## Thinking Path > - Paperclip is the open source app people use to manage AI agents for work. > - Its recovery loop keeps assigned work moving after execution failures. > - Productivity review used run counts, comment counts, and elapsed time to create management tasks. > - Infrastructure failures could satisfy those rules and create more tasks without evidence that the source work needed management review. > - This pull request removes that detector and its continuation holds. > - Bounded recovery, budgets, explicit blockers, and normal review stages remain in place. > - Existing task records stay readable and unchanged. ## Linked Issues or Issue Description Refs #5897. That request describes unwanted automatic productivity reviews and asks to preserve existing tasks. This change retires the feature instead of adding another configuration switch. Related prior approaches: Refs #9191, Refs #12489. Those changes excluded infrastructure failures or bounded review creation. This removal replaces the detector rather than tuning its thresholds. ## What Changed - Delete the scheduled detector, automatic task creation, evidence refresh, and productivity continuation holds. - Remove computed productivity fields, special attention items, badges, and Storybook fixtures. - Retain historical origin values, decision compatibility, and recovery recursion exclusions. Add no migration and change no existing task data. - Update the execution contract. Replace feature tests with regressions for legacy task reads, ordinary attention, and bounded continuation in the presence of an old review. ## Verification - Targeted attention, issue-route, startup, and UI tests: 4 files and 101 tests passed. - Updated issue-route and UI tests: 2 files and 61 tests passed. - Bounded continuation regression: 2 cases passed, including a legacy review plus pre-dispatch cancellation churn. - `pnpm check:token-gates`: all four gates passed. - `git diff --check`: passed. - `pnpm build-storybook`: passed. - Greptile: 5/5 on `a5a612eea`, with no actionable findings. - Scheduler and historical recovery regressions: 2 files and 28 tests passed. - Repository `pnpm -r typecheck` and `pnpm build`: passed. - The complete `pnpm test:run` suite passed across the CI server, serialized-server, and workspace shards on `a5a612eea`. Stopped the duplicate local monolithic run after the full CI suite passed; no completed local full-suite result is claimed. The targeted local suites above passed. - CI serialized shard 5 initially hit a 10-second timeout in the first interaction-route test. The complete file passed locally (78 tests), then the single CI rerun passed. - All CI gates are green, including the build and end-to-end suites. - A local merge check against current `master` (`ce09ea40b`) completed without conflicts. ## Risks - API responses no longer include the computed `productivityReview` field. Consumers must stop using it. - The scheduler no longer creates management work from elapsed time, run counts, or missing comments. This is the intended behavior change. - Existing review tasks and explicit dependencies remain in place. Historical origins still prevent recursive recovery treatment. No task cleanup or data migration occurs. - The native review handoff repair is separate from this removal. ## Model Used OpenAI GPT-6 through Codex, with reasoning, tool use, and code execution. The exact runtime model identifier and context-window size are 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>
12 KiB
Paperclip — Product Definition
What It Is
Paperclip is the control plane for autonomous AI companies. One instance of Paperclip can run multiple companies. A company is a first-order object.
Core Concepts
Company
A company has:
- A goal — the reason it exists ("Create the #1 AI note-taking app that does $1M MRR within 3 months")
- Employees — every employee is an AI agent
- Org structure — who reports to whom
- Revenue & expenses — tracked at the company level
- Task hierarchy — all work traces back to the company goal
Employees & Agents
Every employee is an agent. When you create a company, you start by defining the CEO, then build out from there.
Each employee has:
- Adapter type + config — how this agent runs and what defines its identity/behavior. This is adapter-specific (e.g., an OpenClaw agent might use SOUL.md and HEARTBEAT.md files; a Claude Code agent might use CLAUDE.md; a bare script might use CLI args). Paperclip doesn't prescribe the format — the adapter does.
- Role & reporting — their title, who they report to, who reports to them
- Capabilities description — a short paragraph on what this agent does and when they're relevant (helps other agents discover who can help with what)
Example: A CEO agent's adapter config tells it to "review what your executives are doing, check company metrics, reprioritize if needed, assign new strategic initiatives" on each heartbeat. An engineer's config tells it to "check assigned tasks, pick the highest priority, and work it."
