AboutWorksResumeWritingsAPIContact
  1. Home
    2. /
  2. Writings
    3. /
  3. What is an AI Agent?

What is an AI Agent?

Thirumalesh Pinninti·July 20, 2026·8 min read

Standard language models complete text prompt-by-prompt. AI Agents, by contrast, operate inside closed execution loops—using tools, inspecting API responses, self-correcting errors, and driving tasks through to verified completion.

The default behavior of any large language model (LLM) is to act as a stateless predictor of text. You pass in a prompt, the model calculates probability distributions over vocabulary tokens, streams the resulting response, and halts. It does not verify whether its claims are true, query live systems, or correct mistakes encountered along the way.

This is the fundamental boundary separating a Chatbot from an AI Agent. While a chatbot generates text about a problem, an AI Agent is given the scaffolding to solve it autonomously—binding model reasoning to external tools, system memory, and continuous observation loops.

Pencil sketch illustration of an AI agent concept
Figure 1.0: Conceptual pencil draft of an AI Agent core connecting reasoning to external tool primitives.

When you ask a coding agent to fix a bug in a codebase, it doesn't just guess the fix in a single shot. It reads the project directory, inspects the compiler output, writes a candidate patch, executes unit tests, and loops until the test suite passes.

System Architecture · Hand-Drawn Execution Loop
User Query"Book Flight"Agent BrainReAct Reasoning Loop(Decomposes Task)External APIflight_search()Final Accomplished Result
Figure 1.1: ReAct (Reason + Act) loop with dynamic data flow paths.

The ReAct Execution Loop

Most modern agent architectures implement the ReAct (Reason + Act) framework. Rather than generating a single answer, the agent breaks its objective down into alternating cycles of reasoning and action:

  1. Thought: The LLM core evaluates the user prompt and current state to determine what information or action is needed next.
  2. Action: The agent issues a structured JSON payload calling an external tool signature (e.g., querying a database, invoking a web search, or running a shell script).
  3. Observation: The execution runtime intercepts the tool payload, runs the function, and feeds the response data directly back into the agent's context window.
Pencil sketch illustration of ReAct execution loop
Figure 1.2: The cyclical feedback loop between Thought, Action, and Observation.

This cycle repeats continuously until the agent determines that the goal has been achieved, at which point it returns the final result to the user.

Why Model Context Protocol (MCP) Matters

Historically, connecting AI agents to external databases and dev tools required custom API wrappers for every integration. Model Context Protocol (MCP) solves this by standardizing how host applications expose data sources, prompt templates, and tool interfaces to LLMs over JSON-RPC.

Pencil sketch illustration of Model Context Protocol
Figure 1.3: Model Context Protocol (MCP) acting as a standardized interface between AI cores and external infrastructure.

With MCP, an agent can seamlessly discover available tools—whether it is querying a local Git repository, fetching PostgreSQL records, or inspecting production deployment logs on Google Cloud—using a unified protocol interface.

The Path Forward

Building reliable AI agents requires moving beyond simple prompts and focusing on software engineering discipline: strict tool schemas, deterministic verification steps, and robust error recovery.

In the next chapter of The Visual AI Handbook, we will explore Tool Calling & Model Context Protocol (MCP) in depth—building a custom MCP server from scratch to connect agents to real-world infrastructure.

← All WritingsChapter 2: Tool Calling & MCP Protocols →