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## Thinking Path > - Paperclip is the open source app people use to manage AI agents for work. > - Cloud sets managed feature values for each tenant stack. > - Staging lets admins change experimental features. > - The staging exception also let old tenant false values override Cloud true values. > - This could disable required features such as isolated workspaces. > - This pull request keeps every Cloud-enabled feature on and rejects attempts to disable it. > - The same rule applies to staging, production, and managed stacks without a hostname. ## Linked Issues or Issue Description Refs: #16876 **What happened?** The staging override added in #16876 treats managed true values as editable defaults. A stored tenant false value can disable a feature that Cloud enables. Settings writes also accept attempts to disable Cloud-enabled features. **Expected behavior** If Cloud sets a feature to true, the tenant reads true and cannot turn it off. This applies to every managed stack. Staging can still change allowed experiments that Cloud has not enabled. **Steps to reproduce** 1. Set a managed staging primary hostname and enable isolated workspaces in `PAPERCLIP_MANAGED_CONFIG`. 2. Start with `enableIsolatedWorkspaces: false` in the tenant settings row. 3. Read `/api/instance/settings/experimental`. Before this fix, staging returns false. 4. Try to PATCH that flag to false. After this fix, the API returns 403 without saving, and reads return true with managed control metadata. **Paperclip version or commit** Reproduced against master at `cde944ed75648adfb569e86133adc8b118b55dd6`. ## What Changed - Keep managed true values in the staging read overlay. They override old tenant values and lock the existing UI controls. - Reject attempts to set any Cloud-enabled feature to false before writing settings. - Cover every managed feature in service tests across staging, production, and an absent hostname. Add real-service HTTP tests for isolation on staging and production. - Update operator documentation and the feature and access maps. ## Verification - Confirmed the new service regression fails before the fix: staging returns stored false, and writes can attempt to disable enabled flags. - Passed all six focused settings suites: 168 tests. Passed the final route and overlay suites after adding the HTTP regression: 109 tests. - Passed existing experimental settings UI tests: 54 tests. These cover the existing managed-control behavior. - Passed `node scripts/check-maps.mjs` and `git diff --check`. - Passed `pnpm -r typecheck` and `pnpm build`. The first typecheck used an install that did not honor locked versions and hit an unrelated `@pierre/diffs` API mismatch. Reinstalling from the lockfile resolved it; no lockfile changes are included. - Full CI passed on `f43136812a92ec838a24f0c423270e4fbd997c28`: 55 successful checks, one neutral review check, and two expected skips. This includes typecheck, build, general and serialized tests, runner checks, and E2E shards. [CI run](https://github.com/paperclipai/paperclip/actions/runs/39411907756). - The duplicate local `pnpm test:run` was stopped after the complete CI matrix passed. It is not counted as a completed local full-suite pass. - Greptile reviewed `f43136812a92ec838a24f0c423270e4fbd997c28`: 5/5 with no outstanding findings. The only finding on the first review was the repository rule against new code comments; those comments were removed. ## Risks - Staging users can no longer disable a feature that Cloud enables. This is the intended behavior. - Clients that send a stale full settings form containing false for a Cloud-enabled feature receive 403 and must reload. - No database migration is needed. Stored false values remain in the database, but cannot override an active Cloud true value. - Cloud-disabled staging experiments remain editable. Production restrictions and self-hosted settings remain unchanged. ## Model Used - OpenAI Codex, `gpt-6.1-sol`, with reasoning, repository tools, and test execution. The context window size is not exposed in this session. ## Checklist - [x] I have included a thinking path that traces from project context to this change - [x] I have specified the model used (with version and capability details) - [x] I have checked ROADMAP.md and confirmed this PR does not duplicate planned core work - [x] I have searched GitHub for duplicate or related PRs and linked them above - [x] I have either (a) linked existing issues with `Fixes: #` / `Closes #` / `Refs #` OR (b) described the issue in-PR following the relevant issue template - [x] I have not referenced internal/instance-local Paperclip issues or links (only public GitHub `#NNN` / `github.com/paperclipai/paperclip` URLs) - [x] My branch name describes the change (e.g. `docs/...`, `fix/...`) and contains no internal Paperclip ticket id or instance-derived details - [x] I have run tests locally and they pass - [x] I have added or updated tests where applicable - [x] I have updated relevant documentation to reflect my changes - [x] I have considered and documented any risks above - [x] All Paperclip CI gates are green - [x] Greptile is 5/5 with no open P2s, recommendations, or follow-ups - [x] I will address all Greptile and reviewer comments before requesting merge --------- Co-authored-by: Paperclip <noreply@paperclip.ing>
82 lines
3.6 KiB
Markdown
82 lines
3.6 KiB
Markdown
---
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title: Experimental Features
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summary: What Paperclip experimental features mean for board operators
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---
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Experimental features are opt-in and are provided without compatibility guarantees. They may break, change, or be removed at any time. Use them at your own risk.
