AI-Generated

I want build a code planner agent ,(basically a planner service, which does the agent orchestration). Input is RFC ,output is plan.md which is feeder for code implementation agent .

PlanMaster 9000 (aka RFC-to-PLAN.md Whisperer)

ACTUALLY NOT BAD
7/10
You've drawn a prettier architecture diagram than most YC companies ship — now survive the multi-repo context window.

An orchestrated agentic planner that ingests an RFC/ticket, retrieves codebase + ADR/postmortem context across multiple repos, generates a structured PLAN.md via the strongest available model, self-critiques for gaps, then fans out plan.approved events to Code Impl and SDET sub-agents.

The diagram you uploaded is honestly more thoughtful than 80% of what's on ProductHunt — the self-critique phase and mandatory human gate show you've thought about failure modes. The multi-repo detection piece (which repos does this touch?) is the real hard part nobody's solved well. The fan-out event architecture feeding specialized sub-agents is the right pattern and gives you a moat if you nail the context retrieval.

whycantwehaveanagentforthis.com
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Viability Analysis

Market Demand78
Tech Feasibility62
Competition72
Monetization68
AI Disruption Risk82
Fun Factor88

Pros & Cons

What's going for it

PLAN.md as a versioned, tagged artifact (plan/TICKET-123/v1) is a genuinely underexplored idea — creates audit trail and enables downstream agent specialization that Copilot Workspace completely ignores.
The mandatory human review gate (Phase 4) is the correct product decision — it's the thing that makes enterprise buyers say yes instead of 'we'll wait for the lawsuit precedent.'
Fan-out event architecture (plan.approved → Code Impl Agent + SDET Agent) is modular and lets you sell the planner standalone or as an orchestration hub — two GTM motions in one.
RFC + ADR + postmortem context retrieval is a real differentiator — no current tool ingests organizational memory (postmortems!) into the planning loop. That's your wedge.
Self-critique phase (Phase 3 gap check) dramatically reduces the 'AI hallucinated a whole microservice that doesn't exist' failure mode that kills trust in AI planners.

What's against it

Multi-repo detection ('which repos does this touch?') is genuinely hard — dependency graphs across polyrepos with mixed languages will humble you fast. Budget 40% of your dev time here alone.
Context window limits will bite you on large RFCs + multi-repo codebases simultaneously. You'll need aggressive chunking/summarization that degrades plan quality in ways that are hard to test.
The 'strongest model' in Phase 2 is expensive at scale — one complex RFC touching 8 repos could cost $2-15 in API calls per plan generation. Enterprise pricing math gets weird fast.
GitHub Copilot Workspace is free for GitHub Enterprise customers. You're competing against free-bundled on the exact same workflow trigger (ticket → plan → code).
Schema drift: your fixed PLAN.md schema will be wrong for 30% of use cases immediately. Teams will want custom sections, and then you're in the 'we're basically a template engine' death spiral.

Who You're Up Against

Open Source Alternatives

When Will Big AI Kill This?

Most Likely Killer

GitHub (Microsoft)

Timeline: 12-18 months

Now3mo6mo1yr2yrNever

How They'll Do It

Copilot Workspace adds RFC ingestion, ADR retrieval, and a versioned plan artifact to its existing issue→plan→code pipeline. It ships as a free add-on for GitHub Enterprise at $21/seat/month. Your standalone product has no answer.

Your Survival Strategy

Own the cross-org, multi-VCS (GitHub + GitLab + Bitbucket simultaneously) enterprise segment and the postmortem/ADR organizational memory angle — GitHub will never index your Confluence and your 3-year-old postmortem Notion docs.

Confidence

71%

If You're Crazy Enough to Build It

Solo Dev Time

3-4 months to MVP that doesn't embarrass you; 8 months to something you'd show a Series A investor

Team Size

1 senior backend engineer who's touched RAG pipelines before + 1 person who's read every postmortem ever written and can design the schema

Estimated Cost

$8,000-$25,000 to MVP (infra + API costs during dev/testing); $3-15/plan in production API costs at Claude Opus or GPT-4o tier

Tech Stack

LangGraphClaude API (Opus for Phase 2, Haiku for Phase 3)pgvector on SupabaseGitHub/GitLab webhooksNext.js

Agent-Readiness Score

Build only if you have a moat. PlanMaster 9000 (aka RFC-to-PLAN.md Whisperer)'s readiness gap is real work.

