Agently - Your whole stack, running itself!

@bengeekly I hope someone would say that. This was a placeholder video, more of an Add, the actual video didn't end up done in time.

Honest answer: today it's managed. We run on a mix of models and keep the whole system tuned around them so it just works. That's on purpose and ties straight to the "agent is the commodity" idea, the model is the layer we think you shouldn't have to babysit, so we manage it and keep it current for you. The brain, the part that's actually your moat, is 100% yours.

We have had 290 teams use it in private betas, some of which also requested opening it up to both BYOK and self hosting. Mainly enterprise for the obvious reasons, happy to have a conversation around it. DM me

@bengeekly On the eng side: today it's managed, not BYOK. The reason is reliability, we tune the agent loop, prompt caching, and tool-use behavior around specific frontier models, and swapping in an arbitrary one changes how all of that behaves. We do have per-agent model selection internally (different jobs get different models), so the plumbing for choice already exists. BYOK and self-hosted are architecturally doable and a legit enterprise/on-prem ask, we just haven't exposed them yet, because we'd rather ship one stack that's rock-solid than a dozen that mostly work. If you've got a specific model or a data-residency constraint, happy to scope it with you.

@galdayan Sharpest question in the thread shows that you have been reading up. It's the one most tools get wrong: governance has to be recursive. A gate anyone can quietly widen isn't a gate, it's a suggestion. Our stance is that changing the rules is itself a consequential action. It should be permission-gated, logged with who changed it and when, and loosening a limit should take the same kind of sign-off as the actions it governs, not a solo Friday toggle. I won't overstate exactly where every piece is today vs on the roadmap, but this principle is non-negotiable for us, and you just described the precise failure mode we're building against. Grateful you pushed here 🙏

@shekhar_upadhaya_1 Fair — a 60-sec demo picks "entertaining" over "exhaustive." 🙂 The short version: Agently builds a company brain from your docs, CRM, Slack, tickets, etc., then puts agents on top that take real actions in your tools — grounded in that context, so drafts and answers cite the actual source instead of guessing.

Concretely:

- Sales — spot which HubSpot deals went quiet and why, then draft personalized re-engagement in your buyers' own language.

- Support — triage an Intercom inbox and post replies grounded in your help docs.

- Eng ops — turn a Slack bug thread into a labeled, assigned Linear/Jira issue with repro steps.

- Research — "what did we decide on pricing last quarter?" → cited answer from your real Notion/Drive, not a hallucination.

- Recurring work — save any of these as a one-click private skill.

Reads across ~20 connectors (Slack, HubSpot, Notion, Intercom, Linear, Jira, GitHub, Stripe…) and writes back to many too.

Happy to run a live example on a workflow you care about — tell me your stack.

@shekhar_upadhaya_1 ill give you the answer from another pov.

The hard part for a normal assistant, and the easy part for Agently, is connecting signals across tools and moving the work forward:

• A key account's usage dips in your analytics, their champion goes quiet in Slack, and a support ticket's been open in Linear for a week. Agently connects those three into one "this account is at risk" flag, drafts outreach that references the actual open ticket, and routes it to the owner. Nobody had to notice the pattern.
• A lead fills out your form. It enriches them from the web and your brain (already in the CRM? attended a webinar? at a target account?), drafts a first reply that says why they're a fit, and routes it to the right rep, before they close the tab.
• After a customer call, it writes the recap, then actually creates the Linear tickets, assigns owners, and books the follow-up, holding anything customer-facing for your approval.

The pattern: it's not answering a prompt, it's watching your tools, connecting dots a human would miss, and moving work forward with your sign-off on anything that ships. Want me to pick one and show it live?

@medal411 You've nailed the exact hard case, and honestly it's the line between a toy and a system: a one-way feed whose own clock you can't trust, out-of-order amendments, and corrections superseding corrections.

Short answer: we don't take the source's self-reported time as ground truth, and we don't collapse it into ingestion order either. It's bi-temporal, so both are first-class and stored separately. The source's reported time is treated as a claim (an attribute of the event), and our ingestion order is always retained independently, which is what lets the two disagree without forcing a destructive pick. When a correction supersedes a correction, each one invalidates the prior with a validity interval, so the lineage stays intact even when they land out of order.

