Build systems.
Keep them human.

Machine-intelligence systems built as software systems.

My current work centers on multi-agent coordination, persistent AI memory, local model and runtime tooling, governed AI workflows, retrieval, validation, recovery, and enterprise integration. I approach those problems as architecture and engineering work: explicit boundaries, observable behavior, durable context, and human review rather than treating a model response as the whole system.

I use machine intelligence here in a deliberately practical sense: engineered software around models and agents. My public portfolio does not claim foundation-model training or novel model research; it documents the surrounding systems, runtime, memory, coordination, integration, and governance work.

Public proof you can inspect.

A central public example is Multi-Agent Memory, a source-available private-intranet reference implementation for external-agent identity, scoped coordination, messaging, reviewed durable memory, claims, leases, and human-verifiable state. The public repository, my broader NuGet profile, and GitHub provide implementation-level context.

For a compact technical review, start with Machine Intelligence, then move into the case studies when you need constraints, architecture decisions, validation methods, and explicit limits. For hiring or recruiter review, the stable résumé page keeps the current two-page artifact and accessible HTML together.

More than two decades of enterprise engineering.

The machine-intelligence work sits on a long software foundation: .NET, Microsoft Azure, SQL Server, TypeScript and Angular, APIs, testing, modernization, production support, architecture, and technical mentoring. My public career record documents Azure work from 2010 onward and a broader history of building and modernizing business-critical software.

I’m based in Cicero, Illinois, in the Chicago area. For the detailed employment timeline, technical case studies, résumé, and current evidence, use MikeKappel.com as the source of record.

Why this belongs on AI Spiralism.

AI Spiralism explores another side of the same concerns: what happens when computational systems become material for making, inquiry, revision, memory, and human choice. The project is intentionally visual and experimental, but its underlying design keeps returning to ideas I care about in engineering too—clear authority, inspectable state, reversible choices, durable context, and room for a person to decide what the next turn should be.

Other public work.

For visual creativity outside this project, visit MichaelJosephKappel.com. For software and public code, see GitHub and the Michael.Kappel NuGet profile.

Professional background and contact details summarized from MikeKappel.com, where the current résumé, evidence paths, claim boundaries, and direct contact information are maintained.