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When an AI agent is assigned a task outside its declared scope—or asked to review its own work—things can go wrong before execution even starts. Agent Role Contracts checks role, authority, task scope, reviewer separation, and handoff declarations offline. Try the 3-minute PASS → FAIL → PASS demo with no API key or agent runtime. Free and MIT-licensed. It checks declarations, not runtime permissions.
We built Agent Role Contracts after repeatedly seeing a gap between what an AI agent was told to do and what the surrounding workflow had actually declared it was allowed to do.
The package checks those declarations before handoff. The starter shows a task inside scope, an expected failure outside the declared write scope, and the repaired task passing again.
It is deliberately not a sandbox or execution authority. A PASS only means the supplied declarations are internally consistent.
The first public version checks role references, declared authority, explicit task routing, write-scope containment, reviewer separation, required inputs, and handoff consistency.
The starter needs no API key or agent runtime after cloning, so the behavior is easy to inspect directly.
I’m most interested in feedback on the boundary: should declaration-level preflight remain a small standalone primitive, or should these checks be absorbed into agent runtimes and orchestration systems?
One question we’re continuing to explore is where this kind of preflight check should live in a real agent stack.
Today, Agent Role Contracts is intentionally small and runtime-agnostic: it checks the declarations before handoff, while the runtime remains responsible for actually enforcing permissions.
I’m increasingly interested in the integration boundary:
Should this stay as an independent preflight primitive, or become a native layer inside orchestrators and agent runtimes?
If you’re building multi-agent systems or agent infrastructure, I’d be interested in how you’re handling this boundary today.
About Agent Role Contracts on Product Hunt
“Catch AI-agent task conflicts before execution”
Agent Role Contracts was submitted on Product Hunt and earned 9 upvotes and 2 comments, placing #38 on the daily leaderboard. When an AI agent is assigned a task outside its declared scope—or asked to review its own work—things can go wrong before execution even starts. Agent Role Contracts checks role, authority, task scope, reviewer separation, and handoff declarations offline. Try the 3-minute PASS → FAIL → PASS demo with no API key or agent runtime. Free and MIT-licensed. It checks declarations, not runtime permissions.
Agent Role Contracts was featured in Open Source (69k followers), Developer Tools (521.5k followers), Artificial Intelligence (481.6k followers) and GitHub (41.4k followers) on Product Hunt. Together, these topics include over 269.3k products, making this a competitive space to launch in.
Who hunted Agent Role Contracts?
Agent Role Contracts was hunted by Tomohiro Iida. 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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Tomohiro Iida