From Chatbots to AI Agents: Understanding Modern Agentic Architectures

Introduction

AI agents are reshaping the landscape of AI development. Unlike chatbots that respond once and forget, agents reason, take actions, and operate autonomously in dynamic environments. This post explores the key concepts that differentiate chatbots from AI agents, outlines the core components of modern agentic systems, and introduces patterns like the TAO loop and MCP protocol that enable powerful multi-step reasoning.

Chatbot vs Agents

Let’s start with a simple comparison:

Chatbot = Prompt + LLM

AI Agent = Prompt + LLM + Tools + External Environment + Persistent Memory + TAO Loop

Chatbots are designed for one-shot responses. You give them a prompt, they give you a reply. They don’t remember past conversations or take actions beyond generating text.

In contrast, agents operate in goal-directed workflows. They utilize tools (e.g., APIs, databases), interact with external systems, maintain memory over time, and reason iteratively through the Thought-Action-Observation (TAO) loop.

Anatomy of an AI Agent

Modern agents consist of the following components:

• LLM (Large Language Model): The brain that generates thoughts and plans.
• Prompt: The initial input and system instructions.
• Tools: APIs, functions, or services the agent can call.
• Environment: Real-world or simulated systems the agent interacts with.
• Memory: Persistent or ephemeral storage of past interactions.
• TAO Loop: The reasoning loop that enables iterative decision-making.

Tools in Agentic Systems

Agents extend the capability of LLMs by integrating tools. Tools can vary based on the type of data they provide:

Tools for Structured Data

• SQL query APIs
• Inventory APIs
• Financial dashboards

These tools return predictable, tabular data—useful for planning and analytics.

Tools for Unstructured Data

• Vector databases (e.g., for semantic search)
• Document retrievers (e.g., PDFs, webpages)
• OCR + image-to-text APIs

These provide text or embeddings for reasoning in less predictable contexts.

Thought-Action-Observation (TAO) Loop for iterative agents

TAO (Thought-Action-Observation) is the core loop that powers iterative agents.

Thought → Action → Observation → Thought → …

Each cycle allows the agent to refine understanding, make corrections, and plan next steps.

Visual:

One-Shot vs Iterative Agentic Workflows

One-Shot Agent

• Input → Prompt → LLM → Output
• No feedback or adjustment.

Typical One-Shot Agentic Workflow

1. User inputs a prompt in the chat window.
2. Agent receives the user prompt, retrieves available tool configurations, and passes both the prompt and tool list to the LLM.
3. LLM analyzes the prompt and responds with the most appropriate tool to use, along with the required arguments.
4. Agent regains control, invokes the selected tool using the LLM’s instructions, and retrieves the tool’s response.
5. Agent sends the original user prompt, tool response, and persistent memory (if any) back to the LLM, which generates the final answer.
6. Agent delivers the final response from the LLM back to the user.

Iterative Agent

• Input → Think (LLM) → Act (Tool Call) → Observe (Tool Result) → Repeat

Example:

• Goal: “Get tomorrow’s weather and suggest an outfit.”
  1. Think: Determine what tools are needed.
  2. Act: Call weather API.
  3. Observe: It’s 45°F and rainy.
  4. Think: Recommend waterproof jacket and boots.

This ability to loop until the goal is reached is what defines iterative agentic behavior.

Typical Iterative Agent Workflow with TAO Loop

1. User inputs a prompt in the chat window.
2. Agent initializes the TAO loop with:
   • User prompt
   • Available tools configuration
   • Access to persistent or working memory
3. Agent sends the current state (user prompt + memory + tool history) and tools list to the LLM.
4. LLM analyzes the state and decides:
   • Which tool to invoke (if any)
   • Or whether to return a final answer
   • Or whether to continue reasoning
5. If a tool is selected by the LLM:
   • Agent calls the chosen tool with the specified arguments (Act step).
   • Tool executes and returns a result (observation) to the Agent.
6. Agent updates working memory with:
   • The tool action taken
   • The tool’s response
   • Any other relevant state (e.g., iteration count)
7. Agent loops back and sends updated state to the LLM, which:
   • Observes the tool output
   • Thinks again about next steps
   • Decides to stop or continue
8. Steps 3–7 repeat iteratively (TAO loop) until one of the following happens:
   • LLM issues a FINISH signal indicating task completion
   • Maximum iteration limit is reached
   • A policy-based stop condition is triggered
9. Agent sends the final response generated by the LLM back to the user.

Connecting to the MCP Protocol

The Model Context Protocol (MCP) acts like the nervous system connecting the LLM (brain) with external tools (hands and eyes).

Analogy:

• LLM = Brain: Thinks, reasons, and makes decisions.
• Tools = Hands and Eyes: Perform actions and perceive the environment.
• MCP = Nervous System: Transmits signals and coordinates communication between the brain and tools.

MCP defines a standardized interface that enables agents to:

• Discover available tools dynamically
• Execute tool functions securely using JSON-RPC calls

This abstraction cleanly separates tool management from the LLM’s reasoning process, making agents more modular, flexible, and generalizable.

How Is This Possible?

The below key breakthroughs make agentic systems viable today:

Modern large language models (LLMs) are capable of deeply modeling human language and intent, thanks to two foundational innovations:

• Word2Vec (2013): Introduced vector embeddings that captured semantic relationships between words (e.g., king – man + woman ≈ queen). This marked a significant shift from symbolic language representations to numerical ones, enabling machines to reason about word meaning through geometry.
• Transformer Architecture (2017): Introduced in the paper “Attention is All You Need”, transformers enabled models to dynamically attend to relevant parts of input sequences. This allowed for parallel training, long-range dependencies, and deep contextual understanding at scale.

Together, these breakthroughs paved the way for models like GPT-4 and Claude-3 to interpret complex instructions, understand context, and reason across multiple steps—capabilities essential for modern AI agents.

Conclusion

AI agents represent a significant leap beyond chatbots. By combining LLMs with external tools, persistent memory, and iterative reasoning (via the TAO loop), agents can perform complex tasks and make decisions over time.