (Working Memory)

• The current conversation or task context
• Recent tool results and intermediate reasoning
• Typically stored in the LLM's context window

Long-term memory (Persistent Storage)

• User preferences and past interactions
• Knowledge base documents (via RAG)
• Task history and learned patterns
• Stored in vector databases or traditional databases

Without memory, your agent is stateless — it forgets everything between conversations. That's fine for simple tasks, but enterprise agents need long-term memory to improve over time.

3. Planning Module#

The planning module determines how the agent accomplishes a goal. Different planning strategies include:

ReAct (Reasoning + Acting)

Thought: I need to find the customer's order status.
Action: query_order_database(order_id="12345")
Observation: Order shipped on Feb 1, tracking: UPS-789
Thought: I have the info, I can respond now.
Answer: Your order shipped on Feb 1. Tracking: UPS-789.

Plan-and-Execute

1. Break the goal into sub-tasks
2. Execute each sub-task sequentially
3. Re-plan if a step fails

Tree of Thought

• Explore multiple reasoning paths simultaneously
• Evaluate and prune poor branches
• Select the most promising solution

4. Tools and Actions#

Tools extend the agent beyond text generation. Common tool categories:

| Category | Examples | |----------|----------| | Information retrieval | Web search, database queries, API calls | | Code execution | Running Python, SQL, shell commands | | Communication | Sending emails, Slack messages, notifications | | File operations | Reading/writing documents, spreadsheets | | External services | CRM updates, calendar management, payments |

The more tools an agent has, the more capable it becomes — but also the harder it is to control. Start with 3-5 focused tools.

Agent Types Explained#

Reactive Agents#

Reactive agents respond to inputs with predefined patterns. They don't plan ahead — they react.

Best for: Simple, repetitive tasks with clear rules Example: A chatbot that answers FAQs by matching keywords to responses Limitation: Cannot handle novel situations or multi-step reasoning

Deliberative Agents#

Deliberative agents maintain an internal model of the world and plan before acting. They reason about consequences.

Best for: Complex tasks requiring multi-step reasoning Example: A research agent that plans a search strategy, gathers sources, synthesizes findings, and writes a report Limitation: Slower and more expensive due to extensive reasoning

Hybrid Agents#

Hybrid agents combine reactive speed with deliberative depth. They use fast pattern matching for routine tasks and switch to deliberative planning for complex ones.

Best for: Production systems handling diverse requests Example: A customer service agent that instantly answers common questions (reactive) but escalates complex complaints with a full investigation plan (deliberative)

How Agents Differ from Chatbots#

| Feature | Chatbot | AI Agent | |---------|---------|----------| | Memory | Limited to conversation | Persistent across sessions | | Tools | None (text only) | APIs, databases, services | | Planning | None | Multi-step reasoning | | Autonomy | Responds to prompts | Takes initiative | | Goal pursuit | Answers questions | Completes tasks end-to-end |

The key difference: chatbots talk, agents do.

Choosing the Right Architecture#

Use this decision framework:

1. Simple, single-step tasks → Reactive agent with 1-2 tools
2. Multi-step tasks with clear workflow → Plan-and-execute with defined tool chain
3. Complex, open-ended tasks → ReAct or Tree of Thought with broad tool access
4. High-volume mixed tasks → Hybrid with routing logic

Common Mistakes to Avoid#

1. Over-engineering the architecture: Start with the simplest agent type that solves your problem
2. Ignoring memory design: Decide early what your agent needs to remember
3. Too many tools: More tools mean more confusion for the LLM — curate carefully
4. No observability: Always log agent reasoning steps for debugging

Next Steps#

Now that you understand the architecture, put it into practice:

• Build Your First AI Agent — hands-on beginner project
• Prompt Engineering for AI Agents — craft effective system prompts
• Introduction to RAG for AI Agents — add knowledge to your agent

---

Frequently Asked Questions#

Do all AI agents use LLMs?#

Not necessarily. Traditional AI agents (like game-playing bots) use rule-based systems or reinforcement learning. However, modern AI agents almost always use LLMs as their reasoning engine because of their flexibility and natural language capabilities.

How much does it cost to run an AI agent?#

Costs depend on the LLM provider, number of tool calls, and volume. A simple agent using GPT-4o-mini might cost $0.01-0.05 per task. Complex agents with multiple LLM calls can cost $0.50-2.00 per task. Always monitor your usage.

Can I build an AI agent without coding?#

Yes! Platforms like Lindy.ai, Relevance AI, and n8n let you build agents visually. For more control, frameworks like LangChain and CrewAI require Python knowledge. Start no-code, then graduate to code as your needs grow.

What's the difference between an AI agent and an AI workflow?#

An AI workflow is a fixed sequence of steps — it always follows the same path. An AI agent dynamically decides what to do next based on context. Agents are more flexible but harder to predict. Many production systems combine both: workflows for predictable processes with agent-powered decision points.