Today, I’m thrilled to finally share something we’ve been building quietly for months: Overcut. This isn’t just another AI tool. It’s the result of years of seeing how engineering teams struggle to bring AI into their workflows in a way that’s consistent, reliable, and scalable. When I looked at how teams were experimenting with AI - one developer using Copilot, another pasting into ChatGPT, someone else testing Cursor - it felt messy. Powerful, yes, but messy. At the organizational level, it often looked like chaos: no consistency, no standards, no repeatability. It reminded me of the days before CI/CD. Back then, every team deployed differently, with their own scripts and rituals. Deployments were fragile and nerve-wracking. CI/CD changed all of that by creating a standardized, organization-led system that worked for everyone. That’s the idea behind Overcut: to bring the same kind of predictable, scalable automation to AI in engineering.

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Your Virtual Engineering Team

Imagine a virtual team that never sleeps. Agents that work 24/7, picking up repetitive, organizational tasks so your developers can focus on building. They don’t replace your team - they extend it. And just like your real team, they operate with clear scopes, responsibilities, and guardrails. That’s what Overcut gives you: autonomous agents that work inside the boundaries you set, always aligned with your organizational standards. The best part? Developers don’t need to change a thing. They can keep using GitHub, GitLab, Azure DevOps, or Bitbucket for code, Jira for tickets, and their favorite assistants like Copilot, Cursor, or ChatGPT. Overcut runs in the background, orchestrating workflows across all these tools.

Overcut running a code review on a pull request in GitHub

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Let Me Make It Concrete

  • A pull request is opened in GitHub. Overcut’s Review Agent steps in automatically, scanning for code quality, architecture, and security issues - and adds comments right in the PR.
  • A developer forgets to update the changelog before merging. Overcut spots it, opens a Jira ticket, and even drafts the changelog entry.
  • A bug report arrives. Overcut enriches it with missing details, applies labels, and routes it to the right team.
  • You have 50 services and need all of them to follow the same Kafka setup. Overcut enforces the standard automatically and flags any drift before it spreads.
Each of these tasks sounds small on its own. But multiplied across dozens of repos, services, and teams, they become huge time sinks - and sources of inconsistency. Overcut takes that burden away.

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The Vision

The vision is simple: just as CI/CD pipelines standardized software delivery, Overcut standardizes AI automation in the SDLC. It’s not about replacing developers with AI. It’s about giving your organization a virtual workforce that ensures things are done right, every time - while your people focus on the problems that matter most. This is only the beginning. In the coming weeks, I’ll share more about Overcut’s architecture, security model, and the ways teams are already using it in production.

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See it in action

In this video, you can see Overcut in action: from a new ticket to triage, design, pull request creation, and code review - all automated with AI. A real recording of agentic workflows running inside GitHub (and beyond).”

Overcut in action

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Why Overcut is Different

Overcut isn’t “just another AI assistant.” It’s built to run at the organizational level - giving you automation that’s controlled, customizable, and deeply integrated into your existing workflows. Here’s what makes it unique:

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💬 Interactive Conversations in Git & Tickets

Overcut doesn’t pull you into a new UI. You interact with agents directly in your GitHub/GitLab/Azure DevOps/Bitbucket PRs or inside Jira tickets. It feels like chatting with a teammate, right where you already work.

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🔗 Built-In Linking Between Tickets & Repos

Overcut automatically maps tickets across multiple projects and repositories. It knows that “this Jira ticket” belongs to “that repo and branch,” so workflows can run seamlessly across your entire stack.

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🛠 Fully Customizable Workflows with Prebuilt Playbooks

No black box. You can pick from prebuilt playbooks (like PR Review, RCA, Changelog Enforcement) or create your own workflows. Each can evolve continuously - so your automation improves alongside your team.

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🛡️ Agents Run in Sandboxed Custom Environments

Every agent runs in a dedicated container image - isolated, sandboxed, and preloaded with your CLIs, SDKs, and tools. This ensures agents can act as if they’re on your team’s machine, without touching production directly.

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🎯 Custom Agents for Specialized Tasks

Create and train your own agents for repeatable tasks - like architecture reviews, compliance checks, or documentation updates. Overcut gives you the flexibility to extend beyond out-of-the-box use cases.

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⚡ Automatic Workflow Triggers

Define the rules (PR opened, label applied, ticket created, etc.), and Overcut takes care of the rest. Workflows are triggered automatically, so nothing slips through the cracks.

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🔒 Enterprise-Ready Security

Every agent run is isolated, data is processed in secure environments, and we support Azure OpenAI for teams that need stronger privacy guarantees.

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🌐 Works with Your Entire SDLC

GitHub, GitLab, Bitbucket, Azure DevOps, Jira - Overcut is built to integrate directly, so you don’t need to change your tooling.

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Get Started

This is only the beginning, and we’d love for you to be part of it. 👉 Sign up today and give Overcut a try. Start with prebuilt workflows, experiment with your own, and see how much time and friction your team can save. Your virtual team is ready.