Master AI Agent Orchestration: Routines and Seamless Handoffs for Superior Performance

Want to manage complex AI workflows with ease and efficiency? This guide explores the power of agent orchestration, using routines and handoffs to create robust and controllable AI systems. Learn how to implement these strategies to tackle diverse flows and boost your AI's capabilities.

Simplify with Routines: Define Clear AI Agent Task Sequences

• A routine is a structured set of instructions that guides an AI agent toward a specific goal.
• Think of routines as the "playbook" your agent follows.
• Each routine includes a natural language system prompt and the necessary tools for execution.

Example: Customer Service Routine

Imagine a customer service agent that needs to triage issues, offer solutions, and process refunds. A routine for this agent could look something like the below:

• Guide the agent on its responsibilities:

  You are a customer support agent for ACME Inc. Always answer in a sentence or less.
  Follow the following routine with the user:
  1. First, ask probing questions and understand the user's problem deeper, unless the user has already provided a reason.
  2. Propose a fix (make one up).
  3. ONLY if not satisfied, offer a refund.
  4. If accepted, search for the ID and then execute refund.
  

• Equip your AI agent with tools for the job:
  • look_up_item(search_query): Finds the item ID based on a description or keywords.
  • execute_refund(item_id, reason): Processes a refund for a given item ID and reason.

Executing Routines: A Step-by-Step Approach

With our customer service routine defined, you can execute these steps, by creating a loop that carries out the following:

1. Gets user input.
2. Appends user message to messages.
3. Calls the model.
4. Appends model response to messages.

This keeps the customer service agent on track to resolve any issues.

Supercharge Your Agents: Integrate Tools with Function Schemas

• Tools extend an agent's capabilities, such as item lookup or refund processing.
• Convert Python functions into function schemas for seamless integration with language models.
• A function schema defines the function's name, description, parameters, and required arguments.

Streamline Tool Integration with this handy function_to_schema Implementation

import inspect

def function_to_schema (func) -> dict:
  """
  This great helper function converts a Python routine into the corresponding function schema.
  """
  type_map = {
      str: "string",
      int: "integer",
      float: "number",
      bool: "boolean",
      list: "array",
      dict: "object",
      type ( None): "null",
  }

  try:
      signature = inspect.signature(func)
  except ValueError as e:
      raise ValueError (
          f "Failed to get signature for function { func. __name__}: {str (e)} "
      )

  parameters = {}
  for param in signature.parameters.values():
      try:
          param_type = type_map.get(param.annotation, "string")
      except KeyError as e:
          raise KeyError (
              f "Unknown type annotation { param.annotation} for parameter { param.name}: {str (e)} "
          )
      parameters[param.name] = { "type": param_type}

  required = [
      param.name
      for param in signature.parameters.values()
      if param.default == inspect._empty
  ]

  return {
      "type": "function",
      "function": {
          "name": func. __name__,
          "description": (func. __doc__ or "").strip(),
          "parameters": {
              "type": "object",
              "properties": parameters,
              "required": required,
          },
      },
  }

Dynamic Handoffs: Seamlessly Transfer Conversations Between AI Agents

• When a single routine becomes too complex, divide tasks among specialized agents.
• Implement handoffs to transfer conversations between agents, ensuring continuity and expertise.
• Each agent possesses knowledge of the prior conversation.

Implement Agent Handoff with a transfer_to_XXX Function

1. Equip your agents with handoff functions (e.g., transfer_to_refunds()).
2. The agent can use these functions to signal the need for a handoff.
3. Returning an Agent object from a tool call triggers a transfer to the specified agent.

Python Agent Class

from pydantic import BaseModel
from typing import Optional

class Agent ( BaseModel):
    name: str = "Agent"
    model: str = "gpt-4o-mini"
    instructions: str = "You are a helpful Agent"
    tools: list = []

Takeaways: Elevate AI Agent Performance with Orchestration

By implementing routines and handoffs, you will improve AI agent capabilities:

• Clarity and Control: Routines provide structured guidance for AI agents.
• Flexibility and Scalability: Handoffs enable seamless transitions between specialized agents.
• Improved User Experience: Intelligent routing ensures users receive the right support at the right time.

Start using agent orchestration today to build more powerful, efficient, and user-friendly AI systems!