The org-chart mistake that kills agent fleets
Everyone deploying AI agent fleets faces the same structural question - and the two most popular answers are both wrong.
Eugene Vyborov Blog (AI future, entrepreneurship)
I write about AI Agent systems, Mental Models, Cognitive Biases, AI, Game Theory, Math of Your Decisions
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The Noise Threshold
Why peak intelligence is indistinguishable from randomness - and what that means for minds, machines, and one experiment we are actually running
How to choose AI agent platforms to run agents in production
Half the names are hype, half are genuinely good, and from the outside it's almost impossible to tell which is which. So I stopped answering it one message at a time and wrote the guide.
Agents that improve without retraining
We spent five months running a fleet of AI forecasters that keeps score on itself. 73,552 resolved predictions later, we wrote the paper - and the biggest finding is not the one we expected.
Master loop engineering. Run it on Trinity
Master loop engineering - the discipline of designing agent loops, and the four layers that keep one from running away - then run it unattended on Trinity.
When Your Knowledge Base Learns to Think While You Sleep
*Cornelius v05.26 adds an autonomous thinking engine, a perception layer, and a Socratic interview mode - here is what each does and why they required a fundamental shift in how the agent runs.*
The External Anchor Principle: How to Build Self-Improving Agents That Can't Game Their Evals
The AI research community says eval gaming is unsolved. For most commercial deployments, the architecture that prevents it isn't difficult - it just requires one decision made at the beginning.
Cornelius analyzed Current Hantavirus Outbreak - we are OK, it thinks
Nine Analytical Cycles: How AI Systematic Reasoning Modeled a Real Outbreak in Real Time
Practical Agent Design: Composing Agents from Components
An agent isn't one thing. It's a stack of small, swappable components - and the design work is choosing which components a given agent actually needs.
The Alignment Trap: We're Solving the Wrong Problem
The AI safety conversation is focused on making models behave well. The real problem is who gets access to the tools that make bad behavior catastrophic.