# Zopu Work OS — Product

> Working spec · product/design/ops/research/agent teams · concepts, behavior, boundaries, value. Excludes implementation, infra, tech choices, low-level UI.

## 1. Summary

Zopu Work OS is a persistent OS for technical founders, product engineers, and small teams managing the full lifecycle of building and growing software across products, companies, repos, customers, and functions. It replaces fragmented chats, trackers, docs, and manually coordinated AI agents with one coherent system organized around **units of work**.

One continuous workspace discovers work, builds understanding, coordinates execution, requests human judgment when needed, tracks outcomes, and retains knowledge. Not a better chat app or just another PM tool — it continuously converts company signals into structured, persistent, executable work.

**Promise:** One workspace that understands the company, turns signals into work, progresses work autonomously, and brings the human in only when direction, judgment, permission, or taste is required.

## 2. Customer

**Ideal:** technical founder/small team that operates 1+ software products across several companies/clients/repos; moves between engineering, product, support, sales, marketing, ops, strategy; uses many tools without a unified model; uses AI coding tools but finds session/thread fragmentation; needs more execution capacity without hiring per function; needs context continuity, reliable delegation, visibility into autonomous work.

**Secondary:** product engineers (several initiatives), fractional CTOs, agencies/studios, solo founders, small product/eng teams, multi-internal-product operators.

Technical-first because code is a clear execution domain (measurable artifacts, established workflows, strong familiarity). Broader direction: research, content, marketing, support, product ops, sales enablement, other knowledge/creative work.

## 3. Problem — work is fragmented three ways

| Fragmentation | Detail |
| --- | --- |
| **Across tools** | One problem spans support→analytics→tracker→browser→repo→PR→release notes→customer comms. Each tool stores a partial view; none owns the lifecycle. |
| **Across AI sessions** | Chat/thread-organized work forces repeated decisions: which conversation, what context to restate, which model/agent, where to store output, how it relates to existing work, what changed after shipping. Conversation becomes structure though conversations are temporary and work is persistent. |
| **Founders as coordination** | Small-team founders bridge every system/function: remember decisions, connect feedback to work, check blockers, move work between tools, review AI output, decide next steps. The founder becomes the human integration bus. |

Zopu removes this burden by maintaining a persistent model of work, context, state, evidence, execution, and outcomes.

## 4. Thesis

- **Conversation is the interface, not the structure.** The durable structure is the **work unit**. Changing tasks must not require a new chat; the persistent conversation's scope moves between whole workspace and a specific work unit.
- **Work = outcomes, not activity.** Represent: _fix Safari OAuth failures; reduce onboarding abandonment; launch team invitations; resolve a billing escalation; rewrite the pricing page; validate a direction._ Do not promote every message/tool-call/agent action to a top-level task.
- **The product participates in work** — not just store/display: discover, understand, plan, execute, verify, publish, measure.
- **Autonomy reduces attention, not adds noise.** Successful when it makes correct progress, informs at the right level, asks only when necessary.
- **Knowledge compounds.** Every decision, result, failure, signal, artifact improves understanding and accelerates/future-proofs future work.

## 5. Core objects

Use a small set of durable concepts that stays stable across domains.

### Organization

Hard business/permission/knowledge boundary. Represents a company, client, startup, internal BU, or personal portfolio. Owns its users, products, policies, integrations, knowledge, work, history. **Information never moves between organizations implicitly.**

### Project / product

Durable operating context within an org: software product, repo-backed app, client engagement, internal platform, website/service, focused initiative. Contains goals, product knowledge, conventions, connected sources, relevant people, active work. Reduces context ambiguity without forcing manual folder structures.

### Goal

What the org/project is trying to achieve (e.g. reduce onboarding failure, reach a reliability target, launch enterprise support, improve trial→paid, close first ten enterprise customers). Distinguishes valuable work from merely possible. A work unit may support 1+ goals but every important unit has a clear reason.

### Signal

Raw info that may matter: support ticket, customer comment, error spike, deploy failure, sales objection, team-channel message, analytics change, founder thought. **Not automatically a task.** Fates: attach to existing work, convert to candidate, save as knowledge, dismiss, mark irrelevant/duplicate.

### Candidate work

Proposed work unit not yet accepted as durable active work. Created when a signal/cluster seems actionable. Explains: proposed outcome, why it matters, supporting evidence, project/goal, system confidence, whether similar work exists. User can accept/edit/merge/defer/dismiss.

### Work unit

Central object. Persistent, stateful representation of one meaningful outcome. Owns: objective, desired result, scope/non-goals, current understanding, requirements, success criteria, status, priority, dependencies, blockers, decisions, evidence, plan, human actions, agent activity, artifacts, results, learning.

Persists across conversations, agent runs, people, tools, time. Not identical to (though may contain): a single prompt, chat thread, agent session, PR, checklist item, branch.

### Step

Bounded action within a work unit: inspect logs, reproduce failure, draft plan, add tests, review preview, contact customers. Organizes execution but must not become another noisy task layer — visible when useful, hidden when only the outcome matters.

