Launching today
Revolte
AI for Software Engineering
233 followers
AI for Software Engineering
233 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.
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Launch Team / Built With
Revolte
Hey Product Hunt 👋
Raj here, founder & CEO of Revolte.
For years, I’ve built and worked with engineering teams where the same pattern kept showing up:
Writing code was rarely the only bottleneck.
The real drag was everything around the code: setting up environments, running tests, managing deployments, fixing broken builds, triaging incidents, checking quality, and keeping delivery moving across disconnected tools.
Coding assistants have made developers faster inside the IDE.
But software delivery is much bigger than the IDE.
That’s why we built Revolte.
Revolte is AI for Software Engineering, an agentic platform that helps engineering teams move from intent to production with humans in control.
Give Revolte a ticket or requirement, and its agents can help plan the implementation, work against your actual codebase, generate code, run checks, create the PR, support deployment, monitor runtime behavior, and surface what needs attention.
But the important part is this:
Revolte does not remove engineering judgment.
Every meaningful change goes through human review. Engineers see the diff, the reasoning, the checks, and the rollback path before anything moves forward.
We built it this way because production software cannot run on blind automation. It needs context, governance, and control. Our belief is simple:
AI should not just help engineers type faster.
AI should help engineering teams ship better software faster.
Revolte is built for teams that want more delivery throughput without adding more delivery chaos.
We’d love for you to try it, break it, test it on something real, and tell us where it falls short.
https://revolte.ai/
And if you’re an engineering leader thinking about how agents can safely enter your SDLC, I’d be happy to talk through the governance side with you.
Thanks for checking us out,
Raj.
@rajagopalanar congrats on the launch Raj. How do you avoid a pile up at the judge gate (agent coded and now there's a stack of reviews)?
Revolte
@zolani_matebese honestly, the pile-up at the judge gate is the thing we lose sleep over, so great that you went there straight away.
A few things we've built around exactly this:
First, the workflows are configurable. You can gate how many run in parallel, so you're not suddenly drowning in 20 PRs at once. Teams pace the output to match their review capacity.
Second, Revolte's output isn't "here's some code, good luck." Each workflow comes with full context: what was done, why, what the tests cover, what the security scan flagged. Review goes a lot faster when you're not reverse-engineering what the agent was thinking.
Third, trust builds over time. Teams start spotting which workflow types consistently produce clean output and streamline approval for those, keeping the deeper review for the higher-risk stuff.
And honestly, the pile-up problem is often a sign that the team's review culture needs a rethink alongside the tooling, which is a conversation we actively have with teams onboarding to Revolte.
Really appreciate you pushing on this one.
The approval gating for critical decisions is the right design. Most SDLC agents fail because they either go fully autonomous (risky) or require constant hand-holding. We've felt that tension building agents that touch production. Having it handle quality checks, PRs, and deployment monitoring while preserving human review for high-stakes calls is solid. How does it decide what triggers an approval gate? Is that configurable per repo or risk-scored?
Revolte
@retain_dev Really appreciate the comment — you’ve clearly felt this pain firsthand and that framing is spot on.
Here’s a simple development workflow in Revolte to answer your question.
A feature ticket comes in → the planning agent breaks it down → coding agent implements it → tests are auto-generated → SAST/DAST runs → PR is raised. The dev reviews and approves at the end. No hand-holding in between.
Now the gate question: that’s configurable. Teams define approval rules at the workflow level — a routine feature build on a low-risk service can flow straight through, anywhere a particular artifact to be finally used in the product as code or test script or design, the it will go through approval gate. No output will be used or merged to exisiting repository finally without human approval.
The risk-scoring side is something we’re making smarter over time — more context-aware, less purely rule-based.
But the principle stays the same: full autonomy where it’s safe, human judgement where it matters.
let me know if this answers the question
Revolte
Dear @retain_dev Interesting question actually ., while pretotyping some of our workflows, we started noticing the same challenge & pattern during work within finance environment.
Autonomy there became heavily risk-dependent (along with few other OPA rules).
Say for lower-risk features — like their internal dashboards, FAQ/content updates, coupon dashboards, and non-critical UI flows — the workflows were designed with much lighter Human approvals and higher AI autonomy.
But workflows touching their Core new features -> say., beneficiary logic, critical infrastructure pieces - introduced much stronger validation layers and human checkpoints before anything could move forward.
That’s probably where we feel engineering AI systems evolve over time — systems themselves becoming smarter in identifying which workflows can move autonomously versus which ones need stronger approvals and human oversight. That balance Raj mentioned — autonomy with governed checkpoints — is exactly the direction we’re trying to preserve in REVOLTE.
Curious how you’re thinking about this in your own agent workflows today — are you leaning more toward static approval rules, or dynamic risk/context-based gating?
AI that works inside the engineering workflow is a different bet than AI that sits alongside it. The context problem in code is real. Getting it to reason about system trade-offs isn't just a file-level concern. We've been building in the customer success for developer tool companies space, and Revolte touches on something we think about a lot. What's your approach to handling context across large multi-repo codebases?
@shivam_jaiswal21 Very aligned with that view. We don’t think the context problem gets solved by pushing more files into a bigger prompt window.
Our approach is to treat context as a system graph, not a repo dump. For larger multi-repo environments, Revolte maps the service boundaries, dependencies, APIs, ownership, deployment paths, PR history, tickets/specs, and runtime signals around the change being made. The agent then pulls the relevant slice of context for that workflow instead of trying to reason over the entire estate at once.
