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Wei Sun (Jack)
e295feb4c6
Demonstrates intelligent triage system with root planning agent and parallel execution agents. Use session state to store the execution plan and coordinate with other specialized agents. Check out README.md for more details. PiperOrigin-RevId: 793884758
109 lines
4.2 KiB
Markdown
109 lines
4.2 KiB
Markdown
# Workflow Triage Sample
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This sample demonstrates how to build a multi-agent workflow that intelligently triages incoming requests and delegates them to appropriate specialized agents.
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## Overview
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The workflow consists of three main components:
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1. **Execution Manager Agent** (`agent.py`) - Analyzes user input and determines which execution agents are relevant
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2. **Plan Execution Agent** - Sequential agent that coordinates execution and summarization
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3. **Worker Execution Agents** (`execution_agent.py`) - Specialized agents that execute specific tasks in parallel
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## Architecture
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### Execution Manager Agent (`root_agent`)
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- **Model**: gemini-2.5-flash
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- **Name**: `execution_manager_agent`
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- **Role**: Analyzes user requests and updates the execution plan
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- **Tools**: `update_execution_plan` - Updates which execution agents should be activated
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- **Sub-agents**: Delegates to `plan_execution_agent` for actual task execution
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- **Clarification**: Asks for clarification if user intent is unclear before proceeding
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### Plan Execution Agent
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- **Type**: SequentialAgent
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- **Name**: `plan_execution_agent`
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- **Components**:
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- `worker_parallel_agent` (ParallelAgent) - Runs relevant agents in parallel
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- `execution_summary_agent` - Summarizes the execution results
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### Worker Agents
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The system includes two specialized execution agents that run in parallel:
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- **Code Agent** (`code_agent`): Handles code generation tasks
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- Uses `before_agent_callback_check_relevance` to skip if not relevant
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- Output stored in `code_agent_output` state key
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- **Math Agent** (`math_agent`): Performs mathematical calculations
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- Uses `before_agent_callback_check_relevance` to skip if not relevant
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- Output stored in `math_agent_output` state key
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### Execution Summary Agent
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- **Model**: gemini-2.5-flash
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- **Name**: `execution_summary_agent`
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- **Role**: Summarizes outputs from all activated agents
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- **Dynamic Instructions**: Generated based on which agents were activated
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- **Content Inclusion**: Set to "none" to focus on summarization
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## Key Features
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- **Dynamic Agent Selection**: Automatically determines which agents are needed based on user input
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- **Parallel Execution**: Multiple relevant agents can work simultaneously via `ParallelAgent`
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- **Relevance Filtering**: Agents skip execution if they're not relevant to the current state using callback mechanism
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- **Stateful Workflow**: Maintains execution state through `ToolContext`
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- **Execution Summarization**: Automatically summarizes results from all activated agents
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- **Sequential Coordination**: Uses `SequentialAgent` to ensure proper execution flow
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## Usage
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The workflow follows this pattern:
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1. User provides input to the root agent (`execution_manager_agent`)
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2. Manager analyzes the request and identifies relevant agents (`code_agent`, `math_agent`)
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3. If user intent is unclear, manager asks for clarification before proceeding
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4. Manager updates the execution plan using `update_execution_plan`
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5. Control transfers to `plan_execution_agent`
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6. `worker_parallel_agent` (ParallelAgent) runs only relevant agents based on the updated plan
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7. `execution_summary_agent` summarizes the results from all activated agents
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### Example Queries
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**Vague requests requiring clarification:**
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```
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> hi
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> Help me do this.
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```
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The root agent (`execution_manager_agent`) will greet the user and ask for clarification about their specific task.
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**Math-only requests:**
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```
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> What's 1+1?
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```
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Only the `math_agent` executes while `code_agent` is skipped.
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**Multi-domain requests:**
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```
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> What's 1+11? Write a python function to verify it.
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```
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Both `code_agent` and `math_agent` execute in parallel, followed by summarization.
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## Available Execution Agents
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- `code_agent` - For code generation and programming tasks
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- `math_agent` - For mathematical computations and analysis
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## Implementation Details
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- Uses Google ADK agents framework
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- Implements callback-based relevance checking via `before_agent_callback_check_relevance`
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- Maintains state through `ToolContext` and state keys
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- Supports parallel agent execution with `ParallelAgent`
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- Uses `SequentialAgent` for coordinated execution flow
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- Dynamic instruction generation for summary agent based on activated agents
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- Agent outputs stored in state with `{agent_name}_output` keys
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