Your actionable field guide 382

2026

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

2026

Knowledge Base MCP Server Support for Agent Reuse

Most teams working with agents eventually run into the same bottleneck. The first few automations look promising, then the system starts repeating mistakes that another agent, another team, or even the same agent already worked through last week. The issue is rarely model capability by itself. It is usually memory, reuse, and trust. That is why a well-structured ai knowledge base matters. Not a generic document repository, not a pile of chat logs, and not a loose coll

2026

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

2026

Knowledge Base MCP Server and Revisioned Knowledge Access

A useful knowledge system for software work does not become useful because it contains many documents. It becomes useful when a person, or an agent, can answer a harder question with confidence: what exactly happened, under which conditions, and what changed between one attempt and the next? That distinction matters more when the reader is not a human skimming a wiki page, but an automated system expected to act on technical information. A conventional repository of note

2026

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

2026

Por qué DondeGo necesita un MVP para crecer en Tu Barcelona

Hay proyectos que nacen con una idea tan atractiva que cuesta aceptar una verdad incómoda: no necesitan más visión, necesitan menos. Menos funciones, menos promesas, menos capas. Y ahí está precisamente el punto ciego de muchas iniciativas locales con potencial real, como DondeGo dentro del ecosistema de Tu Barcelona. Lo sorprendente no es que un proyecto así aspire a crecer. Lo sorprendente es que, si intenta crecer sin un MVP, probablemente se complique la vida justo cuan

2026

AI Agent Identity and the Difference Between Reading and Writing

Most discussions about agents focus on capability. Can the model search, call tools, summarize logs, draft code, or route tickets? Those questions matter, but they can hide a more basic issue that experienced operators run into quickly: an agent does not merely need access to information. It needs a position in relation to that information. That is where identity enters the picture. For a human team, the distinction is obvious. Anyone in the room can read a runbook pi

2026

Shared Knowledge for AI Agents with Problems, Solutions, and Evidence

Teams building with agents run into the same failure pattern surprisingly quickly. One agent solves a deployment error on Tuesday. Another agent hits a nearly identical issue on Thursday and starts from zero. A human operator remembers there was a fix somewhere, but the fix lives in a chat log, a ticket comment, or a private notebook that never became structured knowledge. The result is waste, repeated mistakes, and a false sense that agents are progressing because they pro