The important part is also separating “code context” from “delivery context”. A change is rarely just about the files touched. It has impact across tests, environments, release rules, observability, and sometimes adjacent services.
We are still early in how far this can go, but that is the bet: context has to be structured around how engineering teams actually ship, not just how code is stored.
Would love to compare notes given your lens in the dev tool space.
Revolte
@shivam_jaiswal21
Really glad this resonates — and you’ve put your finger on something we spent a lot of time on.
Here’s how we approach it: for every codebase, Revolte maintains a separate entity called an App/Service. Each one builds its own context — the structure, dependencies, patterns, and history specific to that service. That context isn’t shared or muddled across repos; it’s scoped and owned.
When a workflow runs, the agents work within that context. When a dev picks up Revolte Code (our CLI) to review and approve the output, they’re working from the same context — so there’s no gap between what the agent understood and what the human sees.
But the part we’re equally serious about is context poisoning — because bloated, unstructured context is almost as bad as no context. Agents hallucinating based on irrelevant noise is a real failure mode. Our approach is a clear structural flow built into Revolte that governs how context is built, what gets included, and what doesn’t. Meaningless context without reference doesn’t make it in.
It’s an area we’re continuously tightening — because getting the reasoning right at a system level, not just file level, is what actually makes autonomous workflows trustworthy at scale.
mailX by mailwarm
How do you decide what counts as an important decision that requires engineer approval in comparison to something the agent can auto apply?
Revolte
@othman_katim
Great question — and honestly one we debated a lot internally.
The agent never auto-applies anything. Every autonomous output — whether it's a code change, a test suite, a design decision, or a deployment config — is raised as a pull request to the relevant human stakeholder. Developer, tester, architect, whoever owns that stage of the workflow.
The PR isn't only a quality check. It's an ownership handoff. It makes the human the deliberate decision-maker. The workflow is designed so that people are defining intent upfront and reviewing outputs at the back — and the AI is doing the actual work in between.
We believe that's the right inversion. Not "AI assists the human" in the traditional copilot sense, but "human sets direction, AI executes, human signs off." The accountability stays with people while work is done by AI.
It also means the system is naturally auditable — every decision has a human who reviewed and approved it, with full context of what the AI produced and why.
We're still evolving how this works across different team roles, so genuinely curious whether that model resonates with how your team thinks about it. 🙏
@elvis_onjiko Fair question. As you pointed out, this is exactly where most AI dev tools start to struggle as of today.
Our experience is that older production codebases need bounded context, not a giant repo dump into a prompt. Legacy systems usually have old patterns, partial docs, weak test coverage, tribal knowledge, and deployment rules that are not obvious from the code alone.
So Revolte approaches it in smaller controlled slices. It builds context around the change area: repo structure, service boundaries, dependencies, relevant files, test paths, PR history, and where possible, runtime or deployment signals. The goal is not to magically understand the whole estate on day one. It is to make safer changes with the right context and human review points.
Messier codebases are actually where we think this workflow layer matters most. The value is not just writing code, but knowing what to touch, what to avoid, what to test, and where a human should approve.
This feels like Cursor meets Jarvis 👀 How accurate is the task execution in real world coding workflows?
@nithin_raju1 Good question and an important distinction — task execution accuracy is really asking whether the agent actually completes the job end to end, or gets lost halfway through.
The honest answer is that the pipeline structure is what makes execution reliable in practice. There's a planner step that runs before any code gets written — it maps out what the task actually involves and what done looks like. That upfront planning step is what prevents the agent from going off in the wrong direction and only discovering it three commits later.
From there each step in the pipeline — codegen, testgen, regression — has a defined scope and exit condition. It's not one agent trying to freestyle the whole thing. And if something fails at any stage, it surfaces there rather than quietly producing something broken that reaches a PR.
Where execution gets harder is with tasks that span multiple services or involve calls that need real domain judgment — those are the ones where we've deliberately kept humans in the loop rather than pushing for full automation. The goal isn't maximum autonomy, it's reliable execution on the work that can run cleanly, and a clear handoff when it can't.
What we're seeing with customers is that the volume of tasks completing end to end without constant intervention has gone up significantly. That's the real signal on execution for us.
Congrats on the launch team! Quick question though: how is this actually different from Cursor or Claude Code? Trying to figure out where Revolte fits in my stack.
Revolte
@keerthana07 totally fair question, and honestly the one we get most! 💪
Cursor and Claude Code are brilliant tools, but they're essentially a smarter pair of hands for the developer. You're still driving, still prompting, still context-switching between coding and everything else.
Revolte works at a different level. It's not just helping you code, it's handling the whole delivery cycle autonomously: planning, coding, test generation, security scanning, PR creation, all running as one connected workflow with no one babysitting each step.
The dev or tester comes in at the end, opens Revolte Code, reviews what the agents produced, makes any edits, and approves. That's the moment of human judgement, not the hours of groundwork before it.
So in your stack: Cursor and Claude Code assist the developer writing code. Revolte assists the entire delivery process, from ticket to merged PR, with specialised agent personas for development, automation testing, non-functional testing, bug fixing and more.
If Cursor is a smart co-pilot, Revolte is the autonomous crew. ❤️🔥