Feature Request: Allow Workflow as Sub-Agents in ADK 2.0

[ruben-cid]

Workflow as Sub-Agent

Target: ADK 2.0 (Beta) — enable Workflow in LlmAgent.sub_agents for conversation delegation.

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Problem

ADK 2.0 only supports Workflow → Agent (via ctx.run_node()). The inverse — Agent → Workflow — is not possible. This forces an all-or-nothing adoption: either convert everything to workflows or lose deterministic pipelines.

Cost impact: Workflow-root loops generate 7 events/3 LLM calls for data gathering. Sub-agent delegation would reduce to 5 events/2 LLM calls (30% reduction via event compaction on Agent Runtime).

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Proposed Solution

Allow Workflow as a sub-agent via delegation:

from google.adk import LlmAgent, Workflow
from google.adk.workflow import node

@node(rerun_on_resume=True)
async def data_collection_workflow(ctx, request):
    amount = await ctx.run_node(request_amount_node)  # HITL via RequestInput
    date = await ctx.run_node(request_date_node)
    return {"amount": amount, "date": date}

expense_agent = LlmAgent(
    name="expense_agent",
    instruction="Delegate to collect_data if fields missing.",
    sub_agents=[
        Workflow(name="collect_data", edges=[("START", data_collection_workflow)])
    ],
)

Benefits:

• Conversation delegation with HITL support (RequestInput)
• Workflow events scoped/contained for compaction
• Hybrid architecture: agents orchestrate, workflows handle determinism

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Representative Examples

1. Guided Form Collection

Main agent identifies required fields; workflow deterministically collects each with validation.

@node(rerun_on_resume=True)
async def onboarding_workflow(ctx, user_request):
    email = await ctx.run_node(collect_email_node)      # Validate format
    company = await ctx.run_node(collect_company_node)  # Check against DB
    role = await ctx.run_node(collect_role_node)        # HITL selection
    return {"email": email, "company": company, "role": role}

onboarding_agent = LlmAgent(
    name="onboarding_agent",
    instruction="Welcome users. If profile incomplete, delegate to onboarding_workflow.",
    sub_agents=[Workflow(name="onboarding_workflow", edges=[("START", onboarding_workflow)])],
)

2. Compliance Verification

Main agent prepares submission; workflow runs deterministic checks with human approval gates.

@node(rerun_on_resume=True)
async def compliance_workflow(ctx, submission):
    auto_check = await ctx.run_node(automated_policy_check)
    if auto_check.risk_score > 0.7:
        approval = await ctx.run_node(request_manager_approval)  # HITL
    return {"approved": auto_check.passed and approval.value == "yes"}

hr_agent = LlmAgent(
    name="hr_agent",
    instruction="Process leave requests. Delegate to compliance_workflow for policy checks.",
    sub_agents=[Workflow(name="compliance_workflow", edges=[("START", compliance_workflow)])],
)

3. Multi-Step Configuration

Agent understands intent; workflow guides through deterministic setup with confirmation steps.

@node(rerun_on_resume=True)
async def setup_workflow(ctx, intent):
    region = await ctx.run_node(select_region_node)      # HITL: dropdown
    tier = await ctx.run_node(select_tier_node)        # HITL: pricing
    confirm = await ctx.run_node(confirm_provision_node) # HITL: yes/no
    return {"region": region, "tier": tier, "confirmed": confirm}

infra_agent = LlmAgent(
    name="infra_agent",
    instruction="Provision resources. Delegate to setup_workflow for configuration.",
    sub_agents=[Workflow(name="setup_workflow", edges=[("START", setup_workflow)])],
)

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Comparison

Pattern	LangGraph	ADK 2.0 Current	Proposed
Graph as Root	✅	✅	✅
Agent in Graph	✅	✅	✅
Graph as SubAgent	✅ bind_tools([graph])	❌	🎯

LangGraph allows graphs as callables. ADK needs equivalent: Workflow as delegatable sub-agent.

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Considerations

• Event compaction: Sub-workflow events can be compacted; only I/O top-level
• Resumability: Workflow checkpoints within parent session

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Docs: Dynamic Workflows | Graph-based Workflows