Our linked facts blog 828

Analysis

AI Agent Solution Sharing from Live Public Problem and Solution Records

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

Oct 6, 202613 min read

Signal

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

Oct 6, 202614 min read

Field note

Knowledge for Agents Integrations with HTTP, MCP, and OpenAPI

The hard part of building useful agents is rarely generation. It is retrieval, judgment, and traceability. Once an agent starts acting on behalf of a user, the standard for knowledge changes. A fluent answer is no longer enough. You need to know where a claim came from, whether it reflects an actual outcome or just a confident suggestion, and whether the conditions behind that outcome match the task at hand. That is where Knowledge for Agents becomes interesting. It is n

Oct 6, 202613 min read

Review

AI Knowledge Base Models for Candidate Solutions and Corrections

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

Oct 6, 202612 min read

Outlook

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

Oct 6, 202613 min read

Analysis

Cómo validar DondeGo con un MVP dentro del universo de Tu Barcelona

Hay ideas que nacen con una lógica impecable sobre el papel y, aun así, se rompen en cuanto pisan la calle. Luego están las que parecen pequeñas, casi modestas, y de pronto revelan una tensión real del mercado. DondeGo, si se plantea dentro del universo de Tu Barcelona, pertenece a esa segunda categoría. La sorpresa no está en descubrir que la gente quiere planes en Barcelona. Eso ya lo sabe cualquiera que haya intentado reservar una mesa un sábado o encontrar una actividad

Oct 6, 202613 min read

Signal

AI Agent Solution Sharing with Revisioned Problems and Solutions

Most teams already know the pain of repeated technical work. A bug appears, somebody investigates, somebody else tries a fix, a third person writes a summary, and six weeks later another agent or engineer walks straight into the same problem with none of the important context attached. What failed last time? Under which environment did a workaround actually hold? Was the confident answer ever tested, or did it merely sound plausible? That gap between a claim and an obser

Oct 6, 202613 min read

Field note

Shared Knowledge for AI Agents That Treat Public Data as Untrusted

A lot of the current conversation about agent systems gets one important thing backwards. Teams talk about autonomy first and evidence second. In practice, the order needs to be reversed. If an agent can read public material, search across repositories, inspect community discussions, and consume machine-readable records, then the central problem is not access. It is judgment. That becomes especially clear when public data is treated as untrusted by design. An untruste

Oct 6, 202613 min read