Anatomy of AI Agents

An AI agent is an autonomous system that combines perception, reasoning, and action capabilities to achieve specific goals. Rather than viewing an agent as just a large language model, it should be treated as a system made up of several coordinating components.

Core Component Diagram

The following diagram illustrates how inputs (sensors) are processed by the processing unit (brain) to determine actions executed in the environment (actuators).

🧩 The Core Components Breakdown

An agentic architecture is typically divided into three primary layers:

1. Sensors (Input Processing)

Sensors perceive the environment and convert external signals into formats the reasoning engine can process.

• Text Input & User Queries: Natural language conversations from a chatbot interface.
• API Data Feeds: Real-time data streams (e.g., stock market prices, system log outputs).
• Database & Retrieval Queries: Fetching relational rows or unstructured document chunks via semantic search.
• File System Streams: Reading PDFs, text documents, or csv spreadsheets.

2. Processing Unit (The Brain)

The processing unit coordinates reasoning, memory storage, and action planning.

A. The Reasoning Engine (LLM)

The Large Language Model is the central “inference” engine. It interprets the prompt, assesses available tools, plans the sequence of actions, and structures outputs (often as JSON or Tool Calls).

Key Insight: The LLM does not execute actions directly; it instructs the surrounding orchestration framework (the runtime) what actions to run.

B. Knowledge Base & Rules

Defines domain parameters, safety boundaries, regulatory guidelines, and factual constraints that the agent must respect during execution.

C. Reasoning Patterns (e.g., ReAct)

Most modern agents use the ReAct (Reason + Act) loop. The agent executes a cycle of:

1. Thought: The model reasons about the current state and decides which tool to use.
2. Action: The model outputs a tool call with arguments.
3. Observation: The environment runs the tool and returns the results. The model reads this result and decides whether the task is complete.

[User Request] ──► Thought ──► Action (Tool Call) ──► Observation (Tool Result) ──► Final Answer

3. Actuators (Output & Execution)

Actuators perform the decisions calculated by the processing unit.

• Generating Responses: Delivering formatted markdown text or interactive UI elements back to the user.
• Triggering APIs: Running external service webhooks, modifying Linear issues, sending emails, or charging Stripe invoices.
• Modifying State: Saving data to relational tables, updating vector records, or creating local text/binary files.

🏆 Key Characteristics of Agents

• Autonomy: Operates and chooses paths independently without constant human intervention.
• Reactivity: Real-time adaptation to changing tool outcomes, network errors, or input modifications.
• Proactivity: Takes steps to check, verify, and correct its own mistakes (e.g., checking code compilation errors and rewriting the script).
• Social Ability: Communicates with other agents, services, or human managers via standardized protocols (e.g., Model Context Protocol).

📚 Resources & References

Documentation & Guides

• AutoGPT Documentation
• OpenAI Function Calling Guide
• Multi-AI Agent Systems with CrewAI Course
• Building AI Agents with CrewAI (Medium)
• AI Agents Components Overview
• Agentic Systems Roadmap

Research Papers

• ReAct: Synergizing Reasoning and Acting in Language Models (Yao et al., 2022)
• Chain-of-Thought Prompting (Wei et al., 2022)
• Constitutional AI: Harmlessness from AI Feedback (Anthropic, 2022)