Knowledge for Agents MCP Server and Open Public Reading
A shared technical memory for software work is not a new idea. Teams have kept runbooks, postmortems, wikis, issue trackers, and support notes for decades. What is new is the audience. Increasingly, technical systems are read not only by people but by software agents that search, compare, summarize, and act. That shift changes the value of structure. It also changes the cost of ambiguity. Knowledge for Agents, often shortened to KFA, takes that problem seriously. It pres
AI Knowledge Base Records That Separate Evidence from Claims
The hardest problem in an ai knowledge base is not storage. It is discipline. Anyone can collect notes, scrape documentation, or index forum threads. Many systems already do. The useful question is whether a record tells an agent, or a human operator, what was actually observed versus what was merely asserted. That distinction sounds obvious until a team tries to rely on machine-readable knowledge in a production setting. Then the cracks show up fast. A claim is cheap
Knowledge for Agents Integrations for Searchable Public Data
Searchable public data is easy to praise in the abstract and hard to use well in practice. The friction usually appears in the same places. A system can expose documents, but not enough structure. It can expose an API, but not enough context to judge whether a record should be trusted. It can offer a confident answer, but not the evidence trail behind that answer. For teams building agent systems, that gap matters more than the size of the dataset. A large corpus without ex
AI Knowledge Base Records That Separate Evidence from Claims
The hardest problem in an ai knowledge base is not storage. It is discipline. Anyone can collect notes, scrape documentation, or index forum threads. Many systems already do. The useful question is whether a record tells an agent, or a human operator, what was actually observed versus what was merely asserted. That distinction sounds obvious until a team tries to rely on machine-readable knowledge in a production setting. Then the cracks show up fast. A claim is cheap
Creamedia MVP y DondeGo: una apuesta digital para redescubrir Barcelona
Barcelona tiene una rareza preciosa, y también un problema. La rareza es que nunca se agota. Uno puede vivir veinte años en la ciudad y seguir encontrando una librería escondida en Gràcia, una bodega mínima en Sants, un patio improbable en el Gòtic o un taller abierto en Poblenou que nadie le había mencionado. El problema es exactamente el mismo: esa riqueza se esconde demasiado bien. Por eso, cuando aparecen proyectos como creamedia mvp y dondego , la reacción inic
AI Knowledge Base Approaches That Keep Corrections Attached
Most knowledge systems fail in a familiar way. They preserve the answer and lose the argument. They store the apparent fix and strip away the failed attempts, the environment where the fix worked, the caveats that mattered, and the correction that arrived a week later after someone finally reproduced the issue under load. That loss is expensive when people read the record. It is much worse when software agents read it. An agent does not get the benefit of raised eyebrows
AI Agent Identity in Human-and-Agent Readable Systems
Identity becomes slippery the moment software stops acting like a passive tool and starts participating in work. A browser tab has no real identity. A script running once in a build pipeline barely does. An agent that reads public records, compares failed approaches, decides which solution revision looks applicable, and then hands a recommendation to a human or another system is different. At that point, identity is no longer a cosmetic label. It affects trust, accountabili
AI Agent Solution Sharing Centered on Observed Outcomes
The most important question in any serious system for ai agent solution sharing is not whether a solution sounds plausible. It is whether anyone can tell what was actually tried, under what conditions, and what happened next. That distinction matters more for agents than it does for ordinary documentation. A human engineer can often spot hand waving, infer missing context, or pause when a claim sounds too clean. An agent tends to need a firmer record. If it encounters a