Raw documents in; a queryable knowledge graph, cited answers, and autonomous research reports out. A Go engine that fuses classical IR, knowledge-graph extraction, and an agentic reasoning loop — with epistemic grounding so the system knows what it actually knows.
I directed AI coding agents through the whole thing — architecture, implementation, and evaluation of a fairly deep system (agent loops, retrieval, graph reasoning). My job was framing the problem, making the design calls, and verifying the output was sound. The takeaway isn’t that I’m a Go expert — it’s that I can drive AI to produce a working knowledge engine and know enough to keep it honest.
This isn't a mock — it's the actual application I built, running live on my GPU box. Search across every book, ask cited questions, run the deep-research agent, and browse the knowledge graph. (Answers run on a home GPU, so give them a moment.)
The deep-research agent doesn't run a fixed number of steps. It runs a plan → retrieve → reflect loop and self-terminates on a composite stop condition: coverage and confidence met, or consecutive dry rounds, or a hard cap. Hit run and watch a live trace.
Every finding is conformal-gated before it's admitted — the agent abstains rather than assert when the evidence is a bare inferred edge. Grounding is a first-class layer, not an afterthought.
The extraction pipeline reads text, resolves entities, and links concepts with grounded, provenance-tracked edges. Pick a sentence and watch entities and relations light up.