### Run

One agent/workflow attempt to progress a work unit: research root cause, implement, review, verify, prep release. A unit can have parallel/repeated runs. Subordinate to the work unit — never replaces it as the primary object.

### Artifact

Durable output/evidence: PR, commit, patch, design, spec, test report, screenshot, preview, research memo, customer-response draft, launch plan. Attached to the work unit, accessible from history and current state.

### Decision

Records an important human/system choice + reasoning: approve plan, choose design direction, reject a migration, delay a feature, accept a limitation. Stays visible after the conversation so future agents don't repeat settled debates.

### Approval

Specific human permission before a sensitive/consequential action: merge code, deploy prod, send customer message, access sensitive data, spend money, change business policy. Precise, inspectable, bounded — user knows exactly what happens after approval.

### Result & learning

Result records post-completion reality, comparing: intended outcome, expected impact, actual outcome, measurement period, unexpected effects, recommended follow-up. Learning = reusable knowledge extracted to improve future planning/execution across the relevant org/project.

## 6. Work lifecycle

Four conceptually consistent phases across all domains.

| Phase | The system… | Goal/output |
| --- | --- | --- |
| **Discover** | Extracts, groups, dedupes, connects signals; proposes candidate work; relates to goals/projects. | Identify meaningful work with sufficient evidence/relevance — _not_ the largest backlog. |
| **Understand** | Gathers context — research, code/system inspection, customer evidence, analytics, prior decisions, constraints, open questions, risks, success criteria. | Actionable understanding, not an endless report. |
| **Create & iterate** | Coordinates production — code, specs, designs, docs, tests, customer comms, launch assets, research, business recommendations. Produces intermediate artifacts, verifies, incorporates feedback without losing continuity. | Working solution. |
| **Publish & learn** | Releases/delivers/shares, then follows the outcome — deploy status, error rates, adoption, customer reactions, conversion, support volume, revenue impact, qualitative feedback. | Measured result. |

Lifecycle ends only when work produces a result, is intentionally stopped, or is superseded.

## 7. Work-unit boundaries

Quality depends on how work is divided.

**Good unit** = one independently meaningful outcome, summarizable in one clear sentence, reaches a recognizable result. Good: _fix Safari OAuth callback failures; decide SAML support this quarter; reduce onboarding time-to-first-value; prepare & launch team invitations._ Poor: _improve the product; marketing; work on onboarding; open the repo; read the logs_ (too broad or too granular).

**Split when:** multiple independent outcomes; different owners/approval paths; different orgs/permission boundaries; different deadlines/success criteria; one part finishes while another stays active; hard to summarize/review; a child deserves its own lifecycle/result.

**Merge when:** two candidates describe the same outcome; different channels reported the same problem; one candidate is just evidence for an existing unit; neither can be completed independently. System may propose merges; uncertain merges stay reversible/user-reviewable.

**Parent/child:** large initiatives contain child units; parent = larger result, each child still independently meaningful. _Parent: Restore OAuth reliability → children: fix Safari callbacks, improve setup diagnostics, update docs, notify customers, monitor post-release completion._ Parent summarizes progress/dependencies without replacing child detail.

## 8. Conversation model

One persistent conversational surface, two scopes.

| Scope | Use |
| --- | --- |
| **Global** | Think/act across workspace: introduce ideas/problems, ask what needs attention, compare work across projects, reprioritize, create/modify work, request summaries, discuss dependencies, request research/execution. System auto-connects messages to relevant projects/units while preserving source. |
| **Contextual** | Same composer focused on one work unit: add requirements, answer questions, review progress, change direction, approve a plan, request another attempt, ask for explanation, continue from an artifact. Entering does **not** create a new thread — it changes the active scope. |

Conversation is not the source of truth. Important facts/decisions/requirements/approvals/artifacts/results are extracted into structured objects while preserving links to source conversation.

## 9. Autonomous behavior

The system continuously observes, understands, organizes, executes, escalates, learns.

**May autonomously:** attach new evidence; update summaries/understanding; detect stale/blocked work; propose new work; run safe research; prepare plans/drafts; produce code/artifacts inside approved boundaries; run verification; suggest priorities; monitor completed work; create follow-up candidates; summarize activity.

**Request human for:** strategic direction; ambiguous product decisions; business tradeoffs; sensitive permissions; high-risk actions; external communication; production changes; priority conflicts; conflicting evidence; actions outside granted policy.

**Must not:** create speculative work without evidence; treat confidence as certainty; hide failures/uncertainty; silently cross org boundaries; reopen settled decisions without new evidence; perform irreversible actions without authority; optimize for activity over outcomes.

## 10. Human-in-the-loop

User must not inspect every unit to find what needs attention. Human requests centralize into an **Attention Queue**. Each request states: what's required, why, the recommendation, supporting evidence, what happens after approval, what happens if the user does nothing, urgency, reversibility. **Categories:** decision · approval · clarification · access request · review · blocker resolution · risk alert.

