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delegate_agentic_task

Delegate a multi-step task to a separate agent with its own tool loop, scoped to a working directory, and receive only the final answer. Specify task and directory, plus optional limits.

Instructions

Delegate a multi-step task to a model with its own tool-use loop (read_file, write_file, run_bash) scoped to working_dir. Runs until the model stops calling tools, hits max_iterations, or exceeds timeout_seconds. Returns only the final answer, not the full transcript.

The delegated model gets unattended file/bash access within working_dir for the duration of the call - point it at a directory you're comfortable it can read, write, and execute commands in.

Args: task: The task instruction to give the delegated model. working_dir: Directory the model's tools are scoped to. model: Override just the model string for this call. max_iterations: Stop after this many tool-call rounds. timeout_seconds: Wall-clock budget for the whole task. backend: Named backend from models.json (base_url/model/api_key) to use instead of the default DELEGATE_* env vars. model, if also given, overrides the model within that backend. capture_transcript: Log every model message and tool call/result for later retrieval via get_delegation_transcript, instead of just the final answer. Off by default; useful when comparing models (e.g. a bake-off) rather than for routine use.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYes
modelNo
backendNo
working_dirYes
max_iterationsNo
timeout_secondsNo
capture_transcriptNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description fully carries the behavioral disclosure burden. It clearly states that the delegated model gets unattended read/write/execute access within working_dir, that only the final answer is returned, that there are termination conditions, and that transcript capture is opt-in. This is comprehensive and honest about side effects and limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Despite being long, the description is tightly structured: a core behavior paragraph, a safety warning, then a bulleted Args list. Every sentence earns its place, and the most important info (what it does, termination, permissions) is front-loaded. No fluff or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex delegation tool with 7 parameters, no annotations, and a dangerous access profile, the description covers all critical aspects: scope, termination, access level, return value, optional transcript capture, and backend override. The existence of an output schema is acknowledged but not required to detailed since it says returns only the final answer. Nothing an agent needs to invoke it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description is the only source of parameter meaning. It explains every parameter in the Args block, including the nuanced interplay between model and backend (backend as a base_url/model/api_key bundle, and that `model` overrides within that backend). This fully compensates for the schema's lack of descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a precise verb and resource: 'Delegate a multi-step task to a model with its own tool-use loop...'. It clearly states the operation's scope (working_dir) and distinguishes itself from tools like get_delegation_transcript by explaining that it returns only the final answer, not the full transcript. This is a specific, unambiguous definition that lets an agent know exactly what it does.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains the conditions under which the delegated model stops (no more tool calls, max_iterations, timeout_seconds) and warns about unattended file/bash access. It also suggests capture_transcript for comparison scenarios, indirectly routing to get_delegation_transcript. However, it does not explicitly contrast with delegate_task or state when to choose this tool over that sibling, leaving some inference to the agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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