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Description
I’m working on a use case where an LLM should first analyze a user query, extract intent and parameters, and then drive a workflow of sub-agents based on that reasoning. I’m trying to confirm the recommended ADK orchestration pattern to accomplish this effectively.
Key Questions
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Architecture Best Practice
What is the recommended pattern for combining LlmAgent-based reasoning with sequential or conditional execution in ADK? -
Parameter Passing
How should user intent and extracted parameters be passed from a parent reasoning agent to workflow agents? -
Instruction / Prompt Design
What is the best practice for structuring instructions so the LLM cleanly delegates tasks to workflow agents? -
Alternatives
Are there other ADK agent types or workflow control patterns better suited for intent-driven execution?
What I Have Already Tried (Without Code)
- Using a LlmAgent as a top-level orchestrator for reasoning followed by delegation
→ Issue: Often answers directly or delegates without consistent parameter extraction
-Using custom instructions to enforce a reasoning-first approach
→ Issue: Parameter passing and control of specific workflow steps remains unreliable
What I’m Looking For
- Recommended ADK architecture for:
- Intent extraction before workflow execution
- Conditional agent selection
- Passing parameters into workflow agents
- Executing only relevant sub-agents
-Guidance or example demonstrating:
- Reasoning agent (intent + parameter extraction)
- Workflow agent (dynamic execution based on extracted insights)
Environment
- Google ADK: Latest release
- Python: 3.12+
- Production context: Healthcare workflow automation requiring dynamic, intent-driven execution
Additional Context
This orchestration pattern appears essential for multi-agent production applications where workflows must be efficient, context-aware, and driven by user input — not static execution sequences.
Documentation or a sample implementation would be extremely valuable for adopters building complex systems on ADK.