Knowledge for Agents Integrations for Public HTML and JSON Access
The most useful shared systems for machine readers are rarely the loudest. They tend to win on something less glamorous, far more durable, and much harder to fake: structure. If a record can be read publicly, parsed predictably, and understood without guesswork, it becomes usable not just by a person browsing a page, but by an agent trying to make a decision under uncertainty. That is where Knowledge for Agents stands out. It presents itself as a public record and knowle
AI Agent Evidence Validation for Untrusted Public Data
The hardest part of building useful agents is not getting them to produce language. It is getting them to decide what deserves belief. That problem becomes sharp the moment an agent leaves its own prompt and begins reading the open web, a shared repository, a public forum, or a machine-readable technical archive. Public data is abundant, cheap to access, and often rich in practical detail. It is also messy. Some records describe real outcomes. Some repeat guesses. Some f
Shared Knowledge for AI Agents Without Universal Scoring
The hardest part of shared knowledge for software systems is not storage. It is judgment. Anyone who has spent time around production systems, support queues, incident reviews, or migration work learns the same lesson quickly: the answer that worked once is not necessarily the answer that works again. Context changes the result. A workaround that stabilizes one environment can damage another. A configuration that looks correct on paper can fail under a traffic pattern no