Where I'd rather be precise than hand-wavy: for a feed like SAM.gov, where the timestamp itself is unreliable, the right reconciliation isn't a global rule, it's source-specific logic in the adapter. That's where you'd encode "order on the amendment/version sequence, not the reported clock," since those postings usually carry a monotonic version that's safer to trust than the timestamp. Some of that is turnkey, some we'd tune for your exact feed, and I'd rather scope it with you than oversell one answer.

Genuinely a fun problem. DM me and let's go deep.

@medal411 Ahmad gave you the how, here's the principle: a source's clock is a claim, not the truth, so the brain reconciles reality rather than trusting whatever landed last. For a feed as adversarial as SAM.gov that's not a generic setting we'd flip, it's something we'd tune with you. Genuinely our favorite kind of hard, let's get into it.

@yahia_bakour3 Thanks Yahia, appreciate the support! 🙏

@yahia_bakour3 Appreciate you Yahia, one step closer to figuring out AGI. Only saying this to raise an anthropic sized round, hopefully they dont see this

@irahimiam The key is that agents don't get root access to your tools. On every connector they get a defined, allow-listed set of actions, not free rein, so "100+ connectors" never means unbounded power across 100 systems. Within that, anything consequential like issuing a refund sits behind your approval by default: the agent flags it with its reasoning and waits, while low-risk read/draft work runs on its own. Control is by design, not by trusting the model to behave. Ahmad can go under the hood 👇

@irahimiam Mechanically it's two layers. First, capability is bounded: each provider has an action catalog that's an explicit allowlist with schema validation, so if an action isn't in the catalog, the agent can't call it, full stop. The surface across 100 connectors is defined, not open-ended. Second, within what's allowed, a policy gate classifies each call before it runs, consequential actions like refunds route to human approval by default (and you set the conditions, e.g. auto under $X), while reversible ones execute autonomously. It's enforced in the runtime, not asked of the prompt, so "issue vs flag" is a rule, not a hope.

@ramsey_fahed The undocumented "why" is the moat, and no, we don't bet on it leaking into Slack. Connectors wise, we will keep expanding to capture as much context as possible outside the platform. The other way we capture context is through the use of the platform, recursively inputting data from your work into the brain. You just tell Jarvis something, and it lands as a fact with your name on it, same weight as anything a connector brings in. Better: when an agent doesn't know why, it asks. Every gap becomes a question, every answer becomes memory. The brain gets sharper the more you use it instead of dying when someone leaves. 

@ahmadhajj Building a the future for founders!

@saadetpz2y That auto-linking is entity resolution on hard signals (shared email, domain, IDs), so it connects the same customer/thread/ticket across tools with zero setup. For the messy stuff the rule is: degrade to asking, not guessing. Strong signal it acts, weak or conflicting it flags and defers to you instead of forcing a match. Edge cases get surfaced, not papered over.

@saadetpz2y Appreciate that 🙏

Here's the belief behind it in a less technical manner: messy context isn't an edge case, it's the normal state of every company. Tools disagree, data goes stale, half of it lives in someone's head. So we made a deliberate call early: the system should be honest about what it doesn't know rather than confidently wrong.

Sounds small, but it's the whole product. The fastest way to lose a founder's trust is one confident action taken on bad data. So when context gets messy, Agently narrows down and tells you instead of guessing and shipping. We'd rather look a little less magic in that moment and earn the right to run more of your company over time.

The "freaked me out in a good way" part is the payoff of getting the boring foundation right. Glad it landed 🙌

@erkanaltnbkaas Good news: the enforcement layer this needs already exists. Every tool call runs through a policy gate before it executes, so "this action requires a human" is already how we stop consequential stuff from shipping. What you're describing is making that gate conditional on the tool's inputs (amount > $X, refund on an enterprise account, more than N per day → require approval). That's a policy predicate, and it's exactly the layer we're building user-facing rules on top of. The rails are there, we're putting the dashboard on them. Sharp spec 🙂

@erkanaltnbkaas You basically just described our philosophy back to us, so this is a yes. You draw the line, and it shouldn't be a blunt on/off, it should be conditional. "Handle refunds under $50 on your own, ping me above that" is exactly the kind of rule we want in your hands. The point of Agently isn't maximum autonomy, it's autonomy you're never surprised by. You won't get blindsided by something big, because you decide what "big" means. Thank you for putting it so clearly 🙏