Decawork is how IT teams take employee-built AI agents live and control every one of them from one place. An employee builds an agent on Claude Code, Codex, or any vibecoding tool; we take it in, put it on company accounts, and run it as a company asset. From there, IT team manages the agent like an employee: access, oversight, retirement.
AI coding tools made it easy for anyone to build an internal agent. The hard part starts when a teammate wants to use it. Now the agent needs company credentials, access to real systems, an owner, approvals, logs, maintenance, and a way for IT to stop it.
Decawork handles that handoff. Your team still owns the job. Bring us the repo from Claude Code, Codex, Cursor, or whatever your team used. Decawork operates the agent with a company identity. IT decides what it can access, reviews sensitive actions, sees what it did, and can pause or retire it from one place.
We think internal AI is unlocked by IT, not by one more agent builder. IT should not have to choose between blocking every experiment and rebuilding each one by hand.
We've spent years on both sides of this handoff. I built AI systems at NVIDIA that shipped to OpenAI and Meta, then enterprise agents for Microsoft and Hitachi. Aman (my co-founder) built Barclays' AI compliance platform, led a consumer AI product to 100K+ monthly users, and founded a fintech serving 52K families.
We'd love feedback from people building internal agents, especially the IT and security teams asked to let them touch company systems. What was the first thing that broke when your agent moved beyond its original builder?
the retirement concept is the sleeper feature here. everyone's building agents right now, nobody's thinking about what happens when the person who built it leaves or the use case changes. having a kill switch with audit trail is going to be table stakes for any company running more than a handful of internal agents.
This is a real problem I’ve been seeing with startups - someone on the team builds agents that run on personal keys, with no audit trail and everything is all over the place with no central identity or control.
Super interesting! Philosophically in alignment with you about having an AI stack that isn't locked into one provider.
One question, since often security is part technical and part behavioral: what steps of behavioral change do employees need to take for this to work?
Interesting Product. Can I handle secrets management and token issue lifecycle as well for my internal AI agents?
Congrats on YC Sarthak.. great launch.. good wishes for its success.. I remember working on AIM system which has similar need during RPA deployments.. great to learn about all your other startups too.. good luck..
Centralizing control and access permissions for internal AI agents makes complete sense as teams scale. Congrats on the launch!
The interesting question foe me is what happens when an amployees leaves the company . If their agents can be transferred and managed like company assets, that could be a huge operational win.
Access control is probably going one of the biggest challenges of the agent era. Having everything managed from one place a lot of sense.
Connecting AI spend directly to roadmap items makes the numbers much easier to understand. I like that approach.
I can see this being useful once a company has dozens of small agents floating around. Do you have a way to quickly see which agents are active, who owns them, and what they can access?
Security plus visibility is probably where internal agents will need the most help. Curious how you balance storng controls with developer flexibility.
Can admins set different permissions for different agents based on their role or the type of data they can access?
I’d be interested in seeing audit logs for agent activity, especially for teams handling customer or financial data.
Moving agents from personal accounts into company-owned accounts sounds like an important step. How much of that setup is automated?
I like the employee-style lifecycle for agents. Having a clear way to retire old agents could prevent a lot of security headaches.
The idea of putting employee-built agents under IT control feels especially useful as more teams start making their own tools. 👀
Treating internal AI agents like company assets makes a lot of sense. I like the focus on access and retirement instead of just deployment.
A friend of mine ran into an issue at his company where they couldn't connect tools to their internal agents, do y'all take care of those as well?
About Decawork on Product Hunt
“Control your company's internal AI agents and tools”
Decawork launched on Product Hunt on August 24th, 2026 and earned 332 upvotes and 31 comments, earning #2 Product of the Day. Decawork is how IT teams take employee-built AI agents live and control every one of them from one place. An employee builds an agent on Claude Code, Codex, or any vibecoding tool; we take it in, put it on company accounts, and run it as a company asset. From there, IT team manages the agent like an employee: access, oversight, retirement.
Decawork was featured in Software Engineering (42.9k followers), Artificial Intelligence (477.3k followers) and Security (2.9k followers) on Product Hunt. Together, these topics include over 130.6k products, making this a competitive space to launch in.
Who hunted Decawork?
Decawork was hunted by Garry Tan. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Garry Tan
Hey Product Hunt, I'm Sarthak - co-founder of Decawork (YC S26).
AI coding tools made it easy for anyone to build an internal agent. The hard part starts when a teammate wants to use it. Now the agent needs company credentials, access to real systems, an owner, approvals, logs, maintenance, and a way for IT to stop it.
Decawork handles that handoff. Your team still owns the job. Bring us the repo from Claude Code, Codex, Cursor, or whatever your team used. Decawork operates the agent with a company identity. IT decides what it can access, reviews sensitive actions, sees what it did, and can pause or retire it from one place.
We think internal AI is unlocked by IT, not by one more agent builder. IT should not have to choose between blocking every experiment and rebuilding each one by hand.
We've spent years on both sides of this handoff. I built AI systems at NVIDIA that shipped to OpenAI and Meta, then enterprise agents for Microsoft and Hitachi. Aman (my co-founder) built Barclays' AI compliance platform, led a consumer AI product to 100K+ monthly users, and founded a fintech serving 52K families.
We'd love feedback from people building internal agents, especially the IT and security teams asked to let them touch company systems. What was the first thing that broke when your agent moved beyond its original builder?
Find a time at decawork.ai or reach out at [email protected].