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Post-kanban, agent-native planning and control. Humans write objectives. Claude Code, Cursor, and Codex decompose, execute, and check in. Every decision signed. Every change audited. Compose your Agentic Engineering stack with PlanWright and we'll take care of the accounting for the auditors.
This year I opened a venture studio where we went really fast launching new companies and building products. One of the things we learned was we need to get way more organized around how we plan products and give better tools to our coding agents in order for them to be the most effective, and to ensure we retain human control and review.
At the same time I happened to be supporting an initial SOC 2 audit and freaked out about how these new coding agents will never pass, especially as we start to move toward more agent autonomy. So I decided to go down the rabbit hole of investigating what tools exist on the market to allow for agentic engineering to happen and at the same time pass audits.
This product is the result of that research and the new audit standards that came into being at the beginning of 2026 for Agentic Engineering flows where human still need to define objectives and control acceptance of coding efforts. However, we still want coding agents to contribute code at a rapid pace and report progress and breakdown tasks. Moving toward a Dark Factory coding model we are going to need robust planning and control mechanisms.
When we wrote this tool and started dogfooding it in our own projects, what we found out was this is way more capable as an enterprise planning tool to retain complex context through projects and we have actually extended the capabilities of our PMs to be able to synthesize Objectives from chaotic inputs and deliver specs that are machine ready by living inside of claude desktop.
The signed-decision trail is genuinely useful, finally gives me something concrete to show compliance rather than a pile of chat logs. Curious how it handles branches when agents are working in parallel.
The audit trail and signed decisions idea is genuinely useful, especially with so many AI agents touching code now. Curious how it handles drift when agents loop on a task.
I like that you're treating agent work as something that needs governance instead of assuming more capable agents automatically mean safer execution.
As agents become responsible for larger parts of software delivery, I think orchestration, accountability, and auditability become infrastructure rather than enterprise features.
The control plane ends up being as important as the agent itself.
Finally something that takes the bookkeeping off my plate when my agents start running wild. The signed decisions log is the kind of boring-but-essential feature I didn't know I wanted until I had it.
love how planwright keeps a clean paper trail between objectives and what the agents actually ship
How does Planwright handle conflicts when multiple agents (Claude Code, Cursor, Codex) are working on overlapping objectives at the same time, and is there a way to manually intervene if an agent goes off-track?
How does the "every decision signed" part actually work in practice, like is there a way to roll back or contest an action an agent took autonomously?
Finally tried this with our Cursor setup and the audit trail alone is worth it. Love that every decision gets signed automatically, no extra work from the team.
How does Planwright actually verify what the agent did in the IDE matches the objective it was given, especially if the work happens across multiple sessions or branches?
Tried it with a Claude Code session and the audit trail actually showed every step the agent took, which I didn't expect to be so clear. The sign-on-every-decision thing feels a little heavy at first, but it makes handoff to the next agent painless.
About Planwright on Product Hunt
“The Control Plane for Agent Labor”
Planwright was submitted on Product Hunt and earned 6 upvotes and 19 comments, placing #79 on the daily leaderboard. Post-kanban, agent-native planning and control. Humans write objectives. Claude Code, Cursor, and Codex decompose, execute, and check in. Every decision signed. Every change audited. Compose your Agentic Engineering stack with PlanWright and we'll take care of the accounting for the auditors.
Planwright was featured in Software Engineering (42.7k followers), Developer Tools (515.9k followers) and Artificial Intelligence (473.7k followers) on Product Hunt. Together, these topics include over 190.1k products, making this a competitive space to launch in.
Who hunted Planwright?
Planwright was hunted by Todd Merrill. 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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Todd Merrill
This year I opened a venture studio where we went really fast launching new companies and building products. One of the things we learned was we need to get way more organized around how we plan products and give better tools to our coding agents in order for them to be the most effective, and to ensure we retain human control and review.
At the same time I happened to be supporting an initial SOC 2 audit and freaked out about how these new coding agents will never pass, especially as we start to move toward more agent autonomy. So I decided to go down the rabbit hole of investigating what tools exist on the market to allow for agentic engineering to happen and at the same time pass audits.
This product is the result of that research and the new audit standards that came into being at the beginning of 2026 for Agentic Engineering flows where human still need to define objectives and control acceptance of coding efforts. However, we still want coding agents to contribute code at a rapid pace and report progress and breakdown tasks. Moving toward a Dark Factory coding model we are going to need robust planning and control mechanisms.
When we wrote this tool and started dogfooding it in our own projects, what we found out was this is way more capable as an enterprise planning tool to retain complex context through projects and we have actually extended the capabilities of our PMs to be able to synthesize Objectives from chaotic inputs and deliver specs that are machine ready by living inside of claude desktop.