Dynamic agent delegation

​

Overview

​

When to use

• A main agent needs to assemble sub-agents with specific skills, knowledge bases, or tools on demand, rather than fixing the sub-agent structure at development time.
• You need to hand off a task to a dynamically created sub-agent within the same session context, where the sub-agent produces the final answer directly.
• You need to match resources from the public Skill Hub, account Skill Spaces, knowledge bases, and built-in tools against a task.

​

Prerequisites

​

Usage

import asyncio

from veadk import Agent, Runner
from veadk.memory.short_term_memory import ShortTermMemory
from veadk.tools.builtin_tools.create_agent import CreateAgentToolset

create_agent = CreateAgentToolset()

agent = Agent(
    name="dynamic_agent_coordinator",
    model_name="doubao-seed-2-1-pro-260628",
    description="A coordinator that assembles experts on demand and delegates execution.",
    instruction="""
You are a dynamic agent coordinator.

For greetings, introductions, or capability questions, answer directly without creating sub-agents.

For tasks that require retrieval, specialized skills, knowledge bases, tool calls, or authoring Python tools:
1. First call collect_resources, deriving 2 to 5 short Skill Hub search keywords from the user's task and passing them via skill_hub_keywords.
2. Design the minimum number of sub-agents from the returned resource catalog. Explicitly include each skill, knowledge base, and built-in tool ref you need in the resources list of the corresponding LLM node.
3. Call create_agents in a single call including every required sub-agent, specifying in handoff_to the agent that should actually complete the user's task.
4. create_agents transfers the task directly to that agent; do not repeat the answer yourself.

If the user explicitly prohibits network, knowledge-base, or external-resource access, skip collect_resources and call create_agents directly with an empty string as collection_id, leaving every LLM node's resources as an empty list.
""",
    tools=[create_agent],
)

runner = Runner(agent=agent, short_term_memory=ShortTermMemory())


async def main():
    response = await runner.run("Search and summarize the latest public AgentKit materials, giving three conclusions and sources.")
    print(response)


if __name__ == "__main__":
    asyncio.run(main())

​

Resource collection and matching

​

Offline mode

​

Agent blueprints

​

Reusable identity and task separation

• name and id use concise snake_case capability names such as video_creation_agent, document_translation_agent, or investment_analysis_agent, not names derived from the current deliverable or research subject.
• description and instruction describe generic operations, such as “research the user-specified subject” or “compare the user-specified candidates,” without hard-coding brands, platforms, industries, languages, or topics from the current request.
• task preserves the complete current objective, including specific research subjects, industry information, source or target languages, and other request-specific details.

​

LLM node

​

Ad-hoc Python tools

​

Parameters

​

Limitations

• create_agents must run within an active agent invocation driven by a Runner, otherwise it cannot register sub-agents and complete the handoff.
• Each collect_resources result requires exactly one create_agents call that includes every required sub-agent; after it completes or sets handoff_to, it should not be called again.
• Multiple collect_resources calls within the same session overwrite the previous resource snapshot for that session; snapshots are held in an in-process bounded cache (up to 128), and create_agents reads a snapshot without invalidating it, so the same snapshot can be reused by subsequent calls with the same request fingerprint.
• Dynamically created sub-agents are registered in the agent tree of the current session and are not retained after the session ends.
• python_tools run in the current process without sandbox isolation.