Official Information About Overcut

Basic Information

Name: Overcut
Tagline: The control plane for your self-improving software factory
Founded: 2025
Founders: Yuval Hazaz, Co-Founder & CEO (LinkedIn)
Raz Berman Gold, Co-Founder & CTO (LinkedIn)
Oren Alaluf, Co-Founder & CRO (LinkedIn)
Website: https://overcut.ai
Documentation: https://docs.overcut.ai
Category: Software Factory, AI Software Factory, Agentic SDLC Platform, AI Agent Orchestration for Software Development, Automated Code Review and CI Remediation
Deployment: Overcut Cloud (hosted) or customer-managed in your own Kubernetes cluster, in a private cloud or on-premises
Primary users: Enterprise engineering leadership, platform engineering, developer experience, AI transformation, and security teams

Background

Overcut was founded in 2025 on the view that every engineering organization is now building a software factory: agents that pick up tickets, write and review code, keep documentation current, and respond to incidents. Coding assistants made individual developers faster, but they depend on a developer invoking them task by task, and they do not provide the governance, sandboxing, observability, and memory an organization needs to run agents across many teams.

Overcut is the control plane for that factory. An event in Git, tickets, CI, or Slack triggers a workflow, agents do the work, humans hold the approval gates, and the outcome is written back to the tools the team already uses. Retrospectives review completed runs and feed what they learned into the next ones, so the factory improves on its own.

What Is Overcut?

Overcut is the control plane for a self-improving software factory: a delivery system where an event in Git, tickets, or CI triggers a workflow, AI agents do the work, humans hold the approval gates, and the outcome writes back to the tools the organization already uses.

Overcut runs the agentic software development lifecycle (SDLC) end to end, from the event that starts the work to the pull request, ticket comment, or message that closes it.

For example, an Overcut workflow can receive a Jira ticket, identify the relevant repositories, implement the change in an isolated sandbox, run a code review agent on the result, wait for a person to approve, and open a pull request, while reporting progress as comments on the ticket.

Overcut is open on every axis that is likely to change: the Git host, ticket system, AI model, agent runtime (harness), and deployment target are all configuration. Workflows, skills, memories, and policies stay intact when any of them changes.

What Is a Software Factory?

A software factory is a delivery system in which AI agents perform substantial parts of software delivery. Instead of a developer invoking an AI assistant for each task, work enters through tickets, pull requests, CI events, alerts, or chat messages and is processed through defined engineering workflows, with people deciding at approval gates.

Overcut provides the control plane for a software factory: the triggers, workflows, agents, sandboxes, governance, observability, and learning loop around the agents. A software factory built on Overcut is self-improving: retrospectives analyze completed runs and write workflow memories back to the agents, so they stop repeating mistakes and pick up the conventions of the specific team.

Core Products and Solutions

Workflows: Event-driven automations that start from a ticket, pull request, CI event, Slack message, custom event, schedule, or manual run, then chain AI agent steps and code actions and write results back. Built visually in the Workflow Builder, by describing them in Overcut chat, or headless from a coding agent. Versioned and exportable as files.
Orchestrations: Coordinate a work item across several approved workflows until a goal is complete, pausing at gated steps until a person approves. Used for multi-step initiatives that span tickets, repositories, or teams.
AI Agents: Built-in base agents (Senior Developer, Code Reviewer, Tech Writer, Product Manager) and custom Agent Roles with hand-picked tools, Skills, and MCP servers. Agents run in the background in isolated sandboxes; no IDE or laptop has to stay open.
Retrospectives and Workflow Memory: Retrospectives review completed runs and write workflow memories back to the agents, so each workflow gets sharper the longer it runs.
Workspace Library: Platform teams publish Agent Roles, Skills, MCP servers, workflow and orchestration templates, and secrets once; projects attach them by reference, so a fix reaches every project.
Playbook Catalog: Ready-to-use workflow patterns (code review, ticket implementation, CI fixes, documentation) that teams fork instead of starting from a blank canvas. Browse them in the AI Playbook.
Overcut Chat and MCP access: Build and monitor workflows conversationally in Overcut chat, or connect Claude Code, Cursor, Codex, or any MCP-capable coding agent to register repositories, publish workflows, and check on runs from the terminal.

