Dynamical systems
Work evolves over time. Inputs, tools, agents, model outputs, constraints and human decisions form a system whose behavior can be shaped.
Research and science
Complex AI workflows should converge toward stable, testable outcomes.
The name AttractorFactory comes from a simple idea: complex AI workflows should converge toward stable, testable outcomes instead of producing uncontrolled sequences of model outputs.
Translation
Scientific language, engineering discipline
The ideas stay useful because they become concrete design choices: target states, measured feedback and bounded autonomy.
State space
Attractors
Boundaries
Attractor states
The target is a stable, testable state: a reviewed patch, a validated report, a reconciled ledger, a correct escalation.
Multi-agent intelligence
Specialized agents handle analysis, execution, critique, testing, retrieval and reporting — instead of pretending one model should do everything.
Empirical feedback
Routing improves from observation: quality, latency, cost, failures, corrections and human interventions.
Human agency
Automation is strongest when the system makes decisions visible and leaves judgment, responsibility and approval where they belong.
Trustworthy operations
Traceability, logging, robustness and risk controls are architectural requirements — not compliance decoration.