Then you define who reports to the CEO: a CTO managing programmers, a CMO managing the marketing team, and so on. Every agent in the tree gets their own adapter configuration.
Agent Execution
Paperclip supports several ways to run an agent's heartbeat:
- Local CLI/session adapters — Paperclip starts or resumes local coding-tool sessions such as Claude Code, Codex, Gemini, OpenCode, Pi, and Cursor, then tracks the run.
- Run a command — Paperclip kicks off a process (shell command, Python script, etc.) and tracks it. The heartbeat is "execute this and monitor it."
- Fire and forget a request — Paperclip sends a webhook/API call to an externally running agent. The heartbeat is "notify this agent to wake up." OpenClaw-style hooks work this way.
- External adapter plugins — Paperclip loads adapter packages through the plugin/adapter flow so self-hosted installs can add runtimes without hardcoding them in core.
Agent runs can use project and execution workspaces, managed runtime services such as preview/dev servers, adapter-specific session state, and HTTP/webhook-style execution. We provide sensible defaults, but the adapter is still the boundary: if a runtime can be invoked, observed, and authorized, Paperclip can coordinate it.
Task Management
Task management is hierarchical. At any moment, every piece of work must trace back to the company's top-level goal through a chain of parent tasks:
I am researching the Facebook ads Granola uses (current task)
because → I need to create Facebook ads for our software (parent)
because → I need to grow new signups by 100 users (parent)
because → I need to get revenue to $2,000 this week (parent)
because → ...
because → We're building the #1 AI note-taking app to $1M MRR in 3 months
Tasks have parentage. Every task exists in service of a parent task, all the way up to the company goal. This is what keeps autonomous agents aligned — they can always answer "why am I doing this?"
The current issue model includes stable issue identifiers, parent/sub-issues, blockers, a single assignee, comments, issue documents, attachments and work products, and review/approval handoffs. That structure keeps work inspectable by both the board and agents while still allowing agents to decompose work into smaller tasks.
Company Skills and Policy
Company skills are shared operating capabilities, not privileged objects by default. Every authenticated agent in a company can create, import, install, edit, update, test, reset, and remove that company's skills unless the company has configured an explicit restriction.
The governing rule is: skill permissions are opt-in restrictions, not opt-in capabilities. Missing skill grants never create a denial in an otherwise unconfigured company, and ordinary skill work does not require board confirmation, a draft-only workflow, or an activation approval.
Core Paperclip owns the skill runtime, company-boundary enforcement, policy evaluation contract, API denials, validation, path containment, secret redaction, and activity logging. Those safety invariants cannot be disabled by policy. Open-by-default skill work never authorizes arbitrary host-path reads, unsafe executable content, or policy edits: local imports and scans must stay within Paperclip-known workspace or managed-skill roots, remote sources must resolve to validated immutable content, and platform safety denials must stay distinct from optional administrative restrictions. Paperclip EE may provide detailed administration for per-agent, per-role, per-action, per-source, and protected-skill rules, but EE is not required to use skills and is not an enforcement boundary. Without EE, companies remain open by default and any already-configured restrictions continue to be enforced by core.
An explicit restricted policy may deny selected operations or switch to a default-deny preset with explicit allow rules. Core exposes a stable versioned policy API so EE and other administrative clients configure and simulate the same evaluator used by skill mutation routes. Core Skill Studio only needs to perform normal skill work, explain an explicit denial, and point administrators to EE when its richer policy UI is available; it must not recreate a partial enterprise permission editor.
Principles
-
Unopinionated about how you run your agents. Your agents could be OpenClaw bots, Python scripts, Node scripts, Claude Code sessions, Codex instances — we don't care. Paperclip defines the control plane for communication and provides utility infrastructure for heartbeats. It does not mandate an agent runtime.
-
Company is the unit of organization. Everything lives under a company. One Paperclip instance, many companies.
-
Adapter config defines the agent. Every agent has an adapter type and configuration that controls its identity and behavior. The minimum contract is just "be callable."
-
All work traces to the goal. Hierarchical task management means nothing exists in isolation. If you can't explain why a task matters to the company goal, it shouldn't exist.
