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Revolte
AI for Software Engineering
1.3K followers
AI for Software Engineering
1.3K 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.
This is the 2nd launch from Revolte. View more
Interactive Sessions by Revolte
Launched this week
Different work needs different AI oversight, and Revolte gives you both. Our Interactive Sessions let you drive the full lifecycle with the agents, step by step: architecture, code, tests, staging, deploy. You approve every step. Autopilot mode hands off a Jira ticket end-to-end, with agents planning, coding, opening the PR, and deploying. Same workspace, same governance: plan approval, inline diffs, cost caps, and audit trails, built in. Hands-on when you want control, hands-off when you don't.
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Launch Team / Built With
Revolte
Hey PH 👋 Raj here, co-founder & CEO of Revolte.
Every AI dev tool makes you pick a side. Cursor is hands-on: you're in the loop for every keystroke. Devin is hands-off: you hand it a task and check the result. Both are good, but neither is enough on its own for a real team. Because different work needs different oversight.
A tricky refactor in your payments service? You want hands-on. A backlog of dependency bumps? Hand it off. The real question isn't "do we adopt AI?" It's "how do we adopt it across different work with different risk profiles?" Nobody's answering that.
So we built Revolte to give you both, in one workspace.
Autopilot mode (from our previous launch) takes a Jira ticket and runs it end-to-end through PR and deploy, with engineers approving the meaningful steps.
Today we're adding Interactive Sessions: a tabbed workspace where you drive the full lifecycle with the agents directly, architecture, code, tests, staging, deploy. One session per task, you approve every step. No Jira required. Sign up and start in under a minute.
Same governance layer for both. Plan approval before code, inline diffs before merge, cost caps before deploy, audit trail on every action.
Hands-on when you want control, hands-off when you want throughput. Same platform, same procurement conversation.
Would love the hard feedback, especially from engineering leaders figuring out how to adopt AI across teams with different risk appetites. That's the problem we built this for.
@rajagopalanar signed up out of curiosity and had a session going in under a minute with non setup that low barrier to try is rare and it matters good work team.
Revolte
@priya_kushwaha1 We wanted to create ease of use and less barrier for users to start using product. Glad to know that it reflects. Thanks
@rajagopalanar congrats on the launch,best of luck you guys. i supported you guys
Revolte
@elias_leo1 Thanks for the support
@rajagopalanar what happens when an agent makes a wrong architectural decision ?
Revolte
@dipanshu_kushwaha5 no commits go into codebase with out PR approval. During PR approval, you could ask use our CLI - Revolte Code - equivalent of claude code to ask the agent to rework on the architecture.
Think of AI workflow as junior engineer and human developer with CLI as senior engineer. If workflow(junior engineer) makes mistakes senior(Dev with CLI) can correct it.
We have confidence scoring mechanism which helps AI workflow to get better and better.
Revolte
@vipul_kumar1280 Thanks. Glad you find this useful.
Honestly i would be more interested in the failure cases than the happy path. Whats the most comman reason an agent run gets stopped?
Revolte
@manjesh_yadav1 Agents dont get stopped but the output that are provided by agents are not what we anticipate. This is usual expected scenario. The way to go about this is having to tweak prompt, ground the agent and have clear context. If any one if this is not good, the results will not be good. The way to improve output is to improve these. The way to measure these is confidence score. So you could look at confidence score in Revolte to see if you would be assured results
Softorino 💻📲
Nice that Revolte does the delivery grunt work but leaves the big calls to engineers. What happens when one of its PRs needs a security fix after it's already merged?
Revolte
@yelyzaveta_kibets Great question. Revolte doesnt allow any PR to merge without human approval. Developer has window to approve the PR. If the dev misses the PR and approves it, same dev can use CLI tool to fix the same.
Also, you could create a vulnerability and pen testing workflow in Revolte and let Revolte suggest you fix which on approval could be merged again for holistic security assurance.
Looks exciting! My only question is when do I trust an AI agent enough to let it touch my codebase?
Revolte
@new_user___2432026393c9df85f17fd58 Great question. When you can control everything that AI does. If you just see AI as a junior engineer, who knows enough to do development but not enough to be trusted to ship to production, thats exactly the state AI is now. We have been following quality gates in SDLC all along. If you follow the gates and have control you could trust AI agents.
Revolte gives full control to you to see what AI agents does, and lets you to control approval flow.
Buffup.AI
For me the biggest question would be deployment confidence. what happens when an agent hits something unexpected mid deploy?
Revolte
@sansa_grey Thats a good question. We dont allow agents to directly deploy. There are clear quality gates. Agents first deploy in preview environment, developers validate the code & output and then approve the code to be pushed to main branch. Then the main branch is deployed in production. This helps to have good control over ai. Hope this answers your question.
Buffup.AI
@rajagopalanar That clears it up. I like the preview-first approach, especially having developers validate both the code and the output before anything reaches production. It feels like a good balance between automation and control.
Revolte
@sansa_grey Thanks thats great question. We don't allow any agent directly to deploy rather, we allow agents/workflows only to plan, architect, code/write test cases. All of these are artefacts which are saved as text or code in your repo. Code output will be always raised as PR from agent or workflow. On approval, rest of the deployment steps works. For all the deployment steps, we have automated with engineering logics in the product rather than using AI. This assures certainty.
Outcome
@rajagopalanar @parthasarathi_raghavan @arulwatson really neat product - how has adoption been thus far - curious how steep the switching costs are for current dev teams?
Revolte
@dzaitzow thank you! The adoption has been great so far and we have been getting some incredible feedback shaping our product & roadmap. The switching costs are near zero, since you can bring in your skills from other tooling and have this running with minimal overhead.
Revolte
@dzaitzow thanks. This is designed for teams who want to run factory model for their engineering teams. We have good tractions and easier adoption for teams who want to run factory model. But considering shift from traditional sdlc cycle to ai driven factory model is lot of work. Today companies are trying to build factory model with Claude, openclaw and inbuilt context management setup which takes lot of trial and error. We help these companies buy proven solution from Revolte vs building(trialing).
I can see Interactive Sessions being useful for unfamiliar codebases. does the agent keep context across those sessions?