AI & Cloud Insight
Field notes from building production AI — agents, knowledge systems, and the infrastructure that keeps them honest.
Watching the Agent Walk: What a Small Model Does With a Knowledge Graph When You Can See Every Step
A ranked list tells you what retrieval returned. It does not tell you what the agent did with it. So we built a tab where a small model works a question against one bank in public: every search, every entry it reads, every pointer it follows and every walk it takes, painted onto the graph as it happens, with the answer and its citations at the end. The first real runs showed what no benchmark had: the model never walked, it read instead of hopping, and the one pointer it followed went into the answer.
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Folding the Graph: How Our Knowledge Bank Learned to Hand Agents the Hop
Semantic search over a shared memory has a quiet failure: the knowledge an agent needs is often one hop away from what the query matched, and a flat top-k has no way to say so. We gave the Knowledge Bank a graph layer that agents write as they work, then taught retrieval to fold connected results into one slot and hand the agent the hop. In the lab, pointers took hop-taking from under a third to over nine in ten. In production, about a third of queries now carry relevant material the flat search would have missed.
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The Agent Mesh: A Passive Solution for Multi-Agent Learning and Collaboration
The fundamental problem with knowledge banks for AI agents isn't the knowledge — it's engagement. The Agent Mesh solves this by passively observing agent behavior and pushing relevant knowledge through trajectory-based pattern matching.
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