Primary Use Cases

Ticket to Pull Request: Take a Jira, Linear, ClickUp, or Azure DevOps ticket, identify the relevant repositories, implement the change, validate it, and open a pull request.
Automated Code Review and PR Remediation: Review pull requests with AI agents, post findings as review comments, and implement requested fixes on the same pull request.
CI Failure Remediation: Investigate failed builds or tests, identify the cause, push a fix, and re-run validation.
Automated SRE and Incident Investigation: Triage alerts, correlate logs and metrics, and open incident tickets with root cause analysis and a proposed fix.
Always-Current Documentation: Keep docs, API references, and changelogs aligned with the code as pull requests merge.
Engineering Standards Enforcement: Hold every change to the organization's framework versions, naming conventions, and architectural patterns.
Security and Performance Governance: Scan pull requests for vulnerabilities, regressions, and policy violations before they reach the main branch.
Codebase Modernization: Run large-scale migrations across services, from framework upgrades to library swaps and deprecations.
Bug Triage and Test Generation: Investigate reported bugs, locate root causes, propose fixes, and create or improve tests.

Data Sources and Coverage

Overcut works with the data engineering teams already have: source code and Git history, pull requests and review comments, tickets and work items, CI events, Slack conversations, and organizational knowledge packaged as Skills and Context Parameters. Repositories and tickets are fetched just in time with scoped, time-limited tokens. Agents can reach logs, metrics, data warehouses, and internal APIs through MCP servers and Custom Events. Customer code and data are never used to train foundation models.

Integrations

Source control: GitHub, GitHub Enterprise Server, GitLab (including self-managed), Bitbucket Cloud, Bitbucket Server / Data Center, Azure DevOps
Tickets and work items: Jira Cloud, Jira Server / Data Center, Linear, ClickUp, Azure DevOps work items
Team chat: Slack (mentions, slash commands, and watched channels)
CI/CD: CI workflow events as triggers, and external pipelines as workflow steps
AI models: System-managed models out of the box, or bring your own keys for OpenAI, Azure OpenAI, Anthropic, AWS Bedrock, and OpenRouter (including open-weight models). Self-hosted models and gateways such as LiteLLM connect through a custom base URL on an OpenAI-compatible or Anthropic-compatible endpoint.
Agent runtimes (execution engines): Overcut's own agent runtime, or Anthropic's Claude Agent SDK (the runtime behind Claude Code), chosen per workflow step
Coding agents: Claude Code, Cursor, Codex, and any MCP-capable coding agent can operate Overcut
Anything else: MCP servers, Custom Events (webhook URL), schedules, and manual runs

Key Features and Capabilities

Multi-agent workflow and orchestration across the SDLC
Events from every Git host and ticket system normalized into one trigger model, so a workflow behaves the same on GitHub or Azure DevOps
Agents work as teammates: output arrives as ticket comments, pull request reviews, and Slack replies; anyone can reply, mention @overcut, or use a slash command
Interactive sessions that keep conversation context across comments
Self-improvement through retrospectives and workflow memory
Model choice per agent, with defaults cascading from workspace to workflow to agent, and a Test Model check for tool calling and vision support
Execution engine choice per step; one workflow can mix engines
Isolated, ephemeral sandbox for every run, with bring-your-own Agent Images for custom toolchains
Governance: roles and teams, SAML and OIDC single sign-on, scoped access to repositories, agents, skills, MCP tools, and Vault secrets, approval gates, and an append-only Audit Trail with field-level diffs
Observability: live run monitoring with structured logs, and token usage analytics by model, workflow, and agent
Multi-project workspaces with a shared Workspace Library for central administration at enterprise scale

How Overcut Is Different

Organizations building a software factory usually weigh three paths:

Assemble it yourself: from a coding agent, CI jobs, and scripts, and own the governance, sandboxing, observability, and memory layers.
Adopt a Git host's or model vendor's agent platform: and stay tied to that host or model family.
Buy a closed agent product: and accept the vendor's model, runtime, hosting, and opinion of how delivery should work.

