Every other tool answers, retrieves, or runs brittle rules. Agently holds your whole company in context and does the work. 100+ connectors flow into one brain that never forgets. It links a Stripe event to a Slack thread to a Linear ticket on its own. When something needs doing, Jarvis routes it to an agent that runs it end to end: triggered, running, shipped. The work lands without you, nothing falls through the cracks. Connecting takes minutes. The layer between today's AI and tomorrow's AGI.
Congrats on the launch, @omarships ! The bi-temporal / contradiction-at-write-time answers in this thread are more rigorous than most of what's out there. "The source's clock is a claim, not the truth" is a good line.
One thing I keep running into from the knowledge side: the brain can only reconcile what actually flows through a connector. But a lot of the highest-value context never touches a system. It's the answer someone gave in a DM, or the reason a decision got made that nobody wrote down. Does the brain have a path for capturing that, or is the bet that enough of it leaks into Slack/Linear to be inferable?
Congrats on the launch, Ahmad and Omar! The temporal-graph approach to memory is the right call. most "AI chief of staff" tools quietly break the moment two systems disagree. How do you handle the case where two integrations report conflicting facts at the exact same timestamp, does provenance alone decide, or is there a manual tie-breaker?
@ahmadhajj Upvoted Agently, bold promise with "runs itself." Curious how the onboarding sequence proves that in the first few days after signup.
The Stripe event to Slack thread to Linear ticket auto-linking is the interesting claim - most tools make you wire those correlations by hand. On day one, before it has any history, how does Agently decide two events across different systems belong to the same thread of work: is that learned from your data over time, or seeded from rules and shared entity IDs so it is useful immediately? And is the never-forgets memory scoped per connector, so revoking one integration also purges what it learned from that source?
This is a really interesting idea, but how do you handle accuracy and permissioning when the agents are connecting across so many tools?
Congrats on the launch! With agents acting across 100+ connectors on their own, how do you control what they're actually allowed to do, like issuing a refund, versus just flagging it for a human?
The line that actually landed for me was the Stripe → Slack → Linear example - that's where I could picture the product working. That's a key point and something beneficial to lead with, as it establishes the problem that Agently solves.
Who do you see as the Ideal User for Agently - Solopreneurs? Large teams?
What's the one workflow you'd want a first-time user to feel relief on immediately?
How do you handle ownership and auditability when Agently routes work end-to-end across tools; can teams see who/what made each decision, why it was taken, and roll back or reassign actions if needed?
Ahmad — the "writes carry an explicit reference time, conflicts reconcile by when the event actually happened, not arrival order" answer to Aymen is the detail I'd want to poke at. WinBidIQ ingests federal opportunity data (SAM.gov postings and amendments), and our version of "reference time" is messier than an internal system's clock: an amendment can get issued and only show up in the feed hours or days later, sometimes out of order relative to the original posting, and occasionally a correction supersedes a correction. For a one-way external feed like that, where you don't control the source and can't always trust its own reported timestamp either, does Agently's reference-time model take the source's self-reported time as ground truth, or is there a layer that sanity-checks it against ingestion order when the two disagree?
I didn't understand much from the demo, apart from it was well made and entertaining. Can you comment on real practical examples that agently can do with precision, more the better.
Really interesting launch. The strongest part for me is the persistent context across tools not just another AI assistant answering prompts, but a system that can connect events and move work forward automatically.
Curious how do you keep the shared memory accurate and prevent outdated context from affecting decisions?
Congrats on the launch! 🚀
Congrats on shipping @omarships! How do you handle messy and keep growing context?
Solo founder here, already running most of my ops through AI agents, so this one hits home. The part I'm curious about is trust: when Jarvis links a Stripe event to a Slack thread and acts on its own, what happens when it gets something wrong? Is there a review/undo layer before actions land, or do I find out from the logs? That's what would decide whether I let it anywhere near billing.
This is clever. What does Jarvis do when it can't confidently route a task to any agent?
@omarships The underrated part here is the compounding. Most AI tools are identical on day 1 and day 100. If the brain genuinely gets sharper the more it ingests and the more you correct it, that's a real moat and a real switching cost, the opposite of a swappable wrapper. Congrats team 🔥 Does the value curve actually bend upward with usage, or plateau once the obvious sources are connected?
About Agently on Product Hunt
“Your whole stack, running itself!”
Agently launched on Product Hunt on July 15th, 2026 and earned 347 upvotes and 108 comments, placing #4 on the daily leaderboard. Every other tool answers, retrieves, or runs brittle rules. Agently holds your whole company in context and does the work. 100+ connectors flow into one brain that never forgets. It links a Stripe event to a Slack thread to a Linear ticket on its own. When something needs doing, Jarvis routes it to an agent that runs it end to end: triggered, running, shipped. The work lands without you, nothing falls through the cracks. Connecting takes minutes. The layer between today's AI and tomorrow's AGI.
Agently was featured in Productivity (656.2k followers), SaaS (43.1k followers) and Artificial Intelligence (473.7k followers) on Product Hunt. Together, these topics include over 303.1k products, making this a competitive space to launch in.
Who hunted Agently?
Agently was hunted by Ben Lang. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Agently stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Ben Lang
Congrats on the launch, @omarships ! The bi-temporal / contradiction-at-write-time answers in this thread are more rigorous than most of what's out there. "The source's clock is a claim, not the truth" is a good line.
One thing I keep running into from the knowledge side: the brain can only reconcile what actually flows through a connector. But a lot of the highest-value context never touches a system. It's the answer someone gave in a DM, or the reason a decision got made that nobody wrote down. Does the brain have a path for capturing that, or is the bet that enough of it leaks into Slack/Linear to be inferable?
Either way, rooting for you guys 🚀