Launching today
YAGNI
Proactive agent teams you manage like humans
73 followers
Proactive agent teams you manage like humans
73 followers
AI today is reactive: it waits for your next prompt. YAGNI is proactive agent Teams you manage like people. Give a Team responsibilities and guardrails, review its work, and it earns autonomy through a track record you can read, while you keep the calls that matter. Paste your company's URL and YAGNI drafts your first team in seconds. You aren't gonna need more software. You need a team that gets better every week. Become a self-improving company.
YAGNI
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.
@jackcollinshq I’m really curious about the “every correction teaches it” part. What actually happens after I edit or reject something? Does YAGNI save it as memory, turn it into a rule, or use it in some other way? And how do you avoid teaching the agent the wrong general lesson from one very specific case?
YAGNI
@gleb_rosev Thanks for the question! Corrections work at two levels:
Level one: when you edit a draft before approving it, YAGNI saves the before/after pair. That single correction shows up as an example in context the next time the Team drafts that same kind of thing. So one edit teaches immediately, but only as "here's how they revised this exact kind of output", never as a general rule.
Level two: a correction only graduates into a rule when it's a pattern. A background pass looks for three or more similar edits (similar in both what it drafted and how you changed it) before it proposes a rule. The rule is written in plain language, starts applying so the correction compounds forward, and shows up in your Feed as a proposal you can adopt or dismiss. Nothing is learned invisibly... you can read every rule the system is following.
For code, the correction signal is the merge itself. When a Team's PR gets merged, YAGNI diffs what the agent proposed against what actually landed; whatever you changed before merging is captured as a correction, automatically. Mid-run steers ("actually, use the existing helper") are banked as decisions with your consent. Both are fed back into future runs as cited sources: the agent has to cite which past decision or correction informed its answer, so you can always see why it did what it did. Corrections don't kick in until there are enough of them to be a pattern, so one unusual PR doesn't become a doctrine.
Rules also have to keep earning their spot. Every rule is tracked against outcomes it was in context for. If a rule correlates with you reversing the agent's work, or simply never fires, YAGNI suggests retiring it. So a wrong lesson doesn't just sit there forever; the same evidence loop that created it can kill it.
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?
YAGNI
@priya_kushwaha1 Thank you so much! Right now we are hosting our own as well as using US-hosted providers like Fireworks.ai and Together.ai for the open-weight model inference.
We can offer BYOK for those who want to manage their own LLM inference, and happy to support private cloud setup for compliance as well!
Let me know if you have any other questions - and thanks again!
@jackcollinshq that's awesome BYOK and private cloud support are huge pluses for enterprise users.
all the best 👏
I run Claude Code and a couple of agents most of the day, so "manage like humans" resonates. The part I would love to see solved: knowing when an agent is genuinely blocked and waiting versus just thinking. Does the management layer surface that, or is it more about task assignment?
YAGNI
@virko_kask glad to hear it resonates with you!
Yes, work that "Needs You" is flagged very clearly in the app.
The YAGNI Teams work through a loop like this for most of their work:
Map - gather context about the work item)
Plan - YAGNI presents a plan for your review and edits
Execute - Complete the work
Adversarial Review - Specific adversarial agents probe the work for issues, especially with the context of your previous work and corrections
Final Review - your place to review the work before it's sent / pushed / merged
At the Plan and Final Review steps the work has a specific "Needs You" flag that automatically routes it to the top of the list. Additionally, we collate everything that needs you into a simple "Feed" so you can easily and efficiently unblock your YAGNI teams.
I hope that answers your question, but happy to provide more context as well. I appreciate it!
Pasted my site URL and got a surprisingly thoughtful first team draft within seconds, not just a generic org chart. The track record idea for earning autonomy feels like something I'd actually want before handing off real decisions.
YAGNI
@zcanuslusoiuah I'm so happy to hear that!
The "empty room" challenge is one I put a lot of effort into. YAGNI needs context about your business to do its best work, and as a user it's always a pain to give a system a bunch of context just to get started.
So yes, we are building each Teams draft truly custom to your company as you get started, and of course you can edit and tweak their Goals / Responsibilities / Guardrails / etc as you go.
Let me know if you have any other questions!
the rule-promotion system (3+ similar edits before it becomes a rule) is the part that stands out to me, most "agent memory" pitches are vague about how corrections actually turn into behavior change, this is the first concrete answer I've seen. one thing I'd want to know: if two different people on the same team review and correct the same kind of work differently, does the Team end up with conflicting rules, or does it pick up whoever's corrections happen to hit the 3-similar-edits threshold first?
I like how YAGNI emphasizes proactive agent teams, but I'm curious - how do you envision the 'guardrails' working in practice? Would love to see some examples of what that looks like in a real-world setting.