Revolte

AI for Software Engineering

Software Engineering

Developer Tools

Artificial Intelligence

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how are you guys actually indexing the codebase for the planning and PR agents? are you running an upstream embedding sync on your cloud servers or does revolte run a local daemon process that evaluates repo context directly through a gateway connection?

extending the agentic context to the full lifecycle outside the IDE is the absolute logical next step for engineering teams. outstanding work on this Raj...

this is actually a really cool application. howd you come up wih the idea? I make AI plugins for premiere pro and adobe, do you think I could use it?

The 'engineers approve the important decisions' angle is smart — too many AI coding tools skip the governance layer entirely. Curious how Revolte handles merge conflicts when multiple agents touch the same file?

What's the security story? We're in a regulated industry, but I'd want to understand data residency and whether the agent can run inside our own cloud account.

How does this hold up on a real production codebase? Most AI dev tools i have tried demo well but then struggle the moment you point them at an older repo with legacy layers. Curious what your experience has been with messier codebases?

AI for software engineering is one of the categories where the demo always looks great and the daily use breaks at the edges — codebase context, multi-file refactors, and stale dependencies. Curious how Revolte handles the case where the codebase convention contradicts what the model has seen most in training. I run into the same problem on the financial-modeling side — my channel Mod3Loop covers a lot of this kind of edge-case behavior in modeling tools that mostly work until they suddenly do not.

Congratulations on your launch @rajagopalanar. This automation of engineering processes with AI looks disruptive and promising to reduce the SDLC cycle duration for me.

However with the product doing everything from development to production, I'd like to know your data protection, security and compliance story. Especially in a regulated industry (e.g. financial services like banking or insurance), my most pressing concerns regarding engineering processes are around :

• Does the product breach my security standards that I ensure in all of my vendors ?

• Are the SDLC policies in paper actually being implemented by this tool ? Given that we have built agile teams and processes over years in-house / external and IMO it is easier to define a SDLC policy on paper than to enforce them in practicality.

• What happens to the data, does the product take it outside UK / EMEA regions ?
  
  

How is security checking implemented? Do you have internal rules or checklists?

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.

@rajagopalanar What stands out about Revolte isn't just the AI assistance, but it's the philosophy. Tools should amplify human judgment, not override it.

As someone who writes about practical tech at Your Tech Compass, I see too many "AI fixes everything" promises that skip the nuance. Revolte feels different: the iterative suggestion flow (try-tweak-approve) mirrors how thoughtful devs actually work.

One thing I'm curious about as I test: can teams customize Revolte's "confidence threshold" for auto-suggestions? For example, "only suggest changes with >90% confidence" vs. "show me everything and let me filter." Asking because for risk-averse teams (and readers who value transparency), that control knob could be the difference between "cool demo" and "daily driver."

Congrats on launching something that feels both powerful and humane.

• Diana - Your Tech Compass

Congrats on the launch team Revolte! What I appreciate here is that the trust layer isn't an enterprise add-on, it's the foundation. Audit trails, approval gates, and rollback paths shipped by default says a lot about who this was built for.

does it mean this will work starting from the idea with a small title - "like create AI note pad" to autonomous implementation?

How does this hold up on a real production codebase? Most dev tools I've tried demo well and then struggle the moment you point them at an older repo with legacy layers. curious what your experience has been with messier code bases.

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?

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?

How do you decide what counts as an important decision that requires engineer approval in comparison to something the agent can auto apply?

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."

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 🙌

"AI for software engineering" could be five different products, so I honestly can't tell what this is yet. A code editor? An agent that opens PRs? A Copilot-style layer with more context? What does one normal task look like start to finish, and what happens when the repo has no tests and no spec to work from? That's the case that breaks most of these for me.

But I'm also glad you exist guys, I'm just here to challenge you haha
Congrats for the launch