Minimize unnecessary approvals via explicit standing policies while preserving clear control over sensitive actions.

## 11. Provenance & trust

User must understand why the system believes something and what caused an action. **Every important claim carries provenance.** Sources: message, ticket, repo change, error report, analytics event, document, agent observation, human decision. Claim status distinguished as: verified · reported · inferred · assumed · conflicting · outdated. Primary interface stays readable; deeper evidence/derivation available on demand. **Never present an agent summary as equivalent to primary evidence.**

## 12. Knowledge model

Accumulates several kinds; each retains its scope (personal prefs, project facts, org-wide truths never mixed blindly).

| Kind | Content |
| --- | --- |
| **Canonical** | Reviewed descriptions: business, product, customers, design principles, architecture concepts, conventions, policies, goals. |
| **Operational** | Current structured state: active work, priorities, dependencies, ownership, blockers, approvals, results. |
| **Episodic** | History: conversations, runs, errors, decisions, reviews, releases, customer reactions. |
| **Learned patterns** | Reusable conclusions: common failure causes, customer prefs, repeated objections, effective launch patterns, known limitations, preferred technical/product approaches. |

## 13. Product boundaries

| Boundary | Rule |
| --- | --- |
| **Control layer, not tool replacement** | Specialist systems (source control, support, comms, analytics, billing, deployment, docs) keep native records/actions. Zopu provides the unified model: what matters, its state, next step, whether outcome worked. |
| **Own the work graph** | Connects organizations→projects→goals→signals→work units→dependencies→decisions→runs→artifacts→results→learnings. Durable source of coordination/context. |
| **Execution is replaceable** | Execution is a capability beneath the work unit. Users shouldn't care which agent/model/harness ran unless inspection is useful. |
| **No unnecessary AI config** | Users shouldn't normally choose model, thinking level, agent framework, context-window strategy, execution runtime, tool routing. System chooses by task/risk/cost/policy. |

## 14. Optimization

**Optimize for: maximum useful progress with minimum required human attention.**

Measures: correct extraction of meaningful work; low false-positive candidates; signal→understanding time; time to first useful action; accepted units reaching meaningful outcomes; reduced repeated context gathering; human attention per outcome; decision/output quality; trust/inspectability/reversibility; knowledge reuse; improvement from past results.

**Don't primarily optimize:** chat count, time in interface, task count, agent-run count, notification count, raw model usage.

A successful day: user opens briefly, resolves a few high-value decisions, sees many units progressed correctly without them.

## 15. Business value

| Form | Value |
| --- | --- |
| **Execution capacity** | Small team progresses more work without proportional headcount. |
| **Coordination reduction** | Handles repetitive context-gathering, state updates, linking, follow-ups, output prep. |
| **Continuity** | Work keeps context, history, decisions, artifacts across tools/time. |
| **Better opportunity capture** | Notices recurring problems, customer opportunities, operational risks, key relationships teams otherwise miss. |

## 16. Initial scope — first product loop

1. User starts/connects a software project.
2. Product establishes project context.
3. User describes desired work via global composer.
4. System creates/updates a work unit.
5. Unit is clarified and accepted.
6. System progresses work autonomously.
7. User reviews only necessary decisions/outputs.
8. System produces verified artifacts.
9. User approves publication/integration where required.
10. Product updates the unit and retains knowledge.

First version must prove persistent work units beat disposable agent chats.

## 17. Long-term direction

Technical product is the starting point. The same work-unit model later supports: product research, marketing campaigns, content production, sales enablement, support operations, pricing work, business analysis, hiring, internal ops. Long-term: a general OS for creative/knowledge work.

**Enduring principles:** one persistent workspace · work units not chat sessions · outcome-oriented execution · invisible orchestration · explicit human authority · strong provenance · compounding organizational knowledge.

## 18. Initial non-goals

Not intended to be: a full external-tool replacement; a general social collaboration platform; a traditional kanban with AI decoration; a model playground; a prompt-management product; a marketplace of visible agents; a system that creates work faster than users can evaluate; a fully autonomous company with no human authority.

## 19. Principles

1. Organize around work, not conversations.
2. Represent outcomes, not activity.
3. Keep the system calm while the machinery stays powerful.
4. Bring humans in for judgment, not clerical coordination.
5. Expose evidence and uncertainty.
6. Make important actions understandable and reversible.
7. Preserve organization and project boundaries.
8. Prefer one coherent model over many disconnected features.
9. Let knowledge compound across completed work.
10. Measure success by outcomes and reduced attention.

## 20. Working statement

Zopu Work OS is an autonomous OS for technical founders and small product teams. It observes work across the company, turns fragmented signals into persistent units of work, progresses them via agents and tools, and brings the human in only when judgment, permission, or direction is required. One continuous workspace moves from idea/problem to verified, shipped, measurable outcome while preserving context and building durable understanding over time.
