Subagent delegation

When to use

• "Coordinator + specialists" — a parent agent should hand parts of a task to a research agent, math agent, etc.
• "Fan out then collect" — multi-part questions where each part is independent and parallel execution saves wall time.
• "Self-delegation" — one agent breaks complex work into focused sub-tasks for itself.

Two patterns

The two compose — a coordinator can have both.

Pattern 1 — auto-injected run_subtasks

Subtask tools are off by default (tasks=False). Opt in with tasks=TaskConfig(...) and the agent gains:

• run_subtask(task: str) — one isolated sub-task agent.
• run_subtasks(tasks: list[str], parallel: bool = True) — fan out multiple in one tool call (default concurrent).

from autogen.beta import Agent, TaskConfig
from autogen.beta.config import GeminiConfig

config = GeminiConfig(model="gemini-3-flash-preview")

coordinator = Agent(
    "coordinator",
    prompt=(
        "You answer multi-part questions by dispatching run_subtasks "
        "with parallel=True. Use one tool call with every sub-question "
        "packed into the 'tasks' list."
    ),
    config=config,
    tasks=TaskConfig(),  # opt in
)

reply = await coordinator.ask(
    "In one run_subtasks call, answer: "
    "(a) tallest waterfall, (b) Eiffel Tower year, (c) boiling point of nitrogen."
)

TaskConfig controls how the sub-task agents are built:

@dataclass
class TaskConfig:
    config: ModelConfig | None = None    # falls back to parent's config
    prompt: str = "You are a task agent..."
    include_tools: Iterable[str] | None = None   # None = inherit all parent tools
    exclude_tools: Iterable[str] = ()
    extra_tools: Iterable[Callable | Tool] = ()

Common shape — cheaper model for sub-tasks, narrow tool surface:

TaskConfig(
    config=worker_config,                  # smaller model
    prompt="You are a focused worker; one step only.",
    include_tools=["search", "fetch_url"], # don't expose `summarize` to children
)

Sub-task agents are built with tasks=False — they never gain run_subtask tools themselves. Recursive delegation is structurally impossible; no depth limit needed.

Pattern 2 — Agent.as_tool()

Expose a whole agent as a tool the LLM can name and call:

from autogen.beta import Agent
from autogen.beta.config import AnthropicConfig

config = AnthropicConfig(model="claude-sonnet-4-6")

researcher = Agent("researcher", prompt="Provide concise factual findings.", config=config, tools=[search_tool])
writer     = Agent("writer", prompt="Turn research into clear prose.", config=config)

coordinator = Agent(
    "coordinator",
    prompt="First delegate research, then pass findings to the writer.",
    config=config,
    tools=[
        researcher.as_tool(description="Research a topic and return findings."),
        writer.as_tool(description="Write an article. Pass research notes in the context parameter."),
    ],
)

The coordinator's LLM sees task_researcher and task_writer. Each call has two parameters:

• objective (required) — what the sub-task should do.
• context (optional) — relevant info the parent wants to share.

as_tool() accepts:

For more control, use subagent_tool() directly:

from autogen.beta.tools.subagents import subagent_tool

coordinator = Agent("coordinator", config=config, tools=[
    subagent_tool(researcher, description="Research a topic."),
])

Self-delegation via as_tool()

If you want a named self-delegate (sub_task instead of generic run_subtask), give an agent its own tool:

analyst = Agent(
    "analyst",
    prompt=(
        "You have search and sub_task tools. "
        "Only use sub_task when the task has clearly independent parts."
    ),
    config=config,
    tools=[search_tool],
)

analyst.add_tool(
    analyst.as_tool(
        description="Break work into a focused sub-task for independent analysis.",
        name="sub_task",
    )
)

Recursion safety

Self-delegation via as_tool() can recurse — the child has the same sub_task tool, so without a guard the LLM may chain calls indefinitely.

The simplest safe pattern is to prefer the auto-injected run_subtask / run_subtasks path for self-delegation. Sub-tasks spawned that way are constructed with tasks=False, so they have no run_subtask tools and recursion is structurally impossible.

If you genuinely need recursive as_tool() self-delegation, write a tool middleware that increments a depth counter in context.dependencies and short-circuits past a threshold. The subagents module exports subagent_tool, persistent_stream, and StreamFactory from autogen.beta.tools.subagents — verify the current public surface there before relying on a built-in depth-limiting helper.

Sub-task streams

By default, each sub-task gets a fresh MemoryStream — its history is isolated and starts empty. Context flow:

persistent_stream()

When a sub-agent benefits from seeing its prior calls (e.g. avoid repeating searches), give it a stream that persists across invocations within the parent context:

from autogen.beta.tools.subagents import persistent_stream

researcher.as_tool(
    description="Research a topic",
    stream=persistent_stream(),
)

Stores stream id in context.dependencies keyed by f"ag:{agent.name}:stream" and reuses the parent stream's storage backend.

Custom factory

from autogen.beta import Agent, Context
from autogen.beta.streams.redis import RedisStream

def make_redis_stream(agent: Agent, ctx: Context) -> RedisStream:
    return RedisStream(MY_REDIS_URL, prefix=f"ag2:sub:{agent.name}")

researcher.as_tool(description="Research a topic", stream=make_redis_stream)

Going deeper

• Working starter: assets/research_squad.py (mirrors code_examples/05) — covers both run_subtasks(parallel=True) and Agent.as_tool(), with TaskStarted / TaskCompleted lifecycle events.
• Full reference: website/docs/beta/task_delegation.mdx.
• tasks= constructor knob (with KnowledgeConfig, etc.): website/docs/beta/agent_harness.mdx.

Common pitfalls

• Forgetting to opt in — tasks=False is the default. No TaskConfig, no run_subtask tools.
• Expecting sub-tasks to recurse with run_subtask — they can't. Sub-tasks themselves have tasks=False. If you need deeper trees, use Agent.as_tool() self-delegation with a manual depth-counter middleware (see "Recursion safety" above).
• Sharing mutable variables expecting them to merge — concurrent sub-tasks each copy variables; sibling mutations don't propagate. Each sub-task's variable mutations stay local until sync-back on success.
• Treating dependencies as scoped per sub-task — only the top-level dict is copied. Mutable values inside it are still shared by reference. Treat dependencies as read-only inside sub-tasks.
• No description= on as_tool() — the LLM doesn't know when to call it. Required parameter.
• run_subtasks(parallel=False) when work is concurrent — defaults to True for a reason; only set False when later tasks depend on earlier results.
• Confusing task_{agent.name} collisions — pass name= to override if you want shorter names or distinct delegates of the same agent.