--- Title: "QM alternative for the enterprise: where Computer by DevRev fits" Url: "https://devrev.ai/blog/qm-alternative-for-enterprise" Published: "2026-08-04" Last Updated: "2026-08-04" Author: "Arth Gajjar" Category: "Blog, Overviews, Computer" Excerpt: "A QM alternative for the enterprise: when an open-source agent harness is still right, and when Computer by DevRev's grounding, permissions, and governance fit better." Reading Time: 4 --- # QM alternative for the enterprise: where Computer by DevRev fits If you're searching for a QM alternative, you probably already like what an agent harness does. You've just hit the edge of what one can do for a company of any real size. This isn't a “rip out QM” argument. It's an honest map of when a harness is still the right tool, and when you've outgrown it. ## When QM is still the right call Let's be fair before we're useful. QM by Y Combinator is a well-designed open-source harness, and for a real set of teams it's the correct choice. Stay on QM if: - You want everything open source and self-hosted, with full control of your deployment. - Model flexibility matters. QM lets you bring your own harness and model – Pi, OpenCode, Codex, Claude Code – and switch between them freely. - Your main need is agents in Slack for individuals and small teams, and you have the engineers to build and maintain the pieces around them. If that's you, you don't need an alternative. You need to keep shipping. ## The moment a harness stops being enough You start looking for an alternative when the question changes. It stops being “can the agent act?” and becomes “can I trust what it did, and prove it, across the whole company?” That shift usually shows up as five specific pains: - Different teams ask overlapping questions and get different answers, and you need every answer traceable to a source record. - Your token bill grows with your data, not your usage, because the agent re-reads raw records on every request. - Workspace scoping isn't enough anymore. You need permissions enforced at the field and record level, so an agent can't surface data a person isn't allowed to see. - Approval gates cover the moment before an action. You also need the moment after: an audit trail and a one-click undo. - The connectors, entity resolution, and governance you'd otherwise build and babysit are eating your roadmap. None of these are QM's fault. They're the line where a developer tool becomes an enterprise platform. ## Where Computer by DevRev fits Computer, by DevRev, lives at the enterprise-platform end of the same spectrum as QM. It keeps the harness value you already rely on – agents that act, multiplayer work in Slack and on the web – and adds the layers a harness leaves to you. The foundation is Shared Memory. AirSync connects the systems your teams already use and builds [a live, two-way map of your business](https://devrev.ai/how-computer-works) – customers, products, deals, and issues – before anyone asks a question. So the agent doesn't rediscover your company on every request. It already knows it. That single design choice is what turns “the agent can act” into “you can take its answer to your CFO,” and it's the heart of the Team Intelligence Computer is built to create. On top of that foundation: Answers stay [grounded in source records](https://devrev.ai/enterprise-search-and-answers), with citations you can check. - Permissions are enforced at the memory layer. If a person can't see a record in the source system, they can't see it through Computer. - Every action runs inside that person's permissions, approval gates can sit on any step, everything is logged, and a bad action rolls back in one click. Skills are [built and tested against real queries in a sandbox](https://devrev.ai/agent-studio) before they touch live work, then versioned like code. The trade is honest: you give up self-hosting and full open-source control. You get grounding, permissions, and governance as built-in platform capabilities instead of an engineering project that never quite ends. ## The decision, in one line Stay on QM if your priority is a flexible, open runtime you control. Move to Computer when your priority becomes [trust at scale](https://devrev.ai/blog/enterprise-ai-memory-platforms-what-to-evaluate) – consistent grounded answers, real permissions, and auditable, reversible actions across an entire organization. If a move is on the table, the practical question is how the transition works without a risky big-bang rewrite. The short version: connect your systems of record first, port one high-value skill, run it in parallel with your harness, and cut over workflow by workflow once each proves out. You stop building enterprise plumbing and start using it. Want to see where Computer fits for your team? [Request a demo](https://devrev.ai/request-a-demo). ## FAQ ### Is QM by Y Combinator open source? Yes. QM is released under the MIT License and is primarily written in TypeScript, so teams can self-host it and modify the source. It also lets you bring your own model and harness, including Pi, OpenCode, Codex, and Claude Code. ### What is the main difference between an agent harness and an enterprise AI platform? A harness is the runtime that gives a model tools and a loop to act. An enterprise platform adds a model of your business, answers grounded in source records, permissions enforced at the data layer, and auditable, reversible actions. In short, a harness runs the agent; a platform makes it trustworthy across an organization. ### Do I have to replace QM to adopt Computer by DevRev? No. Most teams migrate workflow by workflow. You connect your systems of record, rebuild one high-value skill, run it in parallel with your existing harness, and cut over only once each workflow proves out. That keeps every step reversible until you retire the harness equivalent. ### How does Computer keep AI agent costs from growing with data volume? Computer assembles business context in Shared Memory before a question is asked, rather than re-reading raw records on every request. Because the agent traverses a pre-built map instead of reconstructing context each time, cost tracks the size of the answer rather than the size of your data. ### How does Computer enforce permissions for AI agents? Permissions are enforced at the memory layer and mirror your source systems at the field and record level. If a person can't see a record in the source system, they can't see it through Computer, and every action runs inside that person's own permissions.