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AI agent project management

Project management for AI agents building real products.

RAAV gives Codex, Claude Code, Cursor, and MCP agents one product handbook, task plan, branch lanes, claims, decisions, verification, and audit trail.

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What RAAV gives agents

  • Agents start from the same product handbook instead of chat history
  • Tasks, claims, branch lanes, and verification stay connected
  • Founders review proposed product changes before they become shared truth
  • Handoffs include what changed, why it changed, and what remains risky

The problem

Automated coding loops can create PRs, fix bugs, and keep moving in the background. The weak point is deciding what each agent should do, what product truth is current, and what evidence proves the work is safe.

The RAAV layer

RAAV is the product and project memory layer those agents operate through. It keeps human-readable product truth and agent-readable task state in one local-first ledger.

Why AI agents need their own project management layer

Classic project management assumes a human reads the ticket, remembers the product, talks to the team, and decides what proof is enough. Agent work is different. The worker is fast, disposable, and often starts cold. RAAV is built around that reality.

The project state must be readable by machines and humans

A founder needs a calm view of what the product is, what is in scope, what is blocked, and what changed. A coding agent needs stricter operating state: current brief, accepted requirements, task ownership, file claims, branch lane, verification expectations, and the next safe action.

RAAV keeps those views connected. The same ledger can explain the product to the founder and give Codex, Claude Code, Cursor, or an MCP agent enough context to work without re-litigating the plan in every chat.

  • Product Handbook for goals, ICP, scope, risks, and open questions
  • Task state that connects requirements to owners, files, branches, and proof
  • Founder review queues for assumptions, proposals, and durable memory

The agent loop needs gates, not just tickets

A ticket says what someone wants. An agent operating loop needs more: when to read context, when to claim files, when to check conflicts, when to ask for founder confirmation, and what evidence to submit before the work is considered done.

RAAV turns those gates into repeatable CLI and MCP workflows. Agents can use their own reasoning, but the product state moves through consistent steps: pack, next, claim, conflicts, submit, review, and audit.

  • Claims reduce blind parallel edits
  • Proposal-first writes keep product changes reviewable
  • Submits make verification and risks visible after each run

The founder should not need to inspect every terminal log

As automated loops become longer, raw chat and terminal logs become a poor management interface. They are useful for debugging, but not for deciding whether the product is moving in the right direction.

RAAV summarizes the operating state into founder-readable memory: what is confirmed, what was inferred, what changed, what is risky, and what decision is needed next. That is the project management layer missing between a solo founder and a team of coding agents.

  • Audit trail for what changed and why
  • Memory candidates before facts become permanent
  • Launch readiness checks tied to actual 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.

Plan

Agents read a context pack with brief, goals, open requirements, risks, and next task.

Claim

Before editing, each agent claims a task, files, branch, and worktree lane.

Submit

After work, agents submit summary, files, verification status, risks, and memory candidates.

Review

Founders approve proposals, promote durable memory, and decide the next move.

Start with one agent prompt

RAAV is easiest to adopt when the founder asks the coding agent to install and operate it from inside the repository. The agent does the setup, then RAAV gives future agents the same operating rules.

Paste this into your coding agent
Set up RAAV in this repository. Initialize local product memory, create an initial Product Handbook, identify open product questions, and show me the next safe task before editing code.

RAAV can then expose the same context through CLI and MCP so future agent sessions start from the ledger instead of a blank chat.

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.

Zero-LLM by default: your coding agent reasons; RAAV stores the operating state
Proposal-first writes for material product and project changes
Audit export for founder review, handoff, diligence, and launch readiness

AI agent project management vs human-first task tracking

Context
A ticket title plus whatever context is still in the chat window.
A context pack with product truth, task intent, risks, files, branch lane, and open decisions.
Ownership
Agents can start work without knowing who else is editing the same area.
Claims and conflict checks show active owners before files are touched.
Change control
Agents may silently turn assumptions into implementation details.
Material product changes go through proposals and founder confirmation.
Completion
Done means the agent stopped or opened a PR.
Done means summary, files, verification, risks, and audit are recorded.

FAQ

These are the practical questions founders ask when they move from one-off AI coding sessions to repeatable agent work.

Is RAAV an AI coding agent?

No. RAAV is the memory and project management layer around coding agents. Codex, Claude Code, Cursor, or another agent does the reasoning and coding. RAAV keeps product truth, task state, lanes, verification, and audit stable across sessions.

Why not just use Linear, GitHub Issues, or Notion?

Those tools are useful for humans, but agents need a repo-local operating protocol they can read and update deterministically. RAAV connects product memory, task claims, branch lanes, conflict checks, proposals, submits, and founder review in one loop.

Does RAAV need to run its own model?

The core does not. RAAV is zero-LLM by default for pack, next, claim, submit, review, and audit. The coding agent you already use can supply the intelligence, while RAAV supplies the durable structure.

Who is this for first?

The first ICP is the non-coding or lightly technical solo founder using coding agents to build a real product. RAAV is also useful for small teams once multiple people and agents share a repository.

Related RAAV workflows

Product OS demoMemory consoleInstall prompt