--- summary: "How active-run steering queues messages at runtime boundaries" read_when: - Explaining how steer behaves while an agent is using tools - Changing active-run queue behavior or runtime steering integration - Comparing steering with followup, collect, and interrupt queue modes title: "Steering queue" --- When a normal prompt arrives while a session run is already streaming, OpenClaw tries to send that prompt into the active runtime by default when the queue mode is `steer`. No config entry and no queue directive are required for that default behavior. Pi and the native Codex app-server harness implement the delivery details differently. ## Runtime boundary Steering does not interrupt a tool call that is already running. Pi checks for queued steering messages at model boundaries: 1. The assistant asks for tool calls. 2. Pi executes the current assistant message's tool-call batch. 3. Pi emits the turn end event. 4. Pi drains queued steering messages. 5. Pi appends those messages as user messages before the next LLM call. This keeps tool results paired with the assistant message that requested them, then lets the next model call see the latest user input. The native Codex app-server harness exposes `turn/steer` instead of Pi's internal steering queue. OpenClaw batches queued prompts for the configured quiet window, then sends a single `turn/steer` request with all collected user input in arrival order. Codex review and manual compaction turns reject same-turn steering. When a runtime cannot accept steering in `steer` mode, OpenClaw waits for the active run to finish before starting the prompt. This page explains queue-mode steering for normal inbound messages when the mode is `steer`. If the mode is `followup` or `collect`, normal messages do not enter this steering path; they wait until the active run finishes. For the explicit `/steer ` command, see [Steer](/tools/steer). ## Modes | Mode | Active-run behavior | Later behavior | | ----------- | ------------------------------------------------------ | ----------------------------------------------------------------------------------- | | `steer` | Steers the prompt into the active runtime when it can. | Waits for the active run to finish if steering is unavailable. | | `followup` | Does not steer. | Runs queued messages later after the active run ends. | | `collect` | Does not steer. | Coalesces compatible queued messages into one later turn after the debounce window. | | `interrupt` | Aborts the active run instead of steering it. | Starts the newest message after aborting. | ## Burst example If four users send messages while the agent is executing a tool call: - With default behavior, the active runtime receives all four messages in arrival order before its next model decision. Pi drains them at the next model boundary; Codex receives them as one batched `turn/steer`. - With `/queue collect`, OpenClaw does not steer. It waits until the active run ends, then creates a followup turn with compatible queued messages after the debounce window. - With `/queue interrupt`, OpenClaw aborts the active run and starts the newest message instead of steering. ## Scope Steering always targets the current active session run. It does not create a new session, change the active run's tool policy, or split messages by sender. In multi-user channels, inbound prompts already include sender and route context, so the next model call can see who sent each message. Use `followup` or `collect` when you want messages to queue by default instead of steering the active run. Use `interrupt` when the newest prompt should replace the active run. ## Debounce `messages.queue.debounceMs` applies to queued `followup` and `collect` delivery. In `steer` mode with the native Codex harness, it also sets the quiet window before sending batched `turn/steer`. For Pi, active steering itself does not use the debounce timer because Pi naturally batches messages until the next model boundary. ## Related - [Command queue](/concepts/queue) - [Steer](/tools/steer) - [Messages](/concepts/messages) - [Agent loop](/concepts/agent-loop)