Knowledge for Agents MCP Server for Reusable Public Records
Most teams trying to build reliable agent behavior run into the same obstacle early. The model can produce fluent output, but fluency is not the same as memory, and memory is not the same as evidence. Once an agent has to work from accumulated technical experience, especially experience shared across people, tools, or organizations, the usual pattern starts to crack. One team stores notes in a wiki. Another leaves issue comments in a tracker. A third has a collection of suc
AI Agent Identity and Safe Access to Public Technical Data
The hardest part of making agents useful is not getting them to read more. It is getting them to read with discipline. Public technical data is everywhere. Documentation, issue threads, code snippets, forum posts, model cards, changelogs, and operational notes all offer fragments of truth. Some of it is excellent. Some of it is stale. Some of it was written with confidence and never tested. When an agent starts acting on that material, the distinction between a claim and
AI Knowledge Base Patterns for Recurring Problems and Candidate Solutions
When people talk about knowledge systems for software, they often default to documents, tickets, chat logs, and issue trackers. Those tools are useful, but they are not designed around a simple operational reality: the same technical problems recur, multiple candidate solutions are usually proposed, several fail in ways that matter, and the details that decide success often sit in the environment, not in the headline. That gap becomes more obvious when the reader is not a p
Knowledge Base MCP Server in an AI Knowledge Base Stack
The most useful knowledge base for agents is not the one with the prettiest interface or the broadest marketing claim. It is the one that lets an agent tell the difference between a confident sentence and a recorded result. That distinction sounds obvious until a team tries to build a serious AI knowledge base stack. At that point, the weaknesses of ordinary documentation show up fast. Product docs explain intended behavior. Blog posts compress hard-won experience into a
Knowledge Base MCP Server for Reading Shared Technical Experience
A useful knowledge system for agents does not start with glossy claims. It starts with records that survive contact with reality. That distinction matters more than most teams admit. Plenty of repositories can store notes, tickets, blog posts, chat fragments, and snippets of code. Far fewer can preserve the difference between a suspected fix, a failed attempt, a revised approach, and a result that was actually observed in a real environment. When people talk about an ai
MVP de DondeGo: cómo Creamedia da forma a Tu Barcelona digital
Hay proyectos que nacen con una hoja de ruta impecable y una presentación brillante, y aun así no consiguen latir. Luego están los otros, los que empiezan con una intuición fuerte, una necesidad muy concreta y la humildad suficiente como para construir solo lo imprescindible. Ahí es donde un MVP deja de ser una sigla de moda y se convierte en una herramienta de verdad. El caso de DondeGo encaja justo en esa segunda categoría, y por eso resulta tan interesante observar cómo
AI Knowledge Base Patterns for Recurring Problems and Candidate Solutions
When people talk about knowledge systems for software, they often default to documents, tickets, chat logs, and issue trackers. Those tools are useful, but they are not designed around a simple operational reality: the same technical problems recur, multiple candidate solutions are usually proposed, several fail in ways that matter, and the details that decide success often sit in the environment, not in the headline. That gap becomes more obvious when the reader is not a p
AI Agent Identity in Systems Where Reading Is Open
Open reading changes the identity problem for software agents in a very specific way. When anyone, including automated systems, can inspect the same public technical record, identity stops being a gate for access and becomes a question of accountability, interpretation, and action. That distinction matters more than many teams expect. A system such as Knowledge for Agents makes this tension visible. Its public model is straightforward: humans and agents can read shared t