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Steering & Queueing

Two interaction patterns let users send messages while the agent is already working: steering redirects the agent mid-turn, and queueing buffers messages for sequential processing after the current turn completes.

Overview

When a session is actively processing a turn, incoming messages can be delivered in one of two modes via the mode field on MessageOptions:

ModeBehaviorUse case
"immediate" (steering)Injected into the current LLM turn"Actually, don't create that file — use a different approach"
"enqueue" (queueing)Queued and processed after the current turn finishes"After this, also fix the tests"
sequenceDiagram
    participant U as User
    participant S as Session
    participant LLM as Agent

    U->>S: send({ prompt: "Refactor auth" })
    S->>LLM: Turn starts

    Note over U,LLM: Agent is busy...

    U->>S: send({ prompt: "Use JWT instead", mode: "immediate" })
    S-->>LLM: Injected into current turn (steering)

    U->>S: send({ prompt: "Then update the docs", mode: "enqueue" })
    S-->>S: Queued for next turn

    LLM->>S: Turn completes (incorporates steering)
    S->>LLM: Processes queued message
    LLM->>S: Turn completes

Steering (Immediate Mode)

Steering sends a message that is injected directly into the agent's current turn. The agent sees the message in real time and adjusts its response accordingly — useful for course-correcting without aborting the turn.

Node.js / TypeScript 
import { CopilotClient } from "@github/copilot-sdk";

const client = new CopilotClient();
await client.start();

const session = await client.createSession({
    model: "gpt-4.1",
    onPermissionRequest: async () => ({ kind: "approved" }),
});

// Start a long-running task
const msgId = await session.send({
    prompt: "Refactor the authentication module to use sessions",
});

// While the agent is working, steer it
await session.send({
    prompt: "Actually, use JWT tokens instead of sessions",
    mode: "immediate",
});
Python 
from copilot import CopilotClient
from copilot.session import PermissionRequestResult

async def main():
    client = CopilotClient()
    await client.start()

    session = await client.create_session(
        on_permission_request=lambda req, inv: PermissionRequestResult(kind="approved"),
        model="gpt-4.1",
    )

    # Start a long-running task
    msg_id = await session.send({
        "prompt": "Refactor the authentication module to use sessions",
    })

    # While the agent is working, steer it
    await session.send({
        "prompt": "Actually, use JWT tokens instead of sessions",
        "mode": "immediate",
    })

    await client.stop()
Go 
package main

import (
    "context"
    "log"
    copilot "github.com/github/copilot-sdk/go"
)

func main() {
    ctx := context.Background()
    client := copilot.NewClient(nil)
    if err := client.Start(ctx); err != nil {
        log.Fatal(err)
    }
    defer client.Stop()

    session, err := client.CreateSession(ctx, &copilot.SessionConfig{
        Model: "gpt-4.1",
        OnPermissionRequest: func(req copilot.PermissionRequest, inv copilot.PermissionInvocation) (copilot.PermissionRequestResult, error) {
            return copilot.PermissionRequestResult{Kind: copilot.PermissionRequestResultKindApproved}, nil
        },
    })
    if err != nil {
        log.Fatal(err)
    }

    // Start a long-running task
    _, err = session.Send(ctx, copilot.MessageOptions{
        Prompt: "Refactor the authentication module to use sessions",
    })
    if err != nil {
        log.Fatal(err)
    }

    // While the agent is working, steer it
    _, err = session.Send(ctx, copilot.MessageOptions{
        Prompt: "Actually, use JWT tokens instead of sessions",
        Mode:   "immediate",
    })
    if err != nil {
        log.Fatal(err)
    }
}
.NET 
using GitHub.Copilot.SDK;

await using var client = new CopilotClient();
await using var session = await client.CreateSessionAsync(new SessionConfig
{
    Model = "gpt-4.1",
    OnPermissionRequest = (req, inv) =>
        Task.FromResult(new PermissionRequestResult { Kind = PermissionRequestResultKind.Approved }),
});

