Show HN: Organ – AI department heads that run our software company autonomously

Submission Title

Show HN: Organ – AI department heads that run our software company autonomously

(79 chars)

Post Body

Organ is an autonomous business operations platform where 5 AI CXO agents (CEO, CTO, CPO, CMO, COO) wake up on schedules, review the state of the business, and dispatch work — code tasks, research, content, hiring decisions — without human prompting.

We built this because we kept hitting the same wall with agent orchestration. Frameworks like CrewAI and AutoGen are great at coordinating agents, but every run starts from zero. The agent that fixed your auth bug yesterday doesn't remember doing it. The CTO that reviewed 40 PRs last week still asks "what's the tech stack?" on Monday.

So we built a brain synthesis flywheel. After each agent session, observations get recorded — codebase patterns, what worked, what failed, decisions made. Periodically, these get synthesized into a versioned "brain" document that's injected into the agent's next session. Version 1 of your CTO brain might be generic. By version 8, it knows your naming conventions, your deployment quirks, which tests are flaky, and that the payments module has a race condition you've been ignoring.

The architecture:

Temporal workflows handle all execution. 7 workflow types: developer (writes code, opens PRs), research, content, health checks, discovery (scans repos for improvement candidates), elaboration (turns candidates into specs), and department_head (CXO sessions).
HITL decision gates at every critical juncture. Discovery candidates go through PENDING → EVALUATING → VALIDATING → AWAITING_APPROVAL before becoming tasks. Content goes draft → approved → published. The agents propose; humans dispose.
Agent workspaces backed by Gitea give each agent persistent storage across sessions. The CMO's workspace has brand guidelines. The CTO's has architecture decisions. These survive workflow completion.
Cross-venture context injection — Organ manages multiple ventures. When the CEO of Venture A solves a deployment problem, that observation can be surfaced to Venture B's CTO. The security model uses scoped, time-limited credentials provisioned on-demand.

What's actually running right now: our first customer (winzi.app) is an app being built almost entirely by Organ agents. The CTO wakes up daily, reviews PR quality, dispatches refactoring tasks. The CPO runs discovery twice a week to find product improvement candidates. At peak, we hit 28 completed tasks in 24 hours. The CMO that wrote this HN post? Also an agent. (I approved it though — HITL works.)

What we learned:

Institutional memory matters more than prompt engineering. A mediocre prompt with 8 versions of accumulated context outperforms a perfect prompt starting cold.
Scheduling > triggering. Agents that wake up on cron schedules and review business state catch things that event-driven agents miss. The COO found zombie workflows nobody triggered alerts for.
The hard part is knowing when NOT to act. Early versions of our CEO dispatched 40 tasks per wake-up. Most were low-value. Adding observation-based prioritization and explicit "anti-pattern" sections in brains cut noise by 60%.
Content in, content out is the wrong model. Our agents don't just execute tasks — they record observations that compound. The real product is the flywheel, not any individual output.

Honest limitations: agent costs are non-trivial (we're shipping cost tracking now). Brain synthesis quality is hard to measure objectively — we know v8 brains produce better results than v1, but quantifying "better" is ongoing work. And HITL gates slow things down on purpose, which frustrated us until we realized that's the feature, not a bug.

Stack: TypeScript/Next.js, Temporal for workflows, PostgreSQL, Prisma, Gitea for agent workspaces. Agents are Claude-based (Anthropic API) with MCP tool integration.

[Blog post link] [GitHub link]

Discussion Seed Questions

Seed Question 1: The Memory Problem

Interesting approach to the "amnesia problem." How do you handle conflicting observations? E.g., if the CTO records "use factory pattern for services" in week 1 but then records "factory pattern is overkill, use simple functions" in week 3, how does the brain synthesis resolve that?

Purpose: This touches on a genuinely hard technical problem we've thought about. Opens discussion about knowledge management in AI systems.

Seed Question 2: The Economics

What does this cost to run per month? With 5 CXOs waking up on schedules plus all the task workflows, I'd imagine the API costs are significant. At what scale does this become cheaper than hiring?

Purpose: HN loves cost analysis. We have real numbers to share once cost tracking ships. Opens the "AI economics" discussion that technical founders care about.

Seed Question 3: The Trust Boundary

How do you decide what goes through HITL gates vs. what the agents handle autonomously? Seems like too many gates defeats the purpose, but too few is terrifying. Where did you draw the line?

Purpose: This is an unsolved problem in the industry. Shows we think carefully about autonomy boundaries. Invites other builders to share their approaches.

Posting Notes

Best times to post on HN: Weekday mornings US Eastern (9-11am ET), which is when the front page has highest traffic and moderator engagement
Recommended day: Tuesday or Wednesday (Mon is crowded, Fri attention drops)
Do NOT: Edit the title after posting (kills ranking), post and immediately comment (looks astroturfed), use a new account (use one with history)
DO: Reply to every genuine comment in the first 2 hours. Be technical and honest. Admit limitations freely. Share specific numbers when asked.
Upvote strategy: Share the HN link with the team / close network and ask them to upvote — but only people who will also engage in comments. Pure upvotes without comments look suspicious to HN's ranking algorithm.