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Enterprise Agent Architecture

All research

Production engineering of multi-discipline AI agent systems for enterprise environments. Orchestration design, deterministic-first architectures for financial systems, and the data architecture that lets agents reason against complex organizational data.

The position

An enterprise agent that touches money is a different engineering problem from a chatbot. It has to be auditable, bounded and predictable on the paths that matter, and it has to break in ways an accountant can explain to an auditor.

Three series divide the work. Orchestration covers what decides the sequence of steps, and argues the language model is the wrong layer for it. Data architecture covers what the model is allowed to see, and how that context is assembled, governed and measured. Self-learning covers what an agent may change about itself once it is running, which is far less than most vendors imply.

The through-line across all three is deterministic-first design. Rules verify everything they can, the model judges only the gaps the rules cannot reach, and the boundary between the two is an architectural decision made before anything is built.

Series

Each series is a coherent sequence of papers on a single topic within this subject area.

  1. 01 Agent Orchestration (3 papers) How orchestration of enterprise agent systems should be designed. Where language-model-driven orchestration fails, the five canonical patterns, and the deterministic-first discipline required when agents touch financial systems.
  2. 02 Data Architecture for Enterprise Agents (4 papers) The data architecture that enterprise agents require in order to reason reliably. Each paper treats a separable component of the substrate the model consumes.
  3. 03 Self-Learning in Enterprise Agents (3 papers) When a self-learning agent is safe in a financial system. What it should learn, how its rules inherit through the business's own hierarchies, and why most of what it learns is deferred work.

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