YAGNI 是一款 AI 驱动智能体团队管理工具，像管理员工一样分配职责和护栏，实现自主任务执行，适合需要自动化工作流的企业。
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PH 用户
Hey Product Hunt 👋 Jack here, founder of YAGNI.
The best teams I've been on ran on trust. It's what makes a team fast, and it's the hardest thing to build and the easiest to break. I've spent twelve years building and running teams, through two acquisitions, a Techstars batch, and orgs across healthcare, government, and startups big and small, B2C and B2B. That lesson held everywhere.
AI changed my own output more than any tool ever has. But it brought the trust problem back in a new form. More output means a worse signal-to-noise ratio, and the moment you try to put agents to work inside a business you hit a wall: where do you even start? Every tool assumes you'll be directive. Either you prompt each task ("do this thing"), or you wire up an if-this-then-that graph and hope you predicted the work correctly. That's not how anyone actually runs a team.
YAGNI takes the approach I learned managing people. You hand a Team a real slice of the business to own and give it the structure you'd give a new hire: Responsibilities, a Number it's measured on, Commitments with real deadlines, and Rhythms (its recurring work). Then you manage the early work closely. It drafts, you edit and approve, and every correction teaches it how you'd do it next time.
As its track record grows, it climbs a ladder you control: Training → Supervised → Autonomous. At the top it carries the routine, reversible work on its own, every action leaves a Receipt from the source system proving where things actually stand, and you stay in the loop for the calls that matter. Irreversible and high-risk actions stay behind your approval forever, at every level. That's a design commitment, not a model limitation.
Two things I decided early, because I'd want to know them as a buyer. First, it runs exclusively on open-weight models, so it's cheap enough to let Teams work continuously instead of sparingly. Second, it only uses first-party, official integrations, so your data is read where it lives, never sold, never used to train a model.
Humans and Teams work off the same context, and it all collates onto your Front Page, published as a Brief morning, midday, and evening. Monday's status meeting starts at the decisions instead of the recap. Dive into any work with a persistent chat sidebar to so that you always have the context to make the decision.
Who it's for: founders and operators who've become the bottleneck (the person everything routes through), and lean teams who want real leverage from agents without babysitting them.
What to try first, and don't sign up: go to https://yagni.app/build-your-team, paste your company's website, and about 30 seconds later YAGNI hands you a Brief with your first Teams already drafted: what it would own, which tools it would read, and what it would do in week one. Free, anonymous, no card. If the Team it drafts is wrong for your business, I genuinely want to hear why.
Paid plans start at $99/mo when you're ready to put a Team to work. Get 60% off ANY plan for 6 months with code YAGNIPH (60% because we can offer AT LEAST 60% savings of frontier models).
I'll be here all day. Ask me the hard ones: pricing, security, "isn't this just a wrapper," what happens when it screws up. I'd rather answer those in public than in a sales call.
PH 用户
Congrats on the launch, @jackcollinshq — framing this as "manage like humans" instead of "hire an AI employee" genuinely reframes the category for me.
The piece I keep circling on is the Number each Team is measured on. Giving a Team a single metric to own is exactly how you'd brief a real hire, but it's also how you get Goodhart problems: a Sales Team measured on "qualified meetings/mo" has every incentive to quietly loosen what counts as qualified over time, and the Receipts would all still look clean. How do you keep a Team from optimizing the metric at the expense of the intent behind it, is there anything watching the gap between the Number climbing and the actual downstream outcome (closed deals, not just booked meetings)?
PH 用户
The Training → Supervised → Autonomous ladder is the part I keep thinking about — earning autonomy from a readable track record is such a thoughtful framing for trusting agents with real work.
A couple of gentle questions from an evals angle, if you have a moment. What signal actually promotes a Team up a rung — is it approval rate, and if so, how do you gently tell apart "approved because it was right" from "approved because I was busy and didn't look too closely"? I imagine that's a tricky line to draw.
And on the adversarial review step: does that reviewer run on the same open-weight model as the executor? Would love to understand how you keep it from leaning toward a rubber stamp when critic and author might share the same blind spots.
Really nice launch, congrats @jackcollinshq 👌
PH 用户
Congrats @jackcollinshq👏 on hitting the front page! the choice to run this entirely on open-weight models is a massive selling point for keeping costs scalable, are you guys hosting these models on your own cloud compute nodes or can we host the agent workers inside our own private cloud setup for compliance?