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## What "experimental" means
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When a feature is marked experimental, Paperclip is still evaluating the product shape and implementation details.
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- The feature is not part of the stable operator contract yet.
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- UI, API, CLI, behavior, and stored configuration may change as the feature evolves.
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- Paperclip does not promise compatibility, rollback, migration, or long-term support for experimental features.
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If you need stable behavior for an important workflow, do not rely on an experimental feature.
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## Where you enable them
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Board operators enable or disable experiments from **Instance Settings > Experimental** in the app.
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Controls are listed alphabetically within each section. Hosting operators can hide
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controls they manage; empty developer and legacy sections are omitted. Hidden
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controls retain their configured values.
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On Paperclip Cloud staging (`*.staging.paperclip.app`), instance admins can
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view and change experimental settings that Cloud has not enabled. A feature
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enabled by Cloud stays on for every managed stack, including staging, even when
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an older saved tenant value is false. Its control is locked, and attempts to
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disable it through the settings API return `403`. Cloud-disabled experiments
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can still be changed on staging, and saved choices survive reloads. Production Cloud
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keeps its managed values and hosting restrictions. Staging only permits changes
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to an explicit list of feature controls. Cloud access and execution policies,
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including managed-sandbox-only execution, remain managed on staging.
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The CLI exposes the same surface:
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```sh
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pnpm paperclipai instance settings:experimental
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npx paperclipai instance settings:experimental:update --payload-json '{...}'
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```
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Those commands change the same opt-in settings that the UI manages.
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## Chat connectors
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**Chat connectors** is off by default. Enable it to connect a dedicated
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Slack, GitHub, Microsoft Teams, Telegram, or Discord bot to one Paperclip
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agent. The experiment shows chat setup, connection management, agent channels,
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and external task controls.
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When it is off, existing production tool connectors remain available. For
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example, GitHub opens its normal tool connection flow without asking you to
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choose between chat and tools.
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This setting controls visibility. Turning it off does not disconnect an
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existing bot or stop its messages. To stop a connection, pause it from its
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chat connection settings before turning off the experiment.
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## When to use them
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Experimental features are best used when you are:
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- evaluating a new capability before wider rollout
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- testing a non-critical workflow
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- comfortable with behavior changes between releases
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- prepared to stop using the feature if it changes or disappears
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## Operator expectations
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Before enabling an experimental feature:
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- decide whether the workflow can tolerate breakage or churn
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- avoid making the feature a dependency for stable production processes
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- keep the scope small until you understand how the feature behaves in your company
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- watch release notes and docs for changes to the feature contract
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## Related references
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- See [Status Cards](/guides/board-operator/status-cards) for the watched-query summary experiment, refresh policies, and cost model.
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- See the CLI caveat in [Control-Plane Commands](/cli/control-plane-commands).
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- See the repo CLI reference in [`doc/CLI.md`](https://github.com/paperclipai/paperclip/blob/master/doc/CLI.md) when working from the repository.
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