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Linear for AI agents

Not Linear for humans. Product memory for agents.

RAAV is the agent-operable product and project memory layer for AI-built software: handbook, tasks, claims, lanes, proposals, verification, and audit.

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What RAAV gives agents

  • Founders see readable product state instead of raw agent logs
  • Agents work from structured context instead of vague tickets
  • Every task can connect to files, branches, verification, and decisions
  • Material changes move through proposals before becoming shared truth

The problem

Human-first issue trackers are useful, but coding agents need a repo-local operating memory they can read, validate, and update without losing product context.

The RAAV layer

RAAV borrows PM clarity from tools like Linear, then adds the missing agent layer: context packs, claims, branch lanes, proposal-first writes, memory distillation, and audit.

The Linear-shaped gap appears when agents become the team

Linear is excellent for human teams. The emerging gap is different: a founder may run several coding agents across branches and sessions, while the product memory, task ownership, and proof of work need to remain coherent.

Human issue trackers assume human interpretation

A human can read a vague ticket, remember the roadmap meeting, ask a teammate, and understand which shortcuts are acceptable. An agent needs those assumptions made explicit before it edits code.

RAAV keeps the product handbook, task intent, branch lane, acceptance criteria, and review state together. The issue is not just what to build; it is what the agent must know before it builds.

  • Product context next to task state
  • Agent-readable lanes and claims
  • Founder-readable review queues

The agent PM system needs memory, not only tasks

A task board can tell an agent the next item. It can not by itself preserve the product truth behind that item, the assumptions made during the last implementation, or the verification evidence from the last run.

RAAV treats memory as a first-class project object. Tasks, proposals, decisions, verification, and audit all feed the same operating state so the next agent is not starting from a stale ticket.

  • Confirmed memory vs inferred memory
  • Task lifecycle with defer, cancel, reopen, and submit
  • Audit records for founder review and diligence

The founder needs confidence without managing every prompt

If agent work keeps moving in automated loops, the founder needs a higher-level control surface. They need to know what the agents believe, what they changed, what remains risky, and what decision is waiting on the founder.

RAAV is designed to make that state visible. It is less about replacing the coding agent and more about making a team of agents manageable enough to trust.

  • One source of product truth for multiple agents
  • Proposal gates for material changes
  • Launch readiness tied to completed work

How the agent loop works

RAAV does not replace your coding agent. It gives the agent a durable operating system for product truth, task ownership, and proof.

Product truth

Product handbook stores ICP, problem, promise, scope, non-goals, requirements, and risks.

Work truth

Tasks, tickets, claims, lanes, and submits track active agent work.

Decision truth

Proposals, deferrals, and memory candidates keep the founder in control.

Proof

Audit export reconstructs what changed, who did it, and what was verified.

Try the agent-first PM loop

The fastest way to understand RAAV is to ask your coding agent to set up the project memory, then have it show the current product truth and the next safe task before coding.

Paste this into your coding agent
Set up RAAV as the product and project management system for agents in this repo. Build the Product Handbook, create task proposals, show branch lanes and claims, and ask me to confirm product truth before durable writes.

This gives the founder a product operating system while letting Codex, Claude Code, Cursor, or MCP agents continue doing the coding.

Why this is different

Most tools either write code or track human tickets. RAAV sits between the founder and the coding agents as shared product memory, coordination, and audit.

Agents are managed identities, not paid seats
Zero-LLM core for pack, next, claim, submit, review, and audit
Built for Codex, Claude Code, Cursor, and MCP clients rather than replacing them

Linear for humans vs RAAV for agents

Primary user
Human product and engineering teams managing tickets.
Founders and coding agents sharing product memory and operating state.
Context model
Issues, projects, comments, and human-written context.
Product handbook, context packs, tasks, claims, lanes, proposals, and audit.
Agent workflow
Agents need custom prompting to interpret the board safely.
Agents follow CLI and MCP workflows for read, claim, submit, and review.
Founder control
Product changes may be scattered across issues, PRs, and chats.
Material changes flow through proposals and memory candidates.

FAQ

This page answers the practical positioning question: is RAAV trying to be a prettier issue tracker, or a different system for agent-built software?

Is RAAV trying to replace Linear?

Not directly. Linear is a strong human-first issue tracker. RAAV is focused on the missing layer for AI agents: durable product memory, claims, lanes, proposals, verification, and audit inside the repo.

Can RAAV integrate with Linear later?

Yes. RAAV can remain the agent-operable memory layer while syncing selected tasks or status with a human issue tracker later. The first product should prove the agent workflow before broad integrations.

Why call this a PM system for agents?

Because agent work needs more than tickets. It needs context packs, file ownership, branch lanes, review gates, memory distillation, verification state, and an audit trail that future agents can read.

How should pricing work for this model?

Agents should not be billed as seats. A practical model is free local memory, paid hosted founder visibility, and team seats for humans who need shared workspaces, roles, sync, and longer audit retention.

Related RAAV workflows

Product OSAI agent project managementPricing