The Stack
From business requirements to auditable AI execution.
This is the architecture behind my agentic AI systems. It combines three layers — expertise → process → control — that turn a business request into a result you can check, explain, and improve.
The system does not simply ask AI agents for an answer. It defines what needs to be done, checks that the required information is available, validates each step, and records what happened.
flowchart LR
A["01 · EXPERTISE<br/>Catalog"] --> B["02 · PROCESS<br/>Workflows"]
B --> C["03 · CONTROL<br/>Agentic Runtime"]
C --> D["04 · RESULT<br/>Auditable result"]flowchart TB
A["01 · EXPERTISE<br/>Catalog"] --> B["02 · PROCESS<br/>Workflows"]
B --> C["03 · CONTROL<br/>Agentic Runtime"]
C --> D["04 · RESULT<br/>Auditable result"]See how it all fits together → — the full architecture behind this flow: entry modes, step gates, recorded traces, and the design decisions on record.
Three layers, one flow
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01 · EXPERTISE — Catalog
38 specialized AI agents and 37 skill folders form a reusable expertise layer. Each agent has a defined role and can be combined with others inside structured business workflows — product management, Agile, testing, architecture and other enterprise frameworks.
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02 · PROCESS — Workflows
10 structured workflows turn that expertise into repeatable, end-to-end business processes — from AI product scoping to project post-mortem. Each one defines what happens, in what order, and what must be validated before the next step can start.
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03 · CONTROL — Agentic Runtime
A TypeScript runtime executes each workflow and validates every step. When a requirement is not met it stops instead of silently continuing, and every run is recorded.
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04 · RESULT — Live proofs
Every run produces a clear, inspectable outcome: passed, failed, missing information, or returned for rework — never a confident answer someone has to discover is wrong later.
Real workflows. Real executions. Real outcomes — including the failures.