Files
PaperClipAI/packages/shared/src/telemetry/events.ts
T
DottaandPaperclip 10d0555189 fix(interactions): authorize resolvers consistently (#11376)
## Thinking Path

> - Paperclip is the open source app people use to manage AI agents for
work.
> - Issue interactions give agents and people a structured decision
record.
> - Resolver routes used different authorization rules.
> - Some routes blocked valid agents, including task watchdogs with
normal issue access.
> - The API did not show who could resolve a pending interaction.
> - This pull request gives every interaction kind one resolver policy
evaluator.
> - The benefit is a clear decision path with consistent governance and
company isolation.

## Linked Issues or Issue Description

Fixes: #8087

Refs: #7403

Related PR: #11082 proposes board-only confirmation rules. This change
keeps human-only review as an explicit policy.

**What happened?**

Agents could create issue interactions. Some resolver routes still
required board access.

This left valid agent confirmations pending. Task watchdogs could see
the same problem without board identity.

**Expected behavior**

Every interaction kind must use one resolver policy contract.

The contract must support `anyone`, `not_creator`, and `human_only`. It
must also apply all normal governance controls.

**Steps to reproduce**

1. Create a `request_confirmation` interaction as an agent.
2. Resolve it with another authorized agent.
3. Observe the board-only denial.

**Paperclip version or commit**

The problem exists on `master` before this change.

**Deployment mode**

Local development with `pnpm dev`.

## What Changed

- Add canonical policies for `anyone`, `not_creator`, and `human_only`.
- Use one server evaluator for every interaction kind.
- Apply named addressees, company limits, review rules, and task
watchdog scope.
- Charge cross-issue resolutions to the existing per-run action limit.
- Return the effective resolver audience in attention and interaction
data.
- Show the audience, governance choices, and denial reasons in the board
UI.
- Add telemetry, API documents, product documents, and regression
fixtures.
- Add migration provenance for safe legacy behavior.
- Make migration `0218` safe for complete replays and partial prior
runs.

## Product Rules

- An interaction records a response. It does not grant authority for the
next action.
- `anyone` lets any authorized issue participant respond.
- `not_creator` requires a responder other than the interaction creator.
- `human_only` requires an authorized person.
- A named addressee, company policy, or governed action can narrow the
audience.
- These controls cannot widen the audience.
- A task watchdog uses the same rules as an ordinary agent.
- A task watchdog does not receive board authority.
- An agent resolution on another issue uses the shared cross-issue
action limit.
- Legacy pending interactions keep their earlier restrictions.
- The UI shows the effective audience and a permanent denial reason.

## Verification

- `pnpm --filter @paperclipai/db check:migrations`
- `pnpm --filter @paperclipai/db typecheck`
- `pnpm exec vitest run
packages/db/src/issue-thread-interaction-resolver-policy-migration.test.ts`
- The focused PostgreSQL test applies migration `0218` twice.
- The test also completes a partial prior run and preserves existing
provenance.
- The latest GitHub head has 29 successful checks.
- The opt-in Storybook visual check skipped as expected.
- Greptile reports 5/5 with no open comments.

## Risks

- New interaction writes use `anyone` by default.
- Callers must select `not_creator` or `human_only` when they need
stricter review.
- Legacy pending interactions keep the old creator and human
restrictions.
- Migration `0218` fills only missing provenance fields during recovery.
- Cross-issue resolutions can reach the existing action limit.
- The shared evaluator affects every interaction kind.
- Route, service, database, shared contract, and UI tests cover these
rules.

> This work matches the Agent Reviews and Approvals direction in
`ROADMAP.md`. It does not duplicate a planned item.

## Model Used

OpenAI Codex, GPT-5. The runtime does not expose the exact deployment ID
or context window.

The agent used reasoning, repository tools, shell commands, and test
execution.

## 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 linked public issues or described the issue with the
required labels
- [x] I have not referenced internal Paperclip issues or links
- [x] My branch name describes the change and contains no internal
ticket id
- [x] I have run tests locally and they pass
- [x] I have added or updated tests where applicable
- [x] I have updated relevant documentation
- [x] I have considered and documented the risks
- [x] All Paperclip CI gates are green
- [x] Greptile is 5/5 with no open comments
- [x] I will address all Greptile and reviewer comments before
requesting merge

