AI DevelopmentAnalysis5 min readPublished September 29, 2026

Choose the user interface and event trigger your workflow actually needs

Building ChatGPT Plugins Now: Extensions and MCP Events

ChatGPT plugins can now add extensions such as side panels and file viewers, and support proposed MCP Events. What changed for developers building plugins.

DA
Digital Applied Team
Research and practical guidance
CoverageSeptember 29, 2026

ChatGPT plugins can now provide a place to work alongside the conversation and receive events that start work later. Those are two different design choices. Build a panel when the user needs to inspect or edit a stateful object; add an event trigger when a defined change should start a bounded task.

Editorial note: Prepared October 1 as a September 29, 2026 dispatch, using the dated announcements cited below. Later product developments are outside this article’s scope.

Key takeaways
  1. 01
    Use a panel for persistent stateA file preview or editable record can be clearer than another long chat response.
  2. 02
    Events need lifecycle handlingSubscription creation, delivery, cancellation and duplication all need deliberate behavior.
  3. 03
    The specification is proposedChatGPT’s supported implementation is narrower than the complete draft design.

01 — The evidenceWhat extensions add to a plugin

OpenAI’s extension guide describes sidebar homes, interactive panels and file viewers. The recap advertises broad plan availability, but the implementation docs read October 1 still say Free and Go web support is forthcoming and composer mentions are desktop-only. Check the surface you intend to support rather than treating a plan name as proof of identical behavior everywhere.

Choose an extension around an object the user needs to understand. A report with filters, a file that needs a custom viewer or a form with several dependent fields can benefit from a panel. A short answer may not. The purpose is to make the state and available actions visible, not to reproduce your entire application inside a smaller window.

Keep the conversation and panel consistent. If the agent says a draft changed, the panel should show the current draft and indicate whether it is saved. If the user edits the panel, the next agent action should operate on that version. Ambiguous ownership of the current state produces mistakes even when each individual interface looks correct.

02 — Practical implicationsWhat MCP Events does and does not standardize

The MCP triggers and events working group is developing the specification. OpenAI’s implementation guide describes webhook delivery with callback verification, durable subscriptions and the list, subscribe and unsubscribe operations. Its supported integration does not include every draft transport or notification type. Describe it as support for a proposed specification, not a ratified universal standard.

Conversation
The user asks for work
Immediate context

Use a direct tool call when the current request provides the necessary authorization and inputs.

On demand
Panel
The user works with an object
Visible state

Keep edits, saved status and available actions understandable alongside the chat.

Interactive
Event
A defined change starts work
Persistent subscription

Specify the trigger, permitted response and cancellation behavior before subscribing.

Asynchronous

An event says something happened. It does not automatically authorize every action the agent could take in response. A new support item might justify preparing a draft, while sending the reply needs a separate rule. Keep the subscription’s purpose narrow enough that a reviewer can tell whether a delivered event belongs to it.

Current docs describe support in Work chats on web, desktop Work with Cloud selected and dots, subject to workspace controls. Those implementation details were checked October 1; they should not be used to invent a broader launch-day entitlement.

03 — Practical implicationsTreat event delivery as an application protocol

Store enough information to identify a subscription, its owner and the action it can trigger. When a callback arrives, verify it using the documented mechanism and associate it with that subscription before starting work. A payload should be treated as task data, including any text supplied by a third party, rather than as new operating instructions.

Digital Applied implementation checks; exact transport and callback requirements are in the linked OpenAI guide.
ConditionExpected behavior
Repeated eventRecognize the same event and avoid repeating a consequential action.
Revoked accessStop using the connection and explain which work can no longer continue.
Cancelled subscriptionPrevent future delivery from starting the cancelled task.
Malformed payloadReject or quarantine it with a useful diagnostic record.

A test should include both a normal event and an event that arrives during an interruption. Verify whether the worker can resume safely and whether it knows which steps already finished. Keep a record of the event identity, task identity and outcome. Those details are far more useful during an incident than a generic “automation failed” message.

Do not assume that a webhook’s successful receipt means the business task succeeded. Separate acknowledgment of delivery from acceptance of the resulting work. That lets you retry a failed analysis without accidentally repeating a write that already happened.

04 — Practical implicationsCarry the user boundary into background work

Review which identity supplies data and which identity performs actions. An event-triggered task may run when the initiating person is offline, so it needs a clear owner and a way to stop it. Test revocation before connecting sensitive material. Access should not survive merely because a queued task still holds an old instruction.

OpenAI also announced improved creation and discovery tools, plus Sites hosting for supported plugins on specified business and education plans. Those distribution paths do not eliminate the permission design of the underlying integration. A convenient installation flow and a correct authorization model are separate requirements.

Our dots guide covers persistent delegation, while the managed-runtime guide covers application-owned execution. The permission-default guide explains how to constrain both.

05 — Practical implicationsChoose one event and one reviewable result

A useful first release supports a single event that prepares a draft or updates a reversible internal record. Check duplicate delivery, cancellation and account removal before adding external actions. Our AI transformation service helps define that end-to-end acceptance test.

Next step

Prove the subscription lifecycle before expanding automation

Extensions can make work easier to inspect, and events can start it at the right moment. The reliable implementation keeps state, ownership and cancellation visible from the first event to the accepted result.

Agentic AI implementation

Build a workflow you can evaluate and control

Digital Applied helps teams connect AI capabilities to useful work, clear acceptance checks and responsible operating limits.

Task evaluationsCost visibilityControlled access
Start with one task

Define the pilot

  • →Approved source material
  • →A named reviewer
  • →A clear acceptance check
  • →Spending and permission limits
Questions and answers

Practical questions

The cited work is a proposed specification. ChatGPT implements a documented subset with its own support restrictions.
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