-
Control plane, not execution plane. Paperclip orchestrates. Agents run wherever they run and phone home.
User Flow (Dream Scenario)
- Open Paperclip, create a new company
- Define the company's goal: "Create the #1 AI note-taking app, $1M MRR in 3 months"
- Create the CEO
- Choose an adapter (e.g., process adapter for Claude Code, HTTP adapter for OpenClaw)
- Configure the adapter (agent identity, loop behavior, execution settings)
- CEO proposes strategic breakdown → board approves
- Define the CEO's reports: CTO, CMO, CFO, etc.
- Each gets their own adapter config and role definition
- Define their reports: engineers under CTO, marketers under CMO, etc.
- Set budgets, define initial strategic tasks
- Hit go — agents start their heartbeats and the company runs
Guidelines
There are two runtime modes Paperclip must support:
local_trusted(default): single-user local trusted deployment with no login frictionauthenticated: login-required mode that supports both private-network and public deployment exposure policies
Canonical mode design and command expectations live in doc/DEPLOYMENT-MODES.md.
Further Detail
See SPEC.md for the full technical specification and TASKS.md for the task management data model.
Paperclip’s core identity is a control plane for autonomous AI companies, centered on companies, org charts, goals, issues/comments, heartbeats, budgets, approvals, and board governance. The public docs are also explicit about the current boundaries: tasks/comments are the built-in communication model, Paperclip is not a chatbot, and it is not a code review tool. The roadmap already points toward easier onboarding, cloud agents, easier agent configuration, plugins, better docs, and ClipMart/ClipHub-style reusable companies/templates.
What Paperclip should do vs. not do
Do
- Stay board-level and company-level. Users should manage goals, orgs, budgets, approvals, and outputs.
- Make the first five minutes feel magical: install, answer a few questions, see a CEO do something real.
- Keep work anchored to issues/comments/projects/goals, even if the surface feels conversational.
- Treat agency / internal team / startup as the same underlying abstraction with different templates and labels.
- Make outputs first-class: files, docs, reports, previews, links, screenshots.
- Provide hooks into engineering workflows: worktrees, preview servers, PR links, external review tools.
- Use plugins for edge cases like rich chat, knowledge bases, doc editors, custom tracing.
Do not
- Do not make the core product a general chat app. The current product definition is explicitly task/comment-centric and “not a chatbot,” and that boundary is valuable.
- Do not build a complete Jira/GitHub replacement. The repo/docs already position Paperclip as organization orchestration, not focused on pull-request review.
- Do not build enterprise-grade RBAC first. Paperclip now has authenticated mode, company memberships, instance roles, and permission grants, but fine-grained enterprise governance should remain secondary to the core company control plane.
- Do not interpret agent-level privacy flags as a project/issue privacy feature in V1; work visibility stays company-scoped.
- Do not lead with raw bash logs and transcripts. Default view should be human-readable intent/progress, with raw detail beneath.
- Do not force users to understand provider/API-key plumbing unless absolutely necessary. There are active onboarding/auth issues already; friction here is clearly real.
Specific design goals
-
Time-to-first-success under 5 minutes A fresh user should go from install to “my CEO completed a first task” in one sitting.
-
Board-level abstraction always wins The default UI should answer: what is the company doing, who is doing it, why does it matter, what did it cost, and what needs my approval.
-
Conversation stays attached to work objects “Chat with CEO” should still resolve to strategy threads, decisions, tasks, or approvals.
-
Progressive disclosure Top layer: human-readable summary. Middle layer: checklist/steps/artifacts. Bottom layer: raw logs/tool calls/transcript.
-
Output-first Work is not done until the user can see the result: file, document, preview link, screenshot, plan, or PR.
-
Execution visibility without log worship Active runs, recovery issues, blockers, and work products should be first-class surfaces. Raw transcripts are available when needed, but they are not the primary product surface.
-
Local-first, cloud-ready The mental model should not change between local solo use and shared/private or public/cloud deployment.
-
Safe autonomy Auto mode is allowed; hidden token burn is not.
-
Thin core, rich edges Put optional chat, knowledge, and special surfaces into plugins/extensions rather than bloating the control plane.