Understanding Handoffs vs. Agents-as-Tools in OpenAI's Agents SDK

OpenAI's Agents SDK (v0.0.14) offers powerful mechanisms for orchestrating multi-agent workflows. Two pivotal features—Handoffs and Agents-as-Tools—enable distinct modes of inter-agent collaboration. Grasping their differences is essential for designing effective AI systems.

Handoffs: Transferring Control Between Agents

Definition: A Handoff allows one agent to delegate the entire conversation to another agent, effectively transferring control.

Key Characteristics:

• Control Flow: The receiving agent takes over the conversation.
• Context Sharing: The full conversation history is passed to the new agent.
• Use Case: Ideal when a specialized agent should manage the conversation from a certain point onward.

Example:

from agents import Agent, handoff
billing_agent = Agent(name="Billing Agent")
triage_agent = Agent(name="Triage Agent", handoffs=[billing_agent])        

In this setup, the triage_agent can delegate the conversation to the billing_agent when billing-related issues arise.

Agents-as-Tools: Invoking Agents for Specific Tasks

Definition: An agent can be transformed into a callable tool using the as_tool() method, allowing other agents to utilize its capabilities without transferring control.

Key Characteristics:

• Control Flow: The calling agent retains control of the conversation.
• Context Sharing: Only specific input is provided to the tool agent; it doesn't receive the full conversation history.
• Use Case: Suitable for scenarios where an agent needs to perform a specific subtask or computation.

Example:

query_agent = Agent(name="Query Agent")
main_agent = Agent(name="Main Agent", tools=[query_agent.as_tool()])        

• When to Use Handoff: Opt for handoffs when the task requires a dedicated agent to manage the conversation, ensuring that the agent has full context and control.
• When to Use as_tool(): Use as_tool() when the main agent needs to leverage another agent's capabilities for specific tasks without relinquishing control of the conversation.

Understanding these distinctions allows for more effective orchestration of multi-agent workflows, ensuring that each agent operates within its intended scope and context.

#OpenAI #AgentsSDK #GenerativeAI #ToolCalling #AIAgents #FastAPI #AgenticAI