AI Agents

AI agents are autonomous software systems that can perceive their environment, make decisions, and take actions to achieve specific goals without constant human intervention.

Unlike traditional chatbots that simply respond to queries, AI agents can:

• Plan - Break down complex tasks into steps
• Execute - Use tools and APIs to complete tasks
• Learn - Improve from feedback and experience
• Collaborate - Work with other agents or humans

The key distinction is agency - the ability to act independently toward a goal, adapting strategies as needed.

The best way to understand AI agents is to see the difference in code. Let's compare a simple LLM call versus an AI agent approach.

Approach 1: Simple LLM Call

A standard LLM just generates text based on input - no actions, no tools, no memory:

import openai

# Simple LLM - just generates text, no actions
def simple_llm_query(question: str) -> str:
    response = openai.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "user", "content": question}
        ]
    )
    return response.choices[0].message.content

# Example: Ask about weather
result = simple_llm_query("What's the weather in Tel Aviv?")
print(result)
# Output: "I don't have access to real-time weather data..."

Approach 2: AI Agent with Tools

An AI agent can use tools to actually GET the weather and take actions:

from langchain.agents import initialize_agent, Tool
from langchain.llms import OpenAI
import requests

# Define tools the agent can use
def get_weather(city: str) -> str:
    # Actually fetch real weather data
    api_url = f"https://api.weather.com/{city}"
    response = requests.get(api_url)
    return response.json()["current"]

def send_notification(message: str) -> str:
    # Send actual notification
    # ... notification logic
    return f"Notification sent: {message}"

# Create tools list
tools = [
    Tool(
        name="Weather",
        func=get_weather,
        description="Get current weather for a city"
    ),
    Tool(
        name="Notify",
        func=send_notification,
        description="Send a notification message"
    )
]

# Initialize agent with tools
agent = initialize_agent(
    tools=tools,
    llm=OpenAI(model="gpt-4"),
    agent="zero-shot-react-description"
)

# Agent decides what to do and executes
result = agent.run(
    "Check the weather in Tel Aviv and notify me if it's above 30°C"
)
print(result)

The Key Differences

AI agents operate through a continuous loop of perception, reasoning, and action:

The Agent Loop

1. Observe - Receive input from environment (user queries, API responses, file contents)
2. Think - Use LLM reasoning to analyze the situation and plan next steps
3. Act - Execute tools, call APIs, or generate outputs
4. Reflect - Evaluate results and adjust strategy
5. Repeat - Continue until goal is achieved

Core Components

• LLM Brain - The reasoning engine (GPT-4, Claude, Gemini)
• Tools - APIs and functions the agent can call
• Memory - Short-term (conversation) and long-term (vector DB)
• Planning Module - Task decomposition and strategy

The ecosystem of AI agent frameworks has matured significantly. Here are the leading options:

LangChain / LangGraph

The most popular framework for building LLM applications. LangGraph adds stateful, multi-actor workflows.

• Best for: Production applications, complex workflows
• Pros: Mature ecosystem, extensive documentation, large community
• Cons: Can be verbose, steep learning curve

CrewAI

Focused on multi-agent collaboration with role-based agents working together.

• Best for: Team simulations, complex multi-step tasks
• Pros: Intuitive role-based design, great for workflows
• Cons: Less flexible for single-agent scenarios

AutoGPT / AgentGPT

Fully autonomous agents that can self-direct toward goals.

• Best for: Research, exploration, autonomous tasks
• Pros: True autonomy, minimal human intervention
• Cons: Can go off-track, resource intensive

Claude Code / Anthropic Agent SDK

Anthropic's agentic coding assistant and SDK for building Claude-powered agents.

• Best for: Coding tasks, developer workflows
• Pros: Excellent reasoning, safe by design
• Cons: Claude-specific

Here's a practical roadmap to building your first AI agent:

Step 1: Define the Goal

Start with a specific, achievable goal. Examples:

• Research agent that summarizes articles on a topic
• Code review agent that analyzes pull requests
• Customer support agent that answers FAQs

Step 2: Choose Your Stack

• LLM: Claude 3.5/4, GPT-4, or open-source (Llama 3)
• Framework: LangChain for production, CrewAI for multi-agent
• Memory: Pinecone, Chroma, or Weaviate for vector storage

Step 3: Design Your Tools

Define what actions your agent can take:

• Web search (Tavily, SerpAPI)
• File operations (read, write, edit)
• API calls (custom integrations)
• Code execution (sandboxed environments)

Step 4: Implement Safety Guards

• Input validation
• Output filtering
• Rate limiting
• Human-in-the-loop for critical actions

Step 5: Test and Iterate

Start with simple test cases and gradually increase complexity.

