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Microsoft's Multi-Agent Reference Architecture in Go. Protocol, registry, bus, governance, orchestration, observability, evaluation — and how the seven hold each other up.
The course wrap-up: a Jupyter notebook driven by Go, using GoMLX for tensor ops and GoNB as the kernel. Showed me how to do exploratory Go AI work in the same shape data scientists already use.
A complete chat application: Go backend with RAG, React frontend, single binary. Showed me how to ship a full-stack AI demo without a separate frontend deployment.
Cursor / Claude Code in 600 lines of Go. The agent has read/write/search tools over a project directory and a loop that lets it iterate on its own work.
PDFs are the format that breaks every RAG pipeline. Docling is the IBM-research extractor that handles layout, tables, and figures. The example wires Docling + LLM to make PDFs usable.
Transcribe a video, chunk by timestamp, embed each chunk, RAG-style chat over the result. The shape that powers "ask questions about this meeting recording."
Generate a text description of an image with a vision LLM, embed the description, store in pgvector. Search becomes "find images that match this query" — works surprisingly well.
An agent that can call tools to call tools can drift indefinitely. The escalation budget caps depth and cost; the audit trail records every step so you can replay what the agent did.
An LLM that controls the output can embed malicious HTML, exfiltrate data via crafted links, or inject prompt-stealing payloads. Sanitisation is the defense; the example shows what to allow and what to strip.
A RAG pipeline that ingests user-supplied documents is a prompt-injection vector. An attacker uploads a document with hidden instructions; the LLM retrieves it and follows them. Defense: input filtering, content classification, output verification.