agent_handoff
Delegate a multi-step task (research, composing messages, booking, scheduling) to the full agentic planner. Use when a user ask needs more than a direct answer. The specialist runs synchronously — its response is already shown to the user in real-time. Summarize the OUTCOME in past tense (e.g. 'T...
This record as markdown: /tools/io-github-saloprj-dialogbrain/agent-handoff.md
What agent_handoff does on Dialogbrain
AI agents invoke agent_handoff to trigger actions in Dialogbrain. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
| Parameter | Type | Required | Description |
|---|---|---|---|
mode | string | — | Execution mode: 'sync' (wait for result, default) or 'async' (fire and forget, child runs in background). Async is only available in background/trigger context. |
agent_id | integer | — | Optional ID of another agent in the same workspace to delegate the task to. When set, this becomes cross-agent delegation; the target agent runs with ITS OWN pr |
target_slug | string | — | Optional stable slug of a system-template specialist to delegate to (e.g. 'doc-composer' for the Document Composer). Env-portable alternative to agent_id — reso |
task_description | string | Yes | Plain-language description of what the planner should accomplish. Include everything the planner needs: the user's goal, constraints, and any context already ga |
Parameters from the server's own tool schema.
Why agent_handoff is rated High
This tool triggers an autonomous agentic planner that can perform a wide range of external operations including booking, scheduling, and composing/sending messages. The effects depend entirely on what task is delegated, and the agent runs synchronously with real-time effects. This is an Execute-category tool because it invokes external operations whose consequences depend on arguments.
From the tool's definition Delegate a multi-step task (research, composing messages, booking, scheduling) to the full agentic planner
Attacks that exploit this kind of access
The rule that runs agent_handoff safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Dialogbrain, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For agent_handoff, this is the rule to start with:
agent_handoff stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Dialogbrain, apply this rule, and every agent_handoff call is checked against it from then on.
Questions about agent_handoff
Delegate a multi-step task (research, composing messages, booking, scheduling) to the full agentic planner. Use when a user ask needs more than a direct answer. The specialist runs synchronously — its response is already shown to the user in real-time. Summarize the OUTCOME in past tense (e.g. 'The Media Creator generated your video' or 'The Document Composer failed because...'). Do NOT say 'I will delegate' — the delegation already happened. If status is timeout or error, explain what went wrong and offer to retry. It is categorised as a Execute tool in the Dialogbrain MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
agent_handoff accepts 4 parameters: mode, agent_id, target_slug, task_description. Required: task_description. The full parameter table on this page comes from the server's own tool schema.
Register the Dialogbrain MCP server in PolicyLayer and add a rule for agent_handoff: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Dialogbrain. Nothing to install.
agent_handoff is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the agent_handoff rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for agent_handoff. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
agent_handoff is provided by the Dialogbrain MCP server (https://api.dialogbrain.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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