Launched this week
Revolte
AI for Software Engineering
396 followers
AI for Software Engineering
396 followers
Revolte is for engineering teams to turn intent into production-ready software faster, safer, and with more control. Its agents plan changes, generate code, run quality and security checks, create PRs, support deployment, monitor runtime behavior, and surface risks early. Engineers approve the important decisions. Revolte handles the delivery heavy lifting. Built for higher delivery throughput across SDLC, stronger governance, and more value shipped per engineer.
Free Options
Launch Team / Built With
Revolte
I worked on the deploy and runtime side of @Revolte .
The funny thing about "deploy a service" is that it sounds simple until you see how different every team’s setup is.
Different pipelines. Different secrets. Different rollback rules. Different environments. Different observability habits. Every org has its own delivery snowflake.
A lot of agent demos avoid this by staying in a sandbox. We didn’t want Revolte to be useful only in a clean demo environment.
So the challenge was to make the agent work with the way teams already ship, existing repos, existing pipelines, existing infra patterns, while still giving them a cleaner execution layer on top.
The CLI was a big part of that.
We didn’t want engineers to feel like they had to live inside another SaaS dashboard. The CLI is meant to make Revolte feel close to the actual workflow: ticket, code, checks, PR, deploy support, without forcing engineers out of their flow.
That part took longer than expected, but I think it matters a lot for adoption 🙌
One of the earliest and most consequential decisions we made was this: Revolte would not be a coding tool with delivery features bolted on. We made the SDLC itself the product.
It sounds obvious in hindsight, but the pressure early on was to show something immediately impressive — an agent that generates a working PR from a prompt, a demo that wows in a ten-minute call. That stuff is genuinely useful. But we kept running into the same wall: generating code is not the bottleneck anymore. The bottleneck is everything that has to be true for that code to safely reach production inside a real engineering organisation.
What context did the agent have when it made that decision? Who approved it? What's the audit trail? What happens if it needs to be rolled back? How does an engineering leader defend this to their CISO, their CFO, or their board?
That's where most of our actual engineering effort has gone — not into making agents generate better code, but into making agents operable inside real teams. Audit trails, approval gates, policy-aware actions, delivery visibility, rollback paths. These are not enterprise features we added later to close deals. They are the reason engineering teams can say yes to agents at all.
The line we keep coming back to internally: AI can carry the delivery load, but engineering judgment has to stay visible and accountable. That's not a constraint on what agents can do — it's what makes them trustworthy enough to actually use.
Curious whether others building in this space have hit the same wall — the gap between "the agent works in a demo" and "the organisation can actually run it."
Triforce Todos
Revolte
@abod_rehman Thank you 🙏 This was exactly the call we agonized over. Shipping the agent first and making trust an enterprise tier would've been easier, but no engineer trusts a tool with their prod codebase if the audit trail is a paid upgrade. Had to be the foundation. Glad it landed. @parthasarathi can weigh in on this.
Revolte
@abod_rehman Thanks for the comment. Great point. Its very true. Not only Agent audit trails and approval gates are important, clear roll back path for shipped code on one click is very important.
Revolte has inbuild Platform function to ship AI developed code to Preview environment all the way to production. In any of the stage, the deployed code in any environment could be rolled back on single click.
Revolte with GIT and any feature flagging platform like LaunchDarkly gives great flexibility to teams of any size to have any day release cycle. We call this - Deploy Instantly, Release intentionally.
Congrats on the launch. The framing that resonates most is treating the full SDLC as the product rather than just code generation. That's a meaningfully different bet from the IDE-centric tools, and a harder one to build well.
Revolte
@mariselvi_ramar thank you very much.
You named the bet exactly. The full SDLC is a much harder thing to build than a better autocomplete, but the IDE-centric tools keep hitting the same ceiling: faster typing, when typing was never the bottleneck. Means a lot that the framing landed.