---------

Co-authored-by: Paperclip <noreply@paperclip.ing>
2026-08-16 13:46:50 -05:00

183 lines
6.5 KiB
TypeScript

import type { TelemetryClient } from "./client.js";
import type { EventDimensionsMap } from "./generated/paperclip-telemetry.js";
type RawDimension<T extends string | undefined> = T | (string & {});
function asEventDimension<T extends string>(value: RawDimension<T>): T {
return value as T;
}
export function trackInstallStarted(client: TelemetryClient): void {
client.track("install.started", {});
}
export function trackInstallCompleted(
client: TelemetryClient,
dims: { adapterType: RawDimension<EventDimensionsMap["install.completed"]["adapter_type"]> },
): void {
client.track("install.completed", {
adapter_type: asEventDimension(dims.adapterType),
});
}
export function trackCompanyImported(
client: TelemetryClient,
dims: {
sourceType: RawDimension<EventDimensionsMap["company.imported"]["source_type"]>;
sourceRef: string;
isPrivate: boolean;
},
): void {
const ref = dims.isPrivate ? client.hashPrivateRef(dims.sourceRef) : dims.sourceRef;
client.track("company.imported", {
source_type: asEventDimension(dims.sourceType),
source_ref: ref,
source_ref_hashed: dims.isPrivate,
});
}
export function trackProjectCreated(client: TelemetryClient): void {
client.track("project.created", {});
}
export function trackRoutineCreated(client: TelemetryClient): void {
client.track("routine.created", {});
}
export function trackRoutineRun(
client: TelemetryClient,
dims: {
source: RawDimension<EventDimensionsMap["routine.run"]["source"]>;
status: RawDimension<EventDimensionsMap["routine.run"]["status"]>;
},
): void {
client.track("routine.run", {
source: asEventDimension(dims.source),
status: asEventDimension(dims.status),
});
}
export function trackGoalCreated(
client: TelemetryClient,
dims?: { goalLevel?: RawDimension<EventDimensionsMap["goal.created"]["goal_level"]> | null },
): void {
client.track("goal.created", {
goal_level: dims?.goalLevel ? asEventDimension(dims.goalLevel) : "other",
});
}
export function trackAgentCreated(
client: TelemetryClient,
dims: {
agentRole: RawDimension<EventDimensionsMap["agent.created"]["agent_role"]>;
agentId: string;
},
): void {
client.track("agent.created", {
agent_role: asEventDimension(dims.agentRole),
agent_id: dims.agentId,
});
}
export function trackSkillImported(
client: TelemetryClient,
dims: {
sourceType: RawDimension<EventDimensionsMap["skill.imported"]["source_type"]>;
skillRef?: string | null;
},
): void {
client.track("skill.imported", {
source_type: asEventDimension(dims.sourceType),
...(dims.skillRef ? { skill_ref: dims.skillRef } : {}),
});
}
export function trackAgentFirstHeartbeat(
client: TelemetryClient,
dims: {
agentRole: RawDimension<EventDimensionsMap["agent.first_heartbeat"]["agent_role"]>;
agentId: string;
},
): void {
client.track("agent.first_heartbeat", {
agent_role: asEventDimension(dims.agentRole),
agent_id: dims.agentId,
});
}
export function trackAgentTaskCompleted(
client: TelemetryClient,
dims: {
agentRole: RawDimension<EventDimensionsMap["agent.task_completed"]["agent_role"]>;
agentId: string;
adapterType: RawDimension<EventDimensionsMap["agent.task_completed"]["adapter_type"]>;
model?: string;
},
): void {
client.track("agent.task_completed", {
agent_role: asEventDimension(dims.agentRole),
agent_id: dims.agentId,
adapter_type: asEventDimension(dims.adapterType),
...(dims.model ? { model: dims.model } : {}),
});
}
export function trackErrorHandlerCrash(
client: TelemetryClient,
dims: { errorCode: string },
): void {
client.track("error.handler_crash", { error_code: dims.errorCode });
}
export function trackInteractionCreated(
client: TelemetryClient,
dims: {
interactionKind: RawDimension<EventDimensionsMap["interaction.created"]["interaction_kind"]>;
usedDeprecatedResolverPolicyAlias: boolean;
},
): void {
client.track("interaction.created", {
interaction_kind: asEventDimension(dims.interactionKind),
used_deprecated_resolver_policy_alias: dims.usedDeprecatedResolverPolicyAlias,
});
}
export function trackInteractionResolved(
client: TelemetryClient,
dims: {
interactionKind: RawDimension<EventDimensionsMap["interaction.resolved"]["interaction_kind"]>;
status: RawDimension<EventDimensionsMap["interaction.resolved"]["status"]>;
resolvedByKind: RawDimension<EventDimensionsMap["interaction.resolved"]["resolved_by_kind"]>;
resolutionReason?: RawDimension<EventDimensionsMap["interaction.resolved"]["resolution_reason"]> | null;
createdByKind?: RawDimension<EventDimensionsMap["interaction.resolved"]["created_by_kind"]> | null;
creatorAgentRole?: RawDimension<EventDimensionsMap["interaction.resolved"]["creator_agent_role"]> | null;
continuationPolicy?: RawDimension<EventDimensionsMap["interaction.resolved"]["continuation_policy"]> | null;
targetType?: RawDimension<EventDimensionsMap["interaction.resolved"]["target_type"]> | null;
optionCount?: number;
selectedOptionCount?: number;
questionCount?: number;
answeredQuestionCount?: number;
createdTaskCount?: number;
skippedTaskCount?: number;
legacyInheritedRestriction: boolean;
},
): void {
client.track("interaction.resolved", {
interaction_kind: asEventDimension(dims.interactionKind),
status: asEventDimension(dims.status),
resolved_by_kind: asEventDimension(dims.resolvedByKind),
...(dims.resolutionReason ? { resolution_reason: asEventDimension(dims.resolutionReason) } : {}),
...(dims.createdByKind ? { created_by_kind: asEventDimension(dims.createdByKind) } : {}),
...(dims.creatorAgentRole ? { creator_agent_role: asEventDimension(dims.creatorAgentRole) } : {}),
...(dims.continuationPolicy ? { continuation_policy: asEventDimension(dims.continuationPolicy) } : {}),
...(dims.targetType ? { target_type: asEventDimension(dims.targetType) } : {}),
...(dims.optionCount === undefined ? {} : { option_count: dims.optionCount }),
...(dims.selectedOptionCount === undefined ? {} : { selected_option_count: dims.selectedOptionCount }),
...(dims.questionCount === undefined ? {} : { question_count: dims.questionCount }),
...(dims.answeredQuestionCount === undefined ? {} : { answered_question_count: dims.answeredQuestionCount }),
...(dims.createdTaskCount === undefined ? {} : { created_task_count: dims.createdTaskCount }),
...(dims.skippedTaskCount === undefined ? {} : { skipped_task_count: dims.skippedTaskCount }),
legacy_inherited_restriction: dims.legacyInheritedRestriction,
});
}