Throughout this course, we've explored the individual components of production AI agentsβorchestration, memory, tools, safety, and observability. Now it's time to see how these pieces come together in real-world implementations that handle millions of requests daily.
123456789101112{ "Comment": "Customer Service Agent Orchestration", "StartAt": "ClassifyIntent", "States": { "ClassifyIntent": { "Type": "Task", "Resource": "arn:aws:lambda:us-east-1:123456789:function:classify-intent", "Next": "RouteByIntent", "Catch": [{ "ErrorEquals": ["States.ALL"], "Next": "FallbackToHuman" }]
123456789101112import boto3 from anthropic import Anthropic import subprocess import json class CodingAgent: def __init__(self, repo_path: str): self.bedrock = boto3.client('bedrock-runtime') self.s3 = boto3.client('s3') self.repo_path = repo_path self.context_cache = {}
123456789101112import boto3 import json from typing import Dict, List, Any from dataclasses import dataclass from enum import Enum class AgentRole(Enum): RESEARCHER = "researcher" ANALYZER = "analyzer" WRITER = "writer" REVIEWER = "reviewer"
123456789101112import boto3 import json from typing import Any, Dict, Optional from functools import wraps import time import logging logger = logging.getLogger(__name__) class ToolExecutionError(Exception): def __init__(self, tool_name: str, error_type: str, message: str, recoverable: bool = True):
123456789101112import boto3 import json import time from aws_lambda_powertools import Logger, Tracer, Metrics from aws_lambda_powertools.metrics import MetricUnit from typing import Dict, Any import uuid logger = Logger(service="agent-service") tracer = Tracer(service="agent-service") metrics = Metrics(namespace="AgentMetrics", service="agent-service")