Purpose
Create production-ready AI agents that can handle complex workflows, delegate to specialized sub-agents, maintain context, and produce validated structured outputs.
When to Use
- •Building conversational AI applications
- •Implementing multi-agent systems with specialized roles
- •Creating task management bots with natural language understanding
- •Developing agents that need structured data extraction
Core Competencies
1. Basic Agent Creation
- •Define agents with custom instructions
- •Implement function tools with automatic schema generation
- •Configure model settings (temperature, max_tokens, tool_choice)
- •Handle agent responses and tool calls
- •Implement streaming responses
2. Multi-Agent Systems
- •Build triage agents for routing queries
- •Create expert agents for specific domains
- •Implement agent handoffs and delegation
- •Design agent collaboration patterns
- •Use agent cloning and templates
3. Context Management
- •Build type-safe context objects
- •Implement dependency injection patterns
- •Manage user context across runs
- •Handle conversation threading
- •Access runtime information via context wrappers
4. Guardrails
- •Implement input validation guardrails
- •Create output validation checks
- •Build custom safety guardrails
- •Configure parallel guardrail execution
- •Handle guardrail failures gracefully
5. Structured Outputs
- •Configure agents to produce Pydantic models
- •Extract structured data from conversations
- •Validate outputs against schemas
- •Handle dataclasses and TypedDict
- •Build task parsers with validation
6. Observability
- •Enable built-in tracing
- •Export traces to external platforms
- •Debug agent decision-making
- •Monitor tool usage and performance
- •Visualize agent workflows
Implementation Guidelines
python
from agents_sdk import Agent, function_tool
from pydantic import BaseModel
class Task(BaseModel):
title: str
description: str | None = None
priority: str = "medium"
@function_tool
def add_task(title: str, description: str = None) -> dict:
"""Add a new task to the user's todo list."""
# Implementation
return {"task_id": 1, "status": "created"}
todo_agent = Agent(
name="todo_assistant",
instructions="""You are a helpful todo assistant.
Help users manage their tasks through natural language.""",
model="gpt-4",
tools=[add_task, list_tasks, complete_task]
)
# Run agent with context
result = todo_agent.run(
user_message="Add a task to buy groceries",
context={"user_id": "user123"}
)
Common Patterns
- •Triage agent + expert agents architecture
- •Context-aware tool execution
- •Structured output extraction
- •Multi-turn conversations with state
- •Error recovery and fallbacks
Resources
- •OpenAI Agents SDK Documentation
- •Agent Patterns Guide
- •Guardrails Best Practices