Here's a practical roadmap to building our first AI agent. Let's break this down step by step.
Step 1: Define the Goal
Start with something specific and achievable. Don't try to build Jarvis on day one. Good starting points:
- Research agent that summarizes articles on a topic
- Code review agent that analyzes pull requests
- Customer support agent that answers FAQs from our docs
Step 2: Choose Our Stack
- LLM: Claude 3.5/4 (my recommendation), GPT-4, or open-source like Llama 3
- Framework: LangChain for production, CrewAI for multi-agent
- Memory: Pinecone, Chroma, or Weaviate for vector storage
Step 3: Design Our Tools
What actions should our agent be able to take? Common ones:
- Web search (Tavily, SerpAPI)
- File operations (read, write, edit)
- API calls (custom integrations)
- Code execution (sandboxed environments)
Step 4: Implement Safety Guards
This is critical - we don't want our agent going rogue:
- Input validation - sanitize everything
- Output filtering - no harmful content
- Rate limiting - prevent runaway costs
- Human-in-the-loop for critical actions - always ask before deleting prod data
Step 5: Test and Iterate
Start with simple test cases. If it can't handle "check the weather", don't give it access to our production database.
Practical Example: TypeScript Agent with Tool Use
Here's a real-world example using Anthropic's Claude with tool use. This is actual code we can run:
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);
Agent Execution Flow:
Using tool: search_database
Using tool: create_order
Final Response: "I found Widget Pro (SKU-001) at $29.99 and successfully created order ORD-12345 for 2 units. Your total is $59.98."
The exciting part? This is the entire agent. The LLM decides when to use which tool, and the loop keeps going until the task is done.
