ARCHITECT
What foundations do we need?
Build the factory before building the product. Organizations that scale AI successfully invest in reusable infrastructure — orchestration layers, data pipelines, identity frameworks, shared building blocks — before deploying individual agents.
Assessment Controls (12)
Every AI initiative that passes through this pillar must satisfy these controls. The maturity model measures how consistently the organization enforces them.
Data Pipeline Governance
Are data pipelines governed and accessible to AI tools through controlled interfaces?
AI Agent Identity Management
Do AI agents have their own machine identities, or do they inherit human user credentials?
Multi-Agent Orchestration
Is there an orchestration layer for coordinating multi-agent workflows?
Reusable AI Components
Are there reusable building blocks (templates, connectors, shared services) across AI initiatives?
Agent Development Velocity
Can the organization spin up a new agent initiative in weeks (factory model) or does each one take months (bespoke model)?
Data Quality & Bias Assessment
Is the data infrastructure audited for quality, completeness, and bias before agents access it?
Third-Party AI Integration Inventory
Are third-party integrations (APIs, plugins, external tools) inventoried and assessed?
Agent Development Lifecycle
Is there a standard agent development lifecycle (design, test, deploy, monitor)?
Prompt Gateway & DLP Enforcement
Is there a centralized prompt gateway that enforces DLP, PII masking, and injection filtering before prompts reach any LLM?
RAG Pipeline Security
Are RAG pipelines secured? (Embedding encryption, RBAC on vector queries, retrieval-layer injection monitoring)
Measurement Mechanism Design
Is the mechanism for measuring outcomes designed during architecture, not bolted on after deployment?
Enablement Plan Design
Are training and adoption plans built during the architecture phase? Who needs to be trained, on what, by when — defined before rollout, not after.