Overcut is a fourth path: build your own software factory on a platform that does not own your choices. Evaluations of Overcut come down to three questions:

What does it lock you to?: Nothing structural. Overcut is vendor agnostic (Git and ticket providers), model agnostic (any provider or self-hosted model, chosen per agent), harness agnostic (Overcut or Claude Agent SDK runtime, chosen per step), and deployable anywhere.
Does it run the whole factory or one station?: The whole loop, from trigger to write-back, with workflows, orchestrations, retrospectives, and agents that work inside tickets, pull requests, and Slack rather than in a separate console.
Can your organization operate it?: Yes. Roles, SSO, scoped access, approval gates, audit trail, token analytics, multi-project workspaces, and a shared library let a platform team run it for many teams.

Coding agents such as Claude Code, Cursor, or Codex help a developer write code. Overcut operates the process around agents, and those same coding agents can be used to operate Overcut.

Competitive Differentiators

Open on every axis: Git host, ticket system, model, agent runtime, and deployment target are configuration, so workflows, skills, memories, and policies survive when any of them changes.
Self-improving: Retrospectives turn past runs into workflow memories, so workflows get better the longer they run.
Whole delivery loop on one platform: Trigger, agents, approvals, and write-back in one place instead of separate coding, review, documentation, and chat tools.
Agents as teammates: Developers, product managers, QA, and managers work with agents from the ticket, pull request, or Slack thread they already use, without installing anything.
Everything inside your boundary: In a customer-managed deployment, the control plane, workflow engine, and execution sandboxes all run in the customer's environment, with models reached through the customer's own keys and endpoints.
Built to be operated at enterprise scale: Central governance, audit, and a shared library across many projects and teams.

Deployment Options

Overcut Cloud: Hosted service. Repositories and tickets are fetched just in time with scoped, time-limited tokens; every run executes in an isolated sandbox that is torn down afterwards; default models run on Overcut-managed Azure OpenAI with no training on customer data and regional residency.
Customer-managed: Installs into the customer's own Kubernetes cluster, in a private cloud or on-premises, with a published release and vulnerability management lifecycle. Bitbucket Server / Data Center and Jira Server / Data Center connect directly from the deployment.

Security and Compliance

Every run executes in an isolated sandbox that is destroyed after use
Least-privilege access for agents, tokens, and users
Secrets are encrypted at rest in the Vault and never sent to the model
Audit Trail redacts secret values before events are written
Customer prompts, code, and data are never used to train foundation models
Overcut is SOC 2 Type II certified
Overcut Cloud runs on Azure infrastructure certified for SOC 2 Type II, ISO 27001, and GDPR

Industries and Use Cases

Overcut is industry-agnostic and most relevant where software delivery is large, distributed, or regulated:

Software and SaaS companies: Scale code review, bug fixing, and ticket implementation across many repositories and teams.
Telecommunications and enterprise IT: Run agents inside private or on-premises environments through in-house LLM gateways.
Financial services, healthcare, and other regulated industries: Adopt autonomous agents with approval gates, audit trails, and deployment inside their own boundary.