// Start a long-running task
var msgId = await session.SendAsync(new MessageOptions
{
    Prompt = "Refactor the authentication module to use sessions"
});

// While the agent is working, steer it
await session.SendAsync(new MessageOptions
{
    Prompt = "Actually, use JWT tokens instead of sessions",
    Mode = "immediate"
});
Java 
import com.github.copilot.sdk.CopilotClient;
import com.github.copilot.sdk.events.*;
import com.github.copilot.sdk.json.*;

try (var client = new CopilotClient()) {
    client.start().get();

    var session = client.createSession(
        new SessionConfig()
            .setModel("gpt-4.1")
            .setOnPermissionRequest(PermissionHandler.APPROVE_ALL)
    ).get();

    // Start a long-running task
    session.send(new MessageOptions()
        .setPrompt("Refactor the authentication module to use sessions")
    ).get();

    // While the agent is working, steer it
    session.send(new MessageOptions()
        .setPrompt("Actually, use JWT tokens instead of sessions")
        .setMode("immediate")
    ).get();
}

How Steering Works Internally

  1. The message is added to the runtime's ImmediatePromptProcessor queue
  2. Before the next LLM request within the current turn, the processor injects the message into the conversation
  3. The agent sees the steering message as a new user message and adjusts its response
  4. If the turn completes before the steering message is processed, it is automatically moved to the regular queue for the next turn

Note: Steering messages are best-effort within the current turn. If the agent has already committed to a tool call, the steering takes effect after that call completes but still within the same turn.

Queueing (Enqueue Mode)

Queueing buffers messages to be processed sequentially after the current turn finishes. Each queued message starts its own full turn. This is the default mode — if you omit mode, the SDK uses "enqueue".

Node.js / TypeScript 
import { CopilotClient } from "@github/copilot-sdk";

const client = new CopilotClient();
await client.start();

const session = await client.createSession({
    model: "gpt-4.1",
    onPermissionRequest: async () => ({ kind: "approved" }),
});

// Send an initial task
await session.send({ prompt: "Set up the project structure" });

// Queue follow-up tasks while the agent is busy
await session.send({
    prompt: "Add unit tests for the auth module",
    mode: "enqueue",
});

await session.send({
    prompt: "Update the README with setup instructions",
    mode: "enqueue",
});

// Messages are processed in FIFO order after each turn completes
Python 
from copilot import CopilotClient
from copilot.session import PermissionRequestResult

async def main():
    client = CopilotClient()
    await client.start()

    session = await client.create_session(
        on_permission_request=lambda req, inv: PermissionRequestResult(kind="approved"),
        model="gpt-4.1",
    )

    # Send an initial task
    await session.send({"prompt": "Set up the project structure"})

    # Queue follow-up tasks while the agent is busy
    await session.send({
        "prompt": "Add unit tests for the auth module",
        "mode": "enqueue",
    })

    await session.send({
        "prompt": "Update the README with setup instructions",
        "mode": "enqueue",
    })

    # Messages are processed in FIFO order after each turn completes
    await client.stop()
Go 
package main

import (
	"context"
	copilot "github.com/github/copilot-sdk/go"
)

func main() {
	ctx := context.Background()
	client := copilot.NewClient(nil)
	client.Start(ctx)

	session, _ := client.CreateSession(ctx, &copilot.SessionConfig{
		Model: "gpt-4.1",
		OnPermissionRequest: func(req copilot.PermissionRequest, inv copilot.PermissionInvocation) (copilot.PermissionRequestResult, error) {
			return copilot.PermissionRequestResult{Kind: copilot.PermissionRequestResultKindApproved}, nil
		},
	})

	session.Send(ctx, copilot.MessageOptions{
		Prompt: "Set up the project structure",
	})

	session.Send(ctx, copilot.MessageOptions{
		Prompt: "Add unit tests for the auth module",
		Mode:   "enqueue",
	})

	session.Send(ctx, copilot.MessageOptions{
		Prompt: "Update the README with setup instructions",
		Mode:   "enqueue",
	})
}