51BAND D
  • Some cross-session state — start with Redis, graduate to a vector store.

  • Crowded market: at least 9 integrations to compete.

  • Wide policy surface — full red-team pass, content filter, and human-in-loop required.

  • Eval scaffolding doable — write 50 paired examples and grade with an LLM-as-judge.

DETERMINISTIC SCORE — DERIVED FROM EXISTING ANALYSIS, NO SECOND LLM CALL

⚡ Ship it anyway

The version that survives

You've been dared. Here's the wedge worth your weekend — and the fastest way to find out it won't work.

01

The wedge that isn't taken

Build the postmortem + ADR retrieval layer first and only — sell 'your org's memory injected into every plan' as the SKU. Nobody else is indexing Confluence postmortems.

02

Test this before you write a line of code

That Feature Leads will actually review and approve AI plans rather than rubber-stamp them or ignore the queue entirely, making your human gate theater.

03

The honest cost — and who should walk away

~$20K and 4 months of real senior engineering time. Do NOT build this if you're at a company where 'RFC' means a Google Doc nobody reads — you have no customers.

Think the wedge holds? ↓ Pressure-test it live before you sink a weekend into it — 20 min, free, no signup.

⚡ Pressure-test the wedge

Get this wedge pressure-tested live — 20 min, free.

Bring the wedge above and we'll stress-test it together: is that differentiator really still open, does the riskiest assumption survive contact, what to build first. No signup, no slides.

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How this was generated
9%UPHILL

Production-readiness odds

Real readiness gaps. Build a thin first, harden second; budget runway for both.

ANCHORED TO OUR OWN READINESS RUBRIC — NO EXTERNAL STAT CITED

🛡 Safety considerations

What these mean →

Heuristic, not exhaustive. Surfaces the 3 biggest categories an operator should think about for this idea. Hover any chip for the mitigation pointer.

⚖ Governance checklist

8 controls apply

Things to have in place before you ship. Pairs with the OWASP-style risk chips above — that catalog answers “what could go wrong?”, this one answers “what should you have ready?”

  • Audit trail of every tool call

    critical

    Persist a structured per-call log of inputs, outputs, and decisions for at least the legal retention window. Without this, post-incident review is impossible.

  • Role-based access control on the agent surface

    critical

    Different users, different scopes. The agent should never default to "admin can do everything." Pair with per-task capability scoping.

  • Tenant / workspace isolation

    critical

    A multi-tenant agent must never leak data across tenants in either direction (inputs OR cached intermediate state).

  • Secrets management

    high

    Tokens and API keys live in a vault, not in env vars on a CI runner. Rotate on a documented schedule, not "when something happens."

  • Eval coverage on every release

    high

    A frozen eval suite that runs on every model / prompt change. "It worked when I demoed it" is not a release gate.

  • Per-user / per-tenant rate limits

    medium

    Agent loops are pathologically expensive when wrong. Cap tokens-per-session, tool-calls-per-session, and dollars-per-day before launch.

  • Pin model versions; track the changelog

    medium

    A silent provider-side model upgrade can shift behavior overnight. Pin to a versioned model ID; subscribe to the provider changelog.

  • Documented incident runbook

    low

    Who's on call? Who can flip the killswitch? How do you roll back to last-known-good? Write it before you need it.

OUR INTERNAL TWELVE-CONTROL SYNTHESIS — STANDARD SOC 2 / ISO 27001 / GDPR FAMILIES APPLIED TO LLM AGENTS

🛠 Build this with Claude Code

Skip the boilerplate. Start from a working spec.

We've packaged this idea into a CLAUDE.md + scaffold.sh starter — the problem statement, agent-readiness sub-scores, suggested tools, and smoke evals, all deterministic and ready to drop into a fresh repo. Open it in Claude Code, or copy the markdown into any IDE.

Don't have Claude Code yet? View the bootstrap preview · grab the JSON bundle · or embed the readiness badge.

🛠 Steal this idea

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Post a public 2-paragraph plan. Add the repo URL when you ship. No rights granted; no permission required — credit goes to whoever ships first. See all claims at /steal-this-idea.

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