PH 用户
the 3-similar-edits threshold is a smart way to avoid over-fitting to one correction, but what happens after a rule gets promoted and it turns out to be wrong two weeks later, like it was right for the cases you saw but breaks on an edge case nobody corrected yet. is there a way to see which rules are actually firing and roll one back, or do you have to notice the bad output first and trace it back to the rule that caused it?
PH 用户
这个从培训→监督→自主的阶梯式设计，特别是把"已发布的编辑"视为比"静默批准"更强的证据，比大多数"信任智能体"的产品方案要犀利得多。我一直从只读侧而非行动侧在思考同样的问题：为管理多个业务的创始人设计的AI参谋长，它获得的不是行动的自主权，而是将某件事陈述为事实 vs 标记为"需要审核"的权利。撤销会消耗已建立的信任——这是个很棒的机制。你有没有遇到过某些团队类型，即便是监督级别的信任后来也被证明是误判的？那些撤销信号来得太晚、没能阻止实际损害的案例？
The best teams I've been on ran on trust. It's what makes a team fast, and it's the hardest thing to build and the easiest to break. I've spent twelve years building and running teams, through two acquisitions, a Techstars batch, and orgs across healthcare, government, and startups big and small, B2C and B2B. That lesson held everywhere.
AI changed my own output more than any tool ever has. But it brought the trust problem back in a new form. More output means a worse signal-to-noise ratio, and the moment you try to put agents to work inside a business you hit a wall: where do you even start? Every tool assumes you'll be directive. Either you prompt each task ("do this thing"), or you wire up an if-this-then-that graph and hope you predicted the work correctly. That's not how anyone actually runs a team.
YAGNI takes the approach I learned managing people. You hand a Team a real slice of the business to own and give it the structure you'd give a new hire: Responsibilities, a Number it's measured on, Commitments with real deadlines, and Rhythms (its recurring work). Then you manage the early work closely. It drafts, you edit and approve, and every correction teaches it how you'd do it next time.
As its track record grows, it climbs a ladder you control: Training → Supervised → Autonomous. At the top it carries the routine, reversible work on its own, every action leaves a Receipt from the source system proving where things actually stand, and you stay in the loop for the calls that matter. Irreversible and high-risk actions stay behind your approval forever, at every level. That's a design commitment, not a model limitation.
Two things I decided early, because I'd want to know them as a buyer. First, it runs exclusively on open-weight models, so it's cheap enough to let Teams work continuously instead of sparingly. Second, it only uses first-party, official integrations, so your data is read where it lives, never sold, never used to train a model.
Humans and Teams work off the same context, and it all collates onto your Front Page, published as a Brief morning, midday, and evening. Monday's status meeting starts at the decisions instead of the recap. Dive into any work with a persistent chat sidebar to so that you always have the context to make the decision.
Who it's for: founders and operators who've become the bottleneck (the person everything routes through), and lean teams who want real leverage from agents without babysitting them.
What to try first, and don't sign up: go to https://yagni.app/build-your-team, paste your company's website, and about 30 seconds later YAGNI hands you a Brief with your first Teams already drafted: what it would own, which tools it would read, and what it would do in week one. Free, anonymous, no card. If the Team it drafts is wrong for your business, I genuinely want to hear why.
Paid plans start at $99/mo when you're ready to put a Team to work. Get 60% off ANY plan for 6 months with code YAGNIPH (60% because we can offer AT LEAST 60% savings of frontier models).
I'll be here all day. Ask me the hard ones: pricing, security, "isn't this just a wrapper," what happens when it screws up. I'd rather answer those in public than in a sales call.
The piece I keep circling on is the Number each Team is measured on. Giving a Team a single metric to own is exactly how you'd brief a real hire, but it's also how you get Goodhart problems: a Sales Team measured on "qualified meetings/mo" has every incentive to quietly loosen what counts as qualified over time, and the Receipts would all still look clean. How do you keep a Team from optimizing the metric at the expense of the intent behind it, is there anything watching the gap between the Number climbing and the actual downstream outcome (closed deals, not just booked meetings)?
A couple of gentle questions from an evals angle, if you have a moment. What signal actually promotes a Team up a rung — is it approval rate, and if so, how do you gently tell apart "approved because it was right" from "approved because I was busy and didn't look too closely"? I imagine that's a tricky line to draw.
And on the adversarial review step: does that reviewer run on the same open-weight model as the executor? Would love to understand how you keep it from leaning toward a rubber stamp when critic and author might share the same blind spots.
Really nice launch, congrats @jackcollinshq 👌