adk-python/contributing/samples/workflow_triage

Workflow Triage Sample

This sample demonstrates how to build a multi-agent workflow that intelligently triages incoming requests and delegates them to appropriate specialized agents.

Overview

The workflow consists of three main components:

1. Execution Manager Agent (agent.py) - Analyzes user input and determines which execution agents are relevant
2. Plan Execution Agent - Sequential agent that coordinates execution and summarization
3. Worker Execution Agents (execution_agent.py) - Specialized agents that execute specific tasks in parallel

Architecture

Execution Manager Agent (root_agent)

• Model: gemini-2.5-flash
• Name: execution_manager_agent
• Role: Analyzes user requests and updates the execution plan
• Tools: update_execution_plan - Updates which execution agents should be activated
• Sub-agents: Delegates to plan_execution_agent for actual task execution
• Clarification: Asks for clarification if user intent is unclear before proceeding

Plan Execution Agent

• Type: SequentialAgent
• Name: plan_execution_agent
• Components:
  • worker_parallel_agent (ParallelAgent) - Runs relevant agents in parallel
  • execution_summary_agent - Summarizes the execution results

Worker Agents

The system includes two specialized execution agents that run in parallel:

• Code Agent (code_agent): Handles code generation tasks
  • Uses before_agent_callback_check_relevance to skip if not relevant
  • Output stored in code_agent_output state key
• Math Agent (math_agent): Performs mathematical calculations
  • Uses before_agent_callback_check_relevance to skip if not relevant
  • Output stored in math_agent_output state key

Execution Summary Agent

• Model: gemini-2.5-flash
• Name: execution_summary_agent
• Role: Summarizes outputs from all activated agents
• Dynamic Instructions: Generated based on which agents were activated
• Content Inclusion: Set to "none" to focus on summarization

Key Features

• Dynamic Agent Selection: Automatically determines which agents are needed based on user input
• Parallel Execution: Multiple relevant agents can work simultaneously via ParallelAgent
• Relevance Filtering: Agents skip execution if they're not relevant to the current state using callback mechanism
• Stateful Workflow: Maintains execution state through ToolContext
• Execution Summarization: Automatically summarizes results from all activated agents
• Sequential Coordination: Uses SequentialAgent to ensure proper execution flow

Usage

The workflow follows this pattern:

1. User provides input to the root agent (execution_manager_agent)
2. Manager analyzes the request and identifies relevant agents (code_agent, math_agent)
3. If user intent is unclear, manager asks for clarification before proceeding
4. Manager updates the execution plan using update_execution_plan
5. Control transfers to plan_execution_agent
6. worker_parallel_agent (ParallelAgent) runs only relevant agents based on the updated plan
7. execution_summary_agent summarizes the results from all activated agents

Example Queries

Vague requests requiring clarification:

> hi
> Help me do this.

The root agent (execution_manager_agent) will greet the user and ask for clarification about their specific task.

Math-only requests:

> What's 1+1?

Only the math_agent executes while code_agent is skipped.

Multi-domain requests:

> What's 1+11? Write a python function to verify it.

Both code_agent and math_agent execute in parallel, followed by summarization.

Available Execution Agents

• code_agent - For code generation and programming tasks
• math_agent - For mathematical computations and analysis

Implementation Details

• Uses Google ADK agents framework
• Implements callback-based relevance checking via before_agent_callback_check_relevance
• Maintains state through ToolContext and state keys
• Supports parallel agent execution with ParallelAgent
• Uses SequentialAgent for coordinated execution flow
• Dynamic instruction generation for summary agent based on activated agents
• Agent outputs stored in state with {agent_name}_output keys