I’ve been working on a Python library and formal framework to make Agentic AI systems less fragile.

The core premise is that biological cells are essentially distributed information processors that solved "hallucinations" (noise), "infinite loops" (cancer), and "resource exhaustion" (ischemia) billions of years ago. Instead of just using this as a loose metaphor, I used Applied Category Theory (specifically Polynomial Functors in Poly) to rigorously map Gene Regulatory Networks to Software Agents.

Key concepts implemented in the library:

* Metabolic Coalgebras: We model token budgets as a thermodynamic resource. This makes the "Halting Problem" decidable for agents by enforcing strictly decreasing resource states (like ATP depletion), preventing runaway loops.

* CFFLs (Coherent Feed-Forward Loops): A topological motif for "two-key execution" that mathematically reduces hallucination probability (assuming model diversity).

* Chaperones: Partial validators that treat schema mismatches not as "undefined" errors, but as misfolded proteins requiring active repair loops.

This is an early attempt to move from "prompt engineering" to "topology engineering."

Paper (Preprint): https://github.com/coredipper/operon/blob/main/article/main....

I’m particularly interested in feedback on the definition of the Metabolic Coalgebra and if anyone has tried applying Poly to production AI systems before.