System research · Operational software
AI agents & CRM architecture
Technical studies and implementations connecting communication events, structured records, workflow state, permissions, and bounded AI behavior.
Alexander documents CRM and AI-agent architectures for workflows that do not fit generic data models. The focus is how repetitive steps, judgment points, and human control are represented in software.
These systems connect communication logs, record history, task state, scheduling, notes, inventory data, analytics, and permission models into a traceable workflow.
The goal is not an impressive demo. It is reliable software that reduces repetitive work while preserving clear rules, auditability, and human escalation.
Technical scope
Architecture and implementation notes.
Connected communication records
Messages and channel events connect to stored records so that each state transition retains its previous context.
Workflow and scheduling state
Repeatable classification, scheduling, and status changes are modeled explicitly while judgment-dependent states remain visible to people.
Rules and human escalation
Value constraints, permissions, and exception rules are represented explicitly, with escalation whenever an event falls outside them.
Multi-tenant architecture
Shared infrastructure separates tenant data and configuration while allowing domain-specific workflow and record models.