Lesson 1.2 – How AI Agents Work

📌 Introduction

AI Agents are not just "fancy chatbots" or “automation scripts.” They are autonomous systems that blend reasoning, memory, integration, and communication into a single intelligent digital entity.

To understand how to build and use them effectively, we first need to understand how they actually work — what parts make them intelligent, and how those parts come together when a task is initiated.

Whether a human types a prompt or a system sends a request via API, the agent follows a highly coordinated process powered by an architectural stack purpose-built for reasoning, retrieval, and action.

🧠 The Core Architecture of an AI Agent

At a high level, every AI Agent consists of the following components:

This is not a monolithic system. It’s a modular architecture, where each part contributes context, logic, or data.

⚙️ The Agent Lifecycle: What Happens When a Task is Initiated

Let’s walk through what happens when a user asks a question or an app sends a prompt to an AI Agent.

🔁 Step-by-Step: How the Agent Works

Step 1: The Input (Prompt or API Call)

• The agent receives a natural language message from a human or a structured request from an application.

• This could come via:

  • raia Copilot (chat interface)

  • SMS or email

  • Live chat widget

  • Backend API or system trigger (e.g. “check inventory”)

Step 2: Instruction Context is Loaded

• The agent loads its system instructions — including tone, role, formatting rules, and behavioral expectations.

• These instructions define:

  • "You are a helpful support agent..."

  • How to format responses (bullets, markdown, JSON, etc.)

  • Whether it can take actions (e.g., "Use the CRM function to update status")

Step 3: Context is Retrieved

• The agent gathers relevant knowledge using three types of context:

This is often referred to as Retrieval-Augmented Generation (RAG) — blending static knowledge with live data.

Step 4: The Language Model “Thinks”

The LLM (e.g., GPT-4o) receives:

• The user’s message

• The relevant context chunks from the vector store

• Any outputs from function/tool calls

• Its internal instructions and system prompts

It now reasons over all these inputs, determines the intent, chooses the best path forward, and generates a response.

✨ Unlike traditional software, the AI Agent doesn't follow hardcoded logic — it reasons over context dynamically, every time.

Step 5: The Agent Responds or Acts

Depending on the request and instructions, the agent may:

• Return a response to the user (e.g., a summary, answer, recommendation)

• Perform an action (e.g., create a CRM record, trigger a webhook, send an email)

• Ask a clarifying question if the request is ambiguous

• Escalate to a human if configured to do so

This could appear as:

• A message in Copilot

• A webhook response to an app

• An outbound email

• A step in a multi-agent workflow

Step 6: It Logs, Learns, and Improves

• All interactions are logged.

• raia Copilot can be used to review, rate, and analyze the agent’s response quality.

• raia Academy can be used to update training data or tune retrieval quality if something was missing or inaccurate.

This supports continuous improvement — just like a human employee receiving feedback.

🧠 Why This Matters

This architecture gives AI Agents their unique powers:

It also means you must think differently when designing and testing agents:

• You train the agent with documents, not code

• You debug with feedback and prompt tuning, not logs and stack traces

• You test edge cases and context relevance, not just output correctness

🔌 The Role of Interfaces (UI Options)

AI Agents don't have just one "frontend" — they can be accessed through many channels:

You don’t have to choose one — agents can work across all channels with a unified intelligence layer.

✅ Key Takeaways

• AI Agents are modular systems with memory, logic, tools, and interface layers.

• When prompted, an agent retrieves context (vector store, functions, chat history) and uses the LLM to reason and respond.

• This architecture allows AI Agents to adapt, automate, and act, not just answer questions.

• Unlike traditional software, agents aren’t static — they learn, improve, and evolve based on human feedback and updated knowledge.

• The prompt is the new user interface — but behind the scenes, a lot more is happening than meets the eye.