Introduction to AI Agents: A Technical Overview for Developers

Source: Dev.to

Artificial intelligence has shifted from static prompt–response patterns to systems capable of taking structured actions. These systems are known as AI agents. Although the term is often stretched in marketing, the underlying architecture is practical and grounded in well‑understood software principles.

I took the 5‑day AI agents intensive course with Google and Kaggle, and I promised myself to document what I learned each day.

This article is the first in a 5‑part series that outlines the foundational concepts needed to build an AI agent and sets the stage for subsequent posts that will explore implementation details, tool integration, orchestration, governance, and evaluation.

---

What Is an AI Agent?

Technically, an AI agent is a software system that uses a language model, tools, and state management to complete a defined objective. It operates through a controlled cycle of reasoning and action, instead of remaining a passive text generator.

A typical AI agent includes:

• Model – for reasoning
• Set of tools – for retrieving information or executing operations
• Orchestrator – that manages the interaction between the model and those tools
• Deployment layer – for running the system at scale

This structure turns a model from a text interface into an operational component that can support business processes or technical workflows.

The AI Agent Workflow: The Think–Act–Observe Cycle

All agent systems follow a predictable control loop. This loop is essential because it governs correctness, safety, and resource usage.

1. Mission Acquisition
   The system receives a task, either from a user request or an automated trigger.
   Example: “Retrieve the status of order #12345.”

2. Context Assessment
   The agent evaluates available information: prior messages, stored state, tool definitions, policy rules.

3. Reasoning Step
   The model generates a plan.
   Example:

   • Identify the correct tool for order lookup
   • Identify the tool for shipping data retrieval
   • Determine response structure

4. Action Execution
   The orchestrator calls the selected tool with validated parameters.

5. Observation and Iteration
   The agent incorporates tool output back into its context, reassesses the task, and continues until completion or termination.

This controlled loop prevents uncontrolled behavior and supports predictable outcomes in production systems.

Core Architecture of an AI Agent System

1. Model Layer

The model performs all reasoning. Selection depends on latency requirements, cost boundaries, task complexity, and input/output formats. Multiple models may be used for routing, classification, or staging tasks, though initial implementations usually rely on a single model for simplicity.

2. Tool Layer

Tools provide operational capability. A tool is a function with strict input/output schemas and clear documentation. Categories include:

• Data retrieval (APIs, search functions, database operations)
• Data manipulation (formatting, filtering, transformation)
• Operational actions (ticket creation, notifications, calculations)

Effective tool design keeps actions narrow, predictable, and well‑documented. Tools form the “action surface” of the agent and determine how reliably the system can complete assigned objectives.

3. Orchestration Layer

This layer supervises the system and is responsible for:

• Running the reasoning loop
• Applying system rules
• Tracking state
• Managing tool invocation
• Handling errors
• Regulating cost and step limits

Developers define the agent’s operational scope and boundaries here.

4. Deployment Layer

An agent becomes useful only when deployed as a service. A typical deployment includes:

• An API interface
• Logging and observability
• Access controls
• Storage for session data or long‑term records
• Continuous integration workflows

This layer ensures the agent behaves as a reliable software component rather than a prototype.

Capability Levels in AI Agents

Understanding agent capability levels helps set realistic expectations.

Building Your Practical First Agent

Developers do not need a complex system to get a simple agent running. A small, well‑defined project is sufficient for understanding the architecture. The following steps were executed in a Kaggle Notebook using Google’s Gemini model.

Step 1. Configure Your Gemini API Key

import os

# Replace with your actual key or load it from your environment manager
os.environ["GOOGLE_API_KEY"] = "YOUR_API_KEY_HERE"
print("API key configured.")

Step 2. Import ADK Core Components

from google.adk.agents import Agent
from google.adk.models.google_llm import Gemini
from google.adk.runners import InMemoryRunner
from google.adk.tools import google_search
from google.genai import types

Step 3. Optional: Retry Settings

retry_config = types.HttpRetryOptions(
    attempts=5,
    exp_base=7,
    initial_delay=1,
    http_status_codes=[429, 500, 503, 504],
)

Step 4. Define Your First Agent

root_agent = Agent(
    name="helpful_assistant",
    description="A simple agent that can answer general questions.",
    model=Gemini(
        model="gemini-2.5-flash-lite",
        retry_opt=retry_config,
    ),
    tools=[google_search],
)

From here you can instantiate a runner (e.g., InMemoryRunner) and start sending queries to root_agent. This minimal setup demonstrates the core pieces—model, tools, orchestrator, and deployment—required to build a functional AI agent.