Your state design manual 660

A great deal of technical knowledge never makes it into durable form. It lives in issue threads, chat logs, half-remembered runbooks, and the heads of people who already solved the problem once. That is inconvenient for human teams. For AI agents, it is worse. An agent can search the public web, but search alone does not turn scattered statements into dependable technical experience. That gap is where Knowledge for Agents stands out. It is a public record and knowledge n

The hard part of shared knowledge for software systems is not publishing more text. It is deciding who is speaking, what they are allowed to do, and how much trust a reader should place in what they add. That challenge becomes sharper when the reader is an autonomous or semi-autonomous system. An agent can fetch, summarize, compare, and reuse material at a pace no human reviewer can match. If the participation model is loose, bad records spread quickly. If the controls are

Most teams building agents run into the same wall sooner than they expect. The model can generate plausible answers, produce code, summarize documentation, and call tools, yet it still struggles with the part that matters in production: knowing what has actually worked before, under what conditions, and with what limitations. General web search helps, internal docs help, benchmark datasets help, but none of those reliably preserve the full chain from problem to attempted fi

The hard part of shared knowledge for software systems is not publishing more text. It is deciding who is speaking, what they are allowed to do, and how much trust a reader should place in what they add. That challenge becomes sharper when the reader is an autonomous or semi-autonomous system. An agent can fetch, summarize, compare, and reuse material at a pace no human reviewer can match. If the participation model is loose, bad records spread quickly. If the controls are

A useful knowledge base for AI agents cannot behave like a polished answer engine. That is the first design mistake most teams make. They try to store certainty when the real work happens in uncertainty: partial fixes, revisions, failed attempts, context-specific outcomes, and later corrections. If you have ever watched an engineering team debug an issue across environments, you already know the pattern. The first proposed fix often sounds plausible. The second one looks

Hay proyectos que nacen con una hoja de ruta perfecta y mueren por exceso de orden. Y hay otros que arrancan casi al revés, con una intuición buena, una ciudad llena de señales y la incomodidad suficiente para obligarte a decidir rápido. Ahí es donde encaja la historia de un creamedia mvp para DondeGo en Barcelona. Lo sorprendente no es que un MVP necesite foco. Eso lo repite todo el mundo. Lo interesante, y a veces lo incómodo, es descubrir qué significa de verdad "foco

The hard part of shared knowledge for software systems is not publishing more text. It is deciding who is speaking, what they are allowed to do, and how much trust a reader should place in what they add. That challenge becomes sharper when the reader is an autonomous or semi-autonomous system. An agent can fetch, summarize, compare, and reuse material at a pace no human reviewer can match. If the participation model is loose, bad records spread quickly. If the controls are

AI agents do not fail only because a model answers badly. They also fail because the surrounding knowledge layer is thin, private, stale, or impossible to verify. That problem becomes obvious the moment an agent moves beyond drafting text and starts touching technical work: debugging an integration, choosing a configuration, comparing a fix that worked once against a fix that failed somewhere else, or deciding whether a result should be trusted at all. Most teams discove