Campus AI Agents

The work

As CSU’s AI Strategist I steward the university’s agent platform and help teams across campus ship agents that actually work: student-facing assistants, departmental knowledge agents, and specialized tools for groups like learning design and graduate teaching support.

The stakes are real. An agent that makes up tuition numbers isn’t a fun quirk, it’s a problem with a phone number attached. So the interesting part isn’t any single agent; it’s the method that keeps dozens of them trustworthy.

The method

• Knowledge is designed, not dumped. Raw transcripts and PDFs get pre-processed into structured Markdown (clean topics, consistent headings, explicit policy language) before they ever reach a knowledge base. Most retrieval quality is determined before retrieval happens.
• Agents get tools, not just documents. Where live data matters, agents call MCP servers instead of reading snapshots. The OpenAlex server is the template.
• Local teams own their agents. The sustainable model is distributed: each unit maintains its own agent and knowledge, with central governance and shared patterns keeping quality up. One central team maintaining fifty agents is a bottleneck with extra steps.
• Guardrails are mostly content work. When an agent says something wrong, the cause is usually ambiguous source material, not model misbehavior. Fixing the corpus beats fighting the prompt, almost every time.

Why you might care

If you’re just getting started with agents: the demo is the easy part. Anyone can wire up a chatbot over a folder of PDFs in an afternoon, and it will look great in the meeting.

Getting agents into production across a decentralized institution, with real owners, real governance, and answers you can defend to a dean, is an organizational design problem wearing a technology costume. That’s the problem I actually work on, and the four practices above are what’s survived contact with reality so far.