openai-agentkit-expert

name	OpenAI AgentKit Expert
description	Build production-ready multi-agent systems using OpenAI AgentKit and Agents SDK with best practices for agent orchestration, handoffs, and routines
version	1.1.0
last_updated	"2026-01-06T00:00:00.000Z"
external_version	OpenAI Agents SDK 0.6.4, GPT-5.2

OpenAI AgentKit Expert Skill

Purpose

This skill provides comprehensive guidance on building production-ready multi-agent systems using OpenAI's AgentKit platform and Agents SDK, following 2026 best practices.

Platform Overview

OpenAI AgentKit (2026)

Complete platform for building, deploying, and optimizing agents with enterprise-grade tooling.

Core Components:

• Agent Builder - Visual canvas for creating and versioning multi-agent workflows
• Connector Registry - Central management for data and tool connections
• ChatKit - Embeddable customizable chat-based agent experiences
• Evaluation Suite - Datasets, trace grading, automated prompt optimization
• Multi-Model Support - Third-party model integration capabilities

Agents SDK (Production-Ready)

The Agents SDK is the production evolution of the experimental Swarm framework. Use Agents SDK for all production work - Swarm is educational only.

Migration Note: If you encounter legacy Swarm code, migrate to Agents SDK immediately.

Core Concepts

1. Agents

An Agent encapsulates:

• A set of instructions (system prompt)
• A set of functions/tools
• The capability to hand off execution to another Agent

Design Principle: Agents should be lightweight and specialized rather than monolithic and general-purpose.

2. Routines

A routine is a sequence of actions an agent can perform:

• Natural language instructions (via system prompt)
• Available tools needed to execute
• Context and state management
• Success criteria

Think of it as: A mini-workflow that an agent owns and executes autonomously.

3. Handoffs

Handoffs enable agent-to-agent transitions in execution flow.

Key Pattern: When an agent encounters a task outside its specialization, it hands off to a more appropriate agent.

Example:

# Triage agent determines which specialist to use
if task.type == "refund":
    handoff_to(refund_agent)
elif task.type == "sales":
    handoff_to(sales_agent)

Architectural Patterns

Pattern 1: Triage Pattern

Use Case: Routing requests to specialized sub-agents

Structure:

User Request → Triage Agent → [Determines Category] → Specialist Agent

Example Implementation:

# Triage agent with handoff capabilities
triage_agent = Agent(
    name="Customer Service Triage",
    instructions="Analyze customer requests and route to appropriate specialist",
    functions=[analyze_request],
    handoffs=[refund_agent, sales_agent, support_agent]
)

When to Use:

• Multiple distinct capability domains
• Clear categorization logic
• Different agents need different tools/context

Pattern 2: Sequential Orchestration

Use Case: Multi-step workflows where each step has a specialist

Structure:

Step 1 Agent → [Complete] → Handoff → Step 2 Agent → ... → Final Agent

Example:

# Research → Analysis → Report Generation pipeline
research_agent → analysis_agent → report_agent

When to Use:

• Clear sequential dependencies
• Each step requires specialized expertise
• Output of one step feeds the next

Pattern 3: Parallel Decomposition

Use Case: Breaking complex tasks into parallel subtasks

Structure:

Coordinator Agent
    ↓
    ├─→ Subtask Agent 1
    ├─→ Subtask Agent 2
    └─→ Subtask Agent 3
    ↓
Synthesis Agent (combines results)

When to Use:

• Independent subtasks can run concurrently
• Need to aggregate multiple perspectives
• Performance optimization through parallelization

Best Practices

Agent Design

DO: ✅ Keep agents focused on single responsibilities ✅ Provide clear, specific instructions in system prompts ✅ Define explicit handoff conditions ✅ Use descriptive agent names (helps with debugging) ✅ Test agents in isolation before integration

DON'T: ❌ Create monolithic "do-everything" agents ❌ Allow agents to communicate directly (use handoffs) ❌ Over-engineer with too many specialized agents ❌ Ignore error handling in handoffs ❌ Skip agent boundary testing

Routine Design

Effective Routines:

• Have clear entry and exit conditions
• Include error handling paths
• Specify required context/state
• Document expected inputs/outputs
• Define success metrics

Example:

routine = {
    "name": "Process Refund",
    "entry": "User requests refund",
    "steps": [
        "Verify order exists",
        "Check refund eligibility",
        "Calculate refund amount",
        "Process payment reversal",
        "Send confirmation"
    ],
    "exit": "Refund confirmed or rejection reason provided",
    "error_handling": "Escalate to human agent if verification fails"
}

Handoff Design

Critical Elements:

• Clear Trigger Conditions - When should handoff occur?
• Context Passing - What information transfers?
• Return Path - Can control return to originating agent?
• Failure Handling - What if target agent unavailable?

Example:

def handoff_condition(state):
    """Determine if handoff needed"""
    if state.requires_specialized_knowledge:
        return specialist_agent
    if state.exceeds_authority_level:
        return supervisor_agent
    return None  # Continue with current agent

Performance Optimization

Minimize LLM Calls

Principle: Frameworks that limit LLM involvement and rely on predefined or direct execution flows operate more efficiently.

Strategies:

• Use deterministic logic where possible
• Cache common responses
• Batch similar requests
• Pre-compute decision trees
• Use smaller models for simple tasks

Efficient Tool Use

Principle: Give agents only the tools they need for their specialty.