Practical Example: TypeScript Agent with Tool Use

Here's a real-world example using Anthropic's Claude with tool use:

import Anthropic from "@anthropic-ai/sdk";

// Define the tools our agent can use
const tools = [
  {
    name: "search_database",
    description: "Search the product database for items",
    input_schema: {
      type: "object",
      properties: {
        query: { type: "string", description: "Search query" },
        category: { type: "string", description: "Product category" }
      },
      required: ["query"]
    }
  },
  {
    name: "create_order",
    description: "Create an order for a product",
    input_schema: {
      type: "object",
      properties: {
        product_id: { type: "string" },
        quantity: { type: "number" }
      },
      required: ["product_id", "quantity"]
    }
  }
];

// Tool implementation
async function executeTool(name: string, input: any) {
  if (name === "search_database") {
    // Simulate database search
    return { products: [{ id: "SKU-001", name: "Widget Pro", price: 29.99 }] };
  }
  if (name === "create_order") {
    // Create actual order
    return { order_id: "ORD-12345", status: "confirmed" };
  }
}

// Agent loop - keeps running until task is complete
async function runAgent(userRequest: string) {
  const client = new Anthropic();
  const messages = [{ role: "user", content: userRequest }];

  while (true) {
    const response = await client.messages.create({
      model: "claude-sonnet-4-20250514",
      max_tokens: 1024,
      tools: tools,
      messages: messages
    });

    // Check if agent wants to use a tool
    if (response.stop_reason === "tool_use") {
      for (const block of response.content) {
        if (block.type === "tool_use") {
          console.log(`Using tool: ${block.name}`);
          const result = await executeTool(block.name, block.input);

          // Add tool result back to conversation
          messages.push({ role: "assistant", content: response.content });
          messages.push({
            role: "user",
            content: [{ type: "tool_result", tool_use_id: block.id, content: JSON.stringify(result) }]
          });
        }
      }
    } else {
      // Agent is done - return final response
      return response.content[0].text;
    }
  }
}

// Run the agent
const result = await runAgent("Find a Widget Pro and order 2 of them");
console.log(result);

Multi-agent systems combine multiple specialized agents to tackle complex problems. This approach mirrors how human teams work.

Common Patterns

1. Hierarchical (Manager-Worker)

A manager agent delegates tasks to specialized worker agents.

• Manager: Plans and coordinates
• Workers: Execute specific tasks (research, coding, writing)

2. Collaborative (Peer-to-Peer)

Equal agents that pass work between each other.

• Writer → Editor → Fact-Checker → Publisher

3. Competitive (Debate)

Agents argue different perspectives to reach better conclusions.

• Useful for decision-making and validation

Real-World Applications

• AI Hedge Funds: Analyst, Risk Manager, Trader agents
• Content Pipelines: Researcher, Writer, Editor, SEO agents
• Software Development: Architect, Developer, Tester, Reviewer agents

Memory is what separates simple chatbots from true AI agents. Effective memory allows agents to learn, recall context, and improve over time.

Types of Agent Memory

Short-Term Memory

The current conversation context, typically stored in the prompt.

• Limited by context window size
• Lost after session ends

Long-Term Memory

Persistent storage using vector databases.

• Survives across sessions
• Semantic search retrieval
• Popular solutions: Pinecone, Chroma, Weaviate

Episodic Memory

Records of specific events and interactions.

• What happened, when, with whom
• Useful for learning from past experiences

Procedural Memory

Learned skills and workflows.

• How to perform specific tasks
• Can be updated as agent learns

Implementation Tips

• Use embeddings to store semantic meaning
• Implement relevance scoring for retrieval
• Consider memory summarization for efficiency
• Add timestamps for temporal context