Language Models: Patterns of Engagement
Five asymmetries between managing people and managing language models. Three anti-patterns. One meta-principle.
Bart Kolendowski
Researcher & Co-founder
London, ON
putting AI agents to work
I'm a researcher working on bioinformatics with AI agents in Canada. I also co-founded HoppeSyler Scientific — we make reagents and ship them to life science labs across Canada and the USA.
HoppeSyler began as a conventional reagent business but in 2022 (when ChatGPT was still just "GPT") I saw the writing on the wall and started the process of rebuilding and organizing everything around the inevitable rise of AI. Today, 3.587123 years later, personalized AI agents permeate the entire structure — running every aspect of the business that doesn't require physical labor.
Three years in, the surprise has been how little of the work is the agents themselves. Most of it has been the engineering around them — making sure they fail safely, escalate when they should, and don't ship something they shouldn't.
recently writing
essays from real-stakes agent work
Agentic principles
patterns that have held up under real stakes
- Generate, don't iterate.
Volume-plus-selection beats single-pass refinement when generation is free and evaluation is the bottleneck. Five variants, pick the best, refine the parts still weak. The operator surveys a landscape, not climbs one hill.
- Radical candor has zero cost.
A model has no ego, no morale, no political standing. Every hedge word inherited from human collaboration is a tax with no return. Prompts should read as specifications, not messages to a coworker.
- Fresh sessions are free red teams.
One context builds, another attacks. Adversarial review used to be infrastructure only large teams could afford. Now it's a second conversation window — and almost no operator is using it.
- Context documents are infrastructure.
In a model-augmented workflow, written context becomes the memory substrate every session loads from. Maintain it the way you maintain a database schema — versioned, refactored as the work evolves.
→ full essay: patterns of engagement
building
tools running the business right now
-
▸operator-dashboardWhere agents run the back office. Inbox triage, inventory sync, escalations, change feed — work that normally requires headcount.live
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▸grant-scannerAn always-on prospector. Reads federal grant databases, qualifies bioinformatics fits, feeds the sales pipeline.live
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▸paper-radarAccount intelligence at scale. Connects fresh publications to active customers — surfaces what each lab is doing and what they'll need next.beta
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▸agent-osThe substrate the agents run on. Projects, weekly cycle, journals, communications — all addressable, all auditable, all under version control.live
code, last year
contributions, last 365 days
52 wks ago
today
agent-augmented output. commits is now easy; the real work is in deciding what to ship.
reading
on the desk
- currentlyThe Coming Wave
Less interesting on the AI half than on the synthetic biology half.
- just finishedThe Goal
A novel about manufacturing constraints. Re-read every two years.
- re-readingThe Beginning of Infinity
Slowly, three pages a night.