// Send an initial task
session.Send(ctx, copilot.MessageOptions{
    Prompt: "Set up the project structure",
})

// Queue follow-up tasks while the agent is busy
session.Send(ctx, copilot.MessageOptions{
    Prompt: "Add unit tests for the auth module",
    Mode:   "enqueue",
})

session.Send(ctx, copilot.MessageOptions{
    Prompt: "Update the README with setup instructions",
    Mode:   "enqueue",
})

// Messages are processed in FIFO order after each turn completes
.NET 
using GitHub.Copilot.SDK;

public static class QueueingExample
{
    public static async Task Main()
    {
        await using var client = new CopilotClient();
        await using var session = await client.CreateSessionAsync(new SessionConfig
        {
            Model = "gpt-4.1",
            OnPermissionRequest = (req, inv) =>
                Task.FromResult(new PermissionRequestResult { Kind = PermissionRequestResultKind.Approved }),
        });

        await session.SendAsync(new MessageOptions
        {
            Prompt = "Set up the project structure"
        });

        await session.SendAsync(new MessageOptions
        {
            Prompt = "Add unit tests for the auth module",
            Mode = "enqueue"
        });

        await session.SendAsync(new MessageOptions
        {
            Prompt = "Update the README with setup instructions",
            Mode = "enqueue"
        });
    }
}

// Send an initial task
await session.SendAsync(new MessageOptions
{
    Prompt = "Set up the project structure"
});

// Queue follow-up tasks while the agent is busy
await session.SendAsync(new MessageOptions
{
    Prompt = "Add unit tests for the auth module",
    Mode = "enqueue"
});

await session.SendAsync(new MessageOptions
{
    Prompt = "Update the README with setup instructions",
    Mode = "enqueue"
});

// Messages are processed in FIFO order after each turn completes
Java 
import com.github.copilot.sdk.CopilotClient;
import com.github.copilot.sdk.events.*;
import com.github.copilot.sdk.json.*;

try (var client = new CopilotClient()) {
    client.start().get();

    var session = client.createSession(
        new SessionConfig()
            .setModel("gpt-4.1")
            .setOnPermissionRequest(PermissionHandler.APPROVE_ALL)
    ).get();

    // Send an initial task
    session.send(new MessageOptions().setPrompt("Set up the project structure")).get();

    // Queue follow-up tasks while the agent is busy
    session.send(new MessageOptions()
        .setPrompt("Add unit tests for the auth module")
        .setMode("enqueue")
    ).get();

    session.send(new MessageOptions()
        .setPrompt("Update the README with setup instructions")
        .setMode("enqueue")
    ).get();

    // Messages are processed in FIFO order after each turn completes
}

How Queueing Works Internally

  1. The message is added to the session's itemQueue as a QueuedItem
  2. When the current turn completes and the session becomes idle, processQueuedItems() runs
  3. Items are dequeued in FIFO order — each message triggers a full agentic turn
  4. If a steering message was pending when the turn ended, it is moved to the front of the queue
  5. Processing continues until the queue is empty, then the session emits an idle event

Combining Steering and Queueing

You can use both patterns together in a single session. Steering affects the current turn while queued messages wait for their own turns:

Node.js / TypeScript 
const session = await client.createSession({
    model: "gpt-4.1",
    onPermissionRequest: async () => ({ kind: "approved" }),
});

// Start a task
await session.send({ prompt: "Refactor the database layer" });

// Steer the current work
await session.send({
    prompt: "Make sure to keep backwards compatibility with the v1 API",
    mode: "immediate",
});

// Queue a follow-up for after this turn
await session.send({
    prompt: "Now add migration scripts for the schema changes",
    mode: "enqueue",
});
Python 
session = await client.create_session(
    on_permission_request=lambda req, inv: PermissionRequestResult(kind="approved"),
    model="gpt-4.1",
)

# Start a task
await session.send({"prompt": "Refactor the database layer"})

# Steer the current work
await session.send({
    "prompt": "Make sure to keep backwards compatibility with the v1 API",
    "mode": "immediate",
})