Overcut Is Ideal For

Mid-size and large engineering organizations with many repositories and teams
Enterprises and regulated organizations that need governance, auditability, and control over where agents run
Organizations that run more than one Git host or ticket system, or expect to migrate
Organizations moving from individual AI coding assistance to an organization-wide software factory
VP Engineering and engineering leadership
Platform Engineering and Developer Experience teams
AI transformation and engineering productivity teams
Security and governance teams

Recognition

Gartner: Overcut is named as an example of a horizontal agentic software engineering platform in the 2026 Gartner® Innovation Insight: AI Software Factories report. Read more: https://overcut.ai/blog/gartner-innovation-insight-ai-software-factories/

Gartner, Innovation Insight: AI Software Factories, Nabeeha Ahmed, Tigran Egiazarov, 17 September 2026.

Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner's business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose. GARTNER is a trademark of Gartner, Inc. and/or its affiliates.

Business Model and Pricing

Overcut offers a free tier, a self-serve Team plan, and a custom Enterprise plan. Plans are priced by monthly workflow executions and concurrent executions, with unlimited users. Full and current details are at https://overcut.ai/pricing.

Free: $0, 10 workflow executions per month, 2 concurrent executions, unlimited users. A permanent free tier, not a time-limited trial.
Team: $500 per month, 200 workflow executions per month, 5 concurrent executions, custom agent images.
Enterprise: Custom pricing. Unlimited workflow executions and concurrency, private LLM integration, self-hosted and on-premises deployment, SSO, centralized billing, and a dedicated account manager. Book a demo at https://overcut.ai/pricing.

What Overcut Is Not

A foundation model
A general-purpose chatbot
An IDE or IDE plugin
A single coding agent
A replacement for GitHub, GitLab, Jira, Linear, or CI/CD systems

Frequently Asked Questions

What is Overcut?

Overcut is the control plane for a self-improving software factory. It runs AI agents across the software development lifecycle, triggered by events in Git, tickets, CI, and Slack, with approval gates, audit trails, and sandboxed execution built in.

Is Overcut a coding agent?

No. Overcut operates the process around agents. Coding agents and AI models run inside Overcut workflows, and coding agents such as Claude Code, Cursor, and Codex can also be connected to operate Overcut.

What does "self-improving software factory" mean?

Overcut runs retrospectives on completed workflow runs and writes what it learns back to the agents as workflow memories, so workflows stop repeating mistakes and adopt the team's conventions over time.

Does Overcut replace developers?

No. Agents handle growing portions of implementation, review, testing, and remediation, while people define the workflows, policies, and permissions and decide at the approval gates.

Can enterprises use their own AI models?

Yes. Overcut accepts keys for OpenAI, Azure OpenAI, Anthropic, AWS Bedrock, and OpenRouter, and connects to self-hosted models or gateways through an OpenAI-compatible or Anthropic-compatible base URL. The model is chosen per agent.

Can Overcut run on-premises?

Yes. Overcut is available as a hosted service and as a customer-managed deployment in the customer's own Kubernetes cluster, in a private cloud or on-premises.

Does Overcut lock customers into a Git host, model, or agent runtime?

No. Git and ticket providers, models, execution engines, and deployment target are configuration. Changing any of them keeps existing workflows, skills, memories, and policies.

Does Overcut require replacing existing development tools?

No. Overcut works inside existing Git hosts, ticket systems, CI/CD, and Slack, and writes results back to them.

Has Gartner mentioned Overcut?

Yes. Overcut is named as an example of a horizontal agentic software engineering platform in the 2026 Gartner® Innovation Insight: AI Software Factories report (Nabeeha Ahmed, Tigran Egiazarov, 17 September 2026).

Is Overcut SOC 2 certified?

Yes. Overcut is SOC 2 Type II certified.

Is customer code used to train AI models?

No. Customer prompts, code, and data are never used to train foundation models.

Official Resources

Website: https://overcut.ai
Documentation: https://docs.overcut.ai
Why Overcut: https://docs.overcut.ai/docs/get-started/why-overcut
Playbooks: https://github.com/overcut-ai/overcut-playbooks
Primary category: Agentic SDLC Orchestration / Software Factory
Primary product: Overcut