This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Agent Object Model (AOM) v0.1.0 is an open standard for AI agents that replaces brittle HTML scraping and guesswork with task-centric, machine-readable surfaces defining what agents can see, do, and safely automate. AOM includes JSON Schemas for surfaces, outputs, and site policies, plus validators and CLIs for developers.
Why: the AI agent angle Today most AI agents see the web through brittle hacks: HTML scraping, ad‑hoc CSS selectors, or screenshots glued together with prompts. A tiny DOM change, an extra banner, or an A/B test can quietly break an entire workflow, and nobody knows until a user hits a bad run.
Every agent framework also invents its own JSON or prompt format for describing tasks, inputs, and outputs. That means agents built on one stack cannot easily interoperate with tools, hosts, or plugins from another. The result is a fragile ecosystem of one‑off integrations instead of a shared language.
Because there’s no standard way to describe surfaces, inputs, outputs, and policies, agents can’t reliably answer basic questions: “What is safe to click?”, “Which actions are allowed here?”, “What is the task‑centric view of this surface?” This is the reliability and safety gap AOM is trying to close.
Why: the website / surface owner angle On the other side, websites are seeing a surge of AI traffic from agents, crawlers, and bots that scrape content without clear permission or constraints. Many major sites have started blocking AI crawlers outright because they lack fine‑grained control over what agents can see, cache, or reuse.
Surface owners are stuck doing custom work for every agent or platform: one JSON shape for integration A, another for B, plus brittle scripts for C. This is expensive, hard to monitor, and breaks whenever the UI evolves. There’s still no standard way to publish “this is the task‑centric view for agents” or to encode “your site, your rules” in a machine‑readable form.
Without that standard, websites either over‑block (no agents allowed) or over‑expose (agents scrape everything and guess the rules). AOM’s goal is to give surface owners a clear way to define how agents should see their site and what they are allowed to do, while giving agents a stable, interoperable view they can trust.
Thank you, everyone, for the feedback — even the silence. I realize I haven’t explained this well enough, and that’s on me. Maybe the idea was too easy to dismiss because it sounds simple at first, but it’s actually meant to be a foundational change: like robots.txt gave websites control over crawlers, AOM is meant to give sites control over what AI agents can and cannot do.
About Agent Object Model - The Spec on Product Hunt
“Open spec for surfaces and AI agents”
Agent Object Model - The Spec was submitted on Product Hunt and earned 0 upvotes and 2 comments, placing #130 on the daily leaderboard. Agent Object Model (AOM) v0.1.0 is an open standard for AI agents that replaces brittle HTML scraping and guesswork with task-centric, machine-readable surfaces defining what agents can see, do, and safely automate. AOM includes JSON Schemas for surfaces, outputs, and site policies, plus validators and CLIs for developers.
Agent Object Model - The Spec was featured in Open Source (68.3k followers), Artificial Intelligence (466.4k followers) and GitHub (41.2k followers) on Product Hunt. Together, these topics include over 117.7k products, making this a competitive space to launch in.
Who hunted Agent Object Model - The Spec ?
Agent Object Model - The Spec was hunted by Knight Pulse. 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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Knight Pulse
Why: the AI agent angle
Today most AI agents see the web through brittle hacks: HTML scraping, ad‑hoc CSS selectors, or screenshots glued together with prompts. A tiny DOM change, an extra banner, or an A/B test can quietly break an entire workflow, and nobody knows until a user hits a bad run.
Every agent framework also invents its own JSON or prompt format for describing tasks, inputs, and outputs. That means agents built on one stack cannot easily interoperate with tools, hosts, or plugins from another. The result is a fragile ecosystem of one‑off integrations instead of a shared language.
Because there’s no standard way to describe surfaces, inputs, outputs, and policies, agents can’t reliably answer basic questions: “What is safe to click?”, “Which actions are allowed here?”, “What is the task‑centric view of this surface?” This is the reliability and safety gap AOM is trying to close.
Why: the website / surface owner angle
On the other side, websites are seeing a surge of AI traffic from agents, crawlers, and bots that scrape content without clear permission or constraints. Many major sites have started blocking AI crawlers outright because they lack fine‑grained control over what agents can see, cache, or reuse.
Surface owners are stuck doing custom work for every agent or platform: one JSON shape for integration A, another for B, plus brittle scripts for C. This is expensive, hard to monitor, and breaks whenever the UI evolves. There’s still no standard way to publish “this is the task‑centric view for agents” or to encode “your site, your rules” in a machine‑readable form.
Without that standard, websites either over‑block (no agents allowed) or over‑expose (agents scrape everything and guess the rules). AOM’s goal is to give surface owners a clear way to define how agents should see their site and what they are allowed to do, while giving agents a stable, interoperable view they can trust.