# Queue a follow-up for after this turn
await session.send({
    "prompt": "Now add migration scripts for the schema changes",
    "mode": "enqueue",
})

Choosing Between Steering and Queueing

ScenarioPatternWhy
Agent is going down the wrong pathSteeringRedirects the current turn without losing progress
You thought of something the agent should also doQueueingDoesn't disrupt current work; runs next
Agent is about to make a mistakeSteeringIntervenes before the mistake is committed
You want to chain multiple tasksQueueingFIFO ordering ensures predictable execution
You want to add context to the current taskSteeringAgent incorporates it into its current reasoning
You want to batch unrelated requestsQueueingEach gets its own full turn with clean context

Building a UI with Steering & Queueing

Here's a pattern for building an interactive UI that supports both modes:

import { CopilotClient, CopilotSession } from "@github/copilot-sdk";

interface PendingMessage {
    prompt: string;
    mode: "immediate" | "enqueue";
    sentAt: Date;
}

class InteractiveChat {
    private session: CopilotSession;
    private isProcessing = false;
    private pendingMessages: PendingMessage[] = [];

    constructor(session: CopilotSession) {
        this.session = session;

        session.on((event) => {
            if (event.type === "session.idle") {
                this.isProcessing = false;
                this.onIdle();
            }
            if (event.type === "assistant.message") {
                this.renderMessage(event);
            }
        });
    }

    async sendMessage(prompt: string): Promise<void> {
        if (!this.isProcessing) {
            this.isProcessing = true;
            await this.session.send({ prompt });
            return;
        }

        // Session is busy — let the user choose how to deliver
        // Your UI would present this choice (e.g., buttons, keyboard shortcuts)
    }

    async steer(prompt: string): Promise<void> {
        this.pendingMessages.push({
            prompt,
            mode: "immediate",
            sentAt: new Date(),
        });
        await this.session.send({ prompt, mode: "immediate" });
    }

    async enqueue(prompt: string): Promise<void> {
        this.pendingMessages.push({
            prompt,
            mode: "enqueue",
            sentAt: new Date(),
        });
        await this.session.send({ prompt, mode: "enqueue" });
    }

    private onIdle(): void {
        this.pendingMessages = [];
        // Update UI to show session is ready for new input
    }

    private renderMessage(event: unknown): void {
        // Render assistant message in your UI
    }
}

API Reference

MessageOptions

LanguageFieldTypeDefaultDescription
Node.jsmode"enqueue" \| "immediate""enqueue"Message delivery mode
PythonmodeLiteral["enqueue", "immediate"]"enqueue"Message delivery mode
GoModestring"enqueue"Message delivery mode
.NETModestring?"enqueue"Message delivery mode

Delivery Modes

ModeEffectDuring active turnDuring idle
"enqueue"Queue for next turnWaits in FIFO queueStarts a new turn immediately
"immediate"Inject into current turnInjected before next LLM callStarts a new turn immediately

Note: When the session is idle (not processing), both modes behave identically — the message starts a new turn immediately.

Best Practices

  1. Default to queueing — Use "enqueue" (or omit mode) for most messages. It's predictable and doesn't risk disrupting in-progress work.

  2. Reserve steering for corrections — Use "immediate" when the agent is actively doing the wrong thing and you need to redirect it before it goes further.

  3. Keep steering messages concise — The agent needs to quickly understand the course correction. Long, complex steering messages may confuse the current context.

  4. Don't over-steer — Multiple rapid steering messages can degrade turn quality. If you need to change direction significantly, consider aborting the turn and starting fresh.

  5. Show queue state in your UI — Display the number of queued messages so users know what's pending. Listen for idle events to clear the display.

  6. Handle the steering-to-queue fallback — If a steering message arrives after the turn completes, it's automatically moved to the queue. Design your UI to reflect this transition.

See Also

❮Custom SkillsStreaming Session Events❯

Can you improve this documentation? These fine people already did:
Brett Cannon, Bruno Borges & Patrick NikoletichEdit on GitHub

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