Pattern:

# Specialized agents get targeted toolsets
refund_agent.tools = [verify_order, calculate_refund, process_payment]
sales_agent.tools = [check_inventory, create_quote, process_order]
# NOT: both agents get all 6 tools

Latency Reduction

• Prefer single-agent solutions when possible
• Use async operations for I/O-bound tasks
• Implement request coalescing
• Monitor and optimize hot paths

Common Anti-Patterns

1. Over-Decomposition

Problem: Too many agents for simple tasks creates overhead

Solution: Start simple, add agents only when complexity demands it

2. Circular Handoffs

Problem: Agent A → Agent B → Agent A creates loops

Solution: Design clear hierarchy or state-based termination

3. Stateless Agents

Problem: Agents lose context across handoffs

Solution: Implement proper state management and context passing

4. Unclear Boundaries

Problem: Overlapping agent responsibilities cause conflicts

Solution: Define explicit agent domains and decision criteria

Evaluation & Testing

Key Metrics

Agent-Level:

• Task completion rate
• Average response time
• Tool usage efficiency
• Handoff accuracy

System-Level:

• End-to-end success rate
• Total latency
• Cost per interaction
• User satisfaction scores

Testing Strategy

Unit Testing:

# Test individual agent behaviors
def test_refund_agent():
    result = refund_agent.process(valid_refund_request)
    assert result.status == "approved"
    assert result.amount > 0

Integration Testing:

# Test agent handoffs
def test_triage_to_refund():
    initial_state = {"request": "I want a refund"}
    final_state = orchestrator.run(initial_state)
    assert final_state.handling_agent == "refund_agent"
    assert final_state.completed == True

End-to-End Testing:

# Test full user journeys
def test_customer_journey():
    scenarios = load_test_scenarios()
    for scenario in scenarios:
        result = system.execute(scenario)
        assert result.meets_requirements()

Production Deployment

Monitoring

Essential Observability:

• Agent invocation traces
• Handoff decision logs
• Tool call success rates
• Error patterns and frequencies
• Latency distributions

Tools:

• AgentKit built-in evaluation suite
• Custom logging to centralized system
• Real-time alerting on failures
• Performance dashboards

Security

Agent Security:

• Scope tools to minimum required permissions
• Validate all tool inputs
• Sanitize user inputs before agent processing
• Implement rate limiting per agent
• Audit trail for all agent actions

Data Protection:

• Never expose sensitive data in prompts unnecessarily
• Use secure credential management
• Encrypt state/context storage
• Implement PII detection and masking

Scaling Strategies

Horizontal Scaling:

• Deploy agent instances across multiple servers
• Use load balancing for agent requests
• Implement agent pools for high-volume scenarios

Vertical Optimization:

• Profile and optimize slow agents
• Use caching strategically
• Batch similar requests
• Upgrade to more powerful models selectively

Code Examples

Basic Agent Structure

from openai import OpenAI

client = OpenAI()

# Define specialized agent
support_agent = {
    "name": "Technical Support Agent",
    "model": "gpt-4o",
    "instructions": """You are a technical support specialist.
    Help users troubleshoot technical issues.
    If issue requires refund, hand off to refund agent.
    If issue is sales-related, hand off to sales agent.""",
    "tools": [
        {"type": "function", "function": troubleshooting_guide},
        {"type": "function", "function": escalate_to_human}
    ]
}

Handoff Implementation

def execute_agent_workflow(initial_request):
    current_agent = triage_agent
    context = {"request": initial_request, "history": []}

    while not is_complete(context):
        # Execute current agent
        response = client.chat.completions.create(
            model=current_agent.model,
            messages=build_messages(context, current_agent),
            tools=current_agent.tools
        )

        # Check for handoff
        next_agent = determine_handoff(response)
        if next_agent:
            context["history"].append({
                "from": current_agent.name,
                "to": next_agent.name
            })
            current_agent = next_agent
        else:
            context["result"] = response
            break

    return context

Integration with Other Systems

With Claude SDK

Use AgentKit for OpenAI-based workflows, Claude SDK for Anthropic-based workflows, and MCP to bridge data sources to both.

With LangGraph

LangGraph provides more fine-grained control flow. Use AgentKit for simpler workflows, LangGraph for complex state machines.

With MCP

AgentKit agents can consume MCP servers as tools, standardizing data source connections.

Migration Guide

From Swarm to Agents SDK

Key Changes:

1. Replace swarm.run() with Agents SDK orchestration
2. Update agent definitions to new schema
3. Migrate handoff logic to production patterns
4. Add proper error handling
5. Implement monitoring and observability

Timeline: Swarm is maintenance-only. Migrate all production code by Q2 2025.

Decision Framework

Use OpenAI AgentKit when:

• Building on OpenAI models (GPT-4, etc.)
• Need visual agent builder for non-technical stakeholders
• Want integrated evaluation and monitoring
• Prefer managed platform over open-source frameworks

Consider alternatives when:

• Need model flexibility (use LangGraph)
• Require complex state machines (use LangGraph)
• Want full control over orchestration (use custom solution)
• Working with Anthropic models (use Claude SDK)

Resources

Official Documentation:

• AgentKit Platform: https://platform.openai.com/docs/agents
• Agents SDK: https://github.com/openai/agents-sdk
• Best Practices: https://platform.openai.com/docs/guides/agents-best-practices

Community:

• OpenAI Developer Forum
• AgentKit Discord
• GitHub Discussions

Final Principles

1. Simplicity First - Start with single agents, add complexity only when needed
2. Specialization Over Generalization - Focused agents perform better
3. Explicit Handoffs - Clear routing beats implicit behavior
4. Production-Ready - Use Agents SDK, not Swarm, for real applications
5. Measure Everything - Observability is critical for multi-agent systems

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This skill ensures you build robust, scalable, production-ready multi-agent systems using OpenAI's latest platform capabilities in 2025.