Building Manager-Specialist Agent Workflows
A guide to multi-agent architectures with OpenAI Agents SDK
1Fundamentals of Agent-Based Solutions
The OpenAI Agents SDK enables creation of multiple AI agents to handle complex problems. A manager agent coordinates the overall task while specialist agents solve specific sub-tasks. This approach distributes responsibilities instead of burdening a single agent. For multi-step tasks like travel planning, each specialist produces solutions in their domain before synthesis into a final plan.
2The Agent-as-Tool Pattern
In this pattern, specialist agents act as tools invoked by the manager. For a family's 10-hour Munich layover, logistics checks and local recommendations are delegated to different specialists. Each solves tasks using their own instructions and tools. The manager simply combines results into a final itinerary. This works best when task boundaries are clear but solution paths aren't predefined.
3Logistics Specialist Agent
A dedicated agent verifies travel plan feasibility against current transportation data. Equipped with WebSearchTool capability, it retrieves real-time information. Limited to max 3 reasoning turns, it automates complex timing calculations. To the manager, it appears as a simple tool interface. The agent's instructions focus exclusively on transportation timing and buffer analysis.
4Local Experience Specialist
The second specialist suggests activities and dining options matching traveler preferences. Using web search capabilities, it filters recommendations based on user constraints like family-friendly requirements. Its instructions emphasize personalization within given parameters. Like all specialists, it exposes a clean tool interface to the manager while handling complex reasoning internally.
5Risk Assessment Module
A third specialist analyzes safety factors and timing buffers to prevent missed connections. It ensures adequate return-to-airport time margins for layover activities. Each specialist has customized model settings including 'reasoning effort' parameters. The risk specialist particularly focuses on temporal contingency planning in dynamic environments.
6Structured Output Design
The system produces structured data conforming to the LayoverPlan schema. This standardizes transportation details, activity lists and timelines in JSON format. Structured output enables easy integration with other applications, such as feeding directly into travel agency CRMs. Output quality depends on each specialist's specialized training and tooling.
7Architectural Advantages
This pattern enables clear responsibility separation in complex workflows. Specialist agents can be updated or replaced independently. The manager doesn't need implementation details about all specialists. Scalability benefits emerge as adding new specialists doesn't break system integrity. Modular design reduces maintenance costs and simplifies debugging processes.
8Future Applications
The agent-as-tool pattern applies to financial modeling, medical diagnosis and customer service domains. Deeply trained specialist agents could become digital counterparts to human expert teams. Advanced OpenAI models (like GPT-5.4) will enhance these systems' performance. Enterprise automation projects will increasingly adopt this architecture for complex business processes.
Original article: Towards Data Science — Yönetici-Uzman Ajan Sistemleri ile Çalışma