AI Agent Fundamentals

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{
  "name": "search_customer_orders",
  "description": "Search for customer orders by various criteria. Use this tool when the user asks about order status, order history, or needs to find a specific order. Returns up to 10 matching orders sorted by date descending. Requires at least one search parameter.",
  "input_schema": {
    "type": "object",
    "properties": {
      "customer_email": {
        "type": "string",
        "description": "Customer's email address for exact match"
      },
      "order_id": {
        "type": "string",

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import anthropic
from typing import Any

def run_agent(user_message: str, tools: list, max_iterations: int = 10) -> str:
    client = anthropic.Anthropic()
    messages = [{"role": "user", "content": user_message}]
    
    for iteration in range(max_iterations):
        response = client.messages.create(
            model="claude-sonnet-4-20250514",
            max_tokens=4096,
            tools=tools,

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import json
from typing import Callable
import boto3

class MinimalAgent:
    def __init__(self, model_id: str = 'anthropic.claude-3-sonnet-20240229-v1:0'):
        self.bedrock = boto3.client('bedrock-runtime')
        self.model_id = model_id
        self.tools = {}
        self.max_iterations = 10
        
    def register_tool(self, name: str, func: Callable, description: str, parameters: dict):

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# Define tools for AWS Bedrock Agents using OpenAPI schema
import json

# Tool definition following Bedrock's action group format
calendar_tool_schema = {
    "openapi": "3.0.0",
    "info": {
        "title": "Calendar Management API",
        "version": "1.0.0"
    },
    "paths": {
        "/events": {

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from dataclasses import dataclass
from typing import List, Optional
import json

@dataclass
class ExpectedToolCall:
    tool_name: str
    required_params: dict  # Params that must match exactly
    optional_params: dict = None  # Params that may or may not be present

@dataclass
class AgentTestCase: