SureThing.io - Autonomous agent that communicates results like a human

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Everyone's running AI agents. Seldom hitting their business goals. AI isn't the bottleneck anymore. Humans are. SureThing is a General AI Agency. Paste any GitHub skill — it becomes a team you can @ anytime. One persistent memory across your COO, CMO, and CTO — zero silos. Agents that report up like humans. So you can finally run it like a CEO, not a debugger. With SureThing, now hit your business goals at inference speed.

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the "report up like humans" angle is interesting. we're constantly context-switching between different AI tools at our agency, and the memory silos are real. curious how the persistent memory actually works across different agent roles - is it more like shared context or do they actively reference each other's work?

 everyone needs a "high effciency" AI employee. some of our power users asked us to write a book about The 7 Habits of Highly Effective AI Agents h ha

  Each agents save their own memories and then share memories with each other if needed(they will collaborate by themselves without human in the loop to complete tasks)

the "chairman not debugger" framing hits differently when you're a solo founder wearing all three hats. COO, CMO, CTO — same person, same head. agents that report up instead of waiting to be queried is the actual shift.

 yes, solo founders so far are our core ICP. They really need AI's help on a daily or even hourly basis.

 Thanks, Alex. You got the point! Try it further and share with us more insights!

The "Karpathy research agent / Garry Tan gstack" framing nails the real friction - discoverability is fine, but most starred repos rot, and "right there" usually means broken installs. Curious about the QA layer. When you turn a GitHub skill into a teammate, what catches dead repos vs. the working ones - runtime tests, community signal, or trust the maintainer? And once a skill drifts (a dep breaks, an API moves), does the employee silently fail or know to escalate?

 Good points. Step one is standing on the shoulders of giants — importing external skills from the open ecosystem so every agent starts with proven capabilities, not from scratch. But the harder, longer game is what really matters: getting the agent to self-reflect, catch its own failures, and actually learn from them over time. That's the piece we're committed to investing in for the long run.

Sharp positioning — “run it like a CEO, not a debugger” lands well. Turning scattered GitHub skills into agents that actually report outcomes is the missing layer most tools like OpenClaw still don’t address. Can't wait to try it!

 Hire the best talent in the world!

I hope you do know reddit has a very strict policy for AI generated content & blocks pretty aggressively. Did you guys even tested that before pitching the idea to users?

Maker

 Totally agree, Reddit is one of the strictest, and that's a fair callout.

Each platform has its own rules and we work hard to stay inside them. Take the X reply feature we shipped a few days ago(you can find it here: ): X's API blocks you from replying to posts that don't mention you, but X also offers a path smoother than copy-paste, so we built around that and made it two step shorter than what other Agent offer.

Our goal is to push the user experience as far as possible without crossing any platform's line. No magic here, making high quality content, offering value to the open community, just a balance we keep iterating on.

Maker

btw, we're testing every possible integration path with other platforms, so if there's anything we've missed, please call it out. We always take it as an opportunity to make the product better for users.

 Fair point Gaurav. We do noticed that and provided some best practices guide for some of reddit automation users.
Thanks so much for this advice, we will keep a closer eye on Reddit policy and see whether we should still keep that integration online.

what's the difference with the current agents that exist?

great question Rebecca. We focused on real problem solving ie long horizon tasks, so human-in-the-loop and agent’s long term memory are the key differentiators for such scenarios.

 Most agents suggest. We ship. 😉

Interesting concept! I'm curious whether I could interact with SureThing via Slack or WhatsApp instead of opening up a separate website/app to chat.

we do support connection with Slack. But we recommend using our app (iOS and android all supported) since we have done a lot on human-in-the-loop interactions.

 Sure thing! Try it more and share with us your insights!

the 'reporting line vs terminal' framing is sharp. the hard part nobody's nailed yet is getting the agent to be honest about what it almost-did, especially silent partials like 'i created the event' when the api returned 403. how do you surface that in the dashboard?

 We separate "what the agent claims" from "what the tool actually returned" — any mismatch gets flagged as a partial in the timeline.

  the claim/return diff in the timeline is exactly the right primitive. one followup, how do you handle partial-success cases like a tool returning 200 with an error in the body? is the predicate user-defined per tool or do you have a default schema?

 You nailed it - An agent saying "done" when the API returned 403 is worse than failing loudly. Our approach: when an agent hits a wall, it remembers the failure pattern and proactively verifies execution next time before reporting back. So instead of blindly claiming "event created," it learns to check the actual result first and return what really happened. Still evolving, but the error-memory loop is already making a real difference.

  the error-memory loop is the interesting part. how do you scope what counts as 'the same wall' for memory purposes, by tool + endpoint, by error code, or something fuzzier like 'this auth provider tends to silent-fail on token expiry'? that distinction is where most agent platforms i've seen get tripped up.

The AI team concept from SureThing.io looks bold, but I’m curious whether the real-world execution will actually be that powerful or just a fancy wrapper on top of LLMs.

Can your service find Reddit posts on a specific topic and then write a reply based on my preferences and the context of the post?