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+# 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
+1. **Plan Execution Agent** - Sequential agent that coordinates execution and summarization
+1. **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`)
+1. Manager analyzes the request and identifies relevant agents (`code_agent`, `math_agent`)
+1. If user intent is unclear, manager asks for clarification before proceeding
+1. Manager updates the execution plan using `update_execution_plan`
+1. Control transfers to `plan_execution_agent`
+1. `worker_parallel_agent` (ParallelAgent) runs only relevant agents based on the updated plan
+1. `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