Henry remembers across conversations using three memory systems: markdown workspace files, a knowledge graph, and vector recall via LanceDB.

Nemotron handles the default path, Codex handles code generation, Gemini is available for fallback, and Gemma 4 runs lightweight local ops work.

25 cron jobs run independently: briefings, opportunity scans, health checks, graph rebuilds, token refresh, and operational watchdogs.

This is wired into Microsoft Graph, Granola, GitHub, Vercel, and Telegram — so the agent operates on actual calendars, meetings, code, and deployments.

Memory lives in files you can inspect. Outputs land in docs. Mission Control shows jobs, graph state, tasks, and routing live.

No human trigger. The agent wakes up, loads context, researches, scores, and delivers — every morning before Brett's first meeting.

Ideas aren't scored generically — they're weighted against Brett's active projects, strategic priorities, and upcoming events from the knowledge graph.

Every idea is cross-referenced against UCSD's enterprise data catalog — which systems have the data, who owns them, and what access tier (P1-P3) is required.

Top-scoring ideas flow into Mission Control's pipeline. When Brett approves one, it becomes the brief for the Software Blueprint — triggering the full multi-agent build pipeline automatically.

7:00 AM: scan surfaces idea + data sources. Brett approves. Blueprint plans, builds, tests, deploys. By end of day: live app on Vercel with GitHub repo, UCSD SSO, and docs.

blueprint new data-portal --type fullstack --db postgres
6-agent pipeline — live in ~15 min from idea.

Planner → Architect → Builder → QA → Deploy → Docs. Each uses the right model. Builder defaults to Nemotron on-prem for UCSD apps.

Three approval points via Telegram: plan, spec, and localhost preview. The agent proposes — Brett decides.

Build fails → error feedback to Builder → up to 3 GAN-style iterations. Escalates to human only after max retries.

Each agent broadcasts state to Mission Control — live animation shows which agent is working, waiting, or complete.

The point isn’t to summarize everything. It’s to show Brett what matters in under 30 seconds of reading.

Only creates tasks for items requiring Brett’s action — replies, decisions, deadlines. Skips noise, invites, and automated messages.

Email and calendar don’t just get summarized — they become structured backlog items in Mission Control.

Runs daily without prompting. If the briefing is missing, a watchdog alerts Brett that the pipeline broke.

Instead of disappearing into a notes app, key decisions and commitments become reusable operational context.

The filter is the product: only tasks Brett owns or will otherwise lose track of get added.

Long transcripts are compressed into decisions, actions, and context-rich summaries Brett can scan instantly.

The same meeting corpus later powers neglected relationship detection, follow-up prompts, and open-loop tracking.

The briefing is tuned to TritonAI, Brett’s talks, vendor evaluations, and higher-ed AI strategy — not mass-market AI hype.

If a story doesn’t connect to Brett’s projects or priorities, it doesn’t make the brief.

This is less “news” and more briefing material for presentations, cabinet conversations, and strategic decisions.

Every intelligence run lands in Docs, creating a searchable history of AI market and peer-institution signals.

Instead of waiting for complaints to climb formal channels, Henry listens where pain actually shows up first.

The magic is not just spotting pain — it’s knowing whether UCSD actually has the systems and data to solve it.

This is the front end of the innovation funnel: pain signals become candidate agents, dashboards, and campus tools.

It helps answer a key executive question: which campus problems are loud, repeated, solvable, and worth acting on now?