Deterministic-first escalation architecture
Switchkit separates registered deterministic strategies, adaptive inspection, and bounded model escalation. The accepted 21-location evaluation reached 21/21 fidelity with zero model and geocoding calls.
Applied Lab
DemonstratingAI Architecture
AI Architecture explores the technical patterns that connect models to enterprise context, tools, systems, controls, and users.
What I’m exploring
Which architecture patterns allow enterprise AI systems to become useful, governed, observable, and evolvable?
Enterprise AI architecture is more than a model plus a vector database. Useful systems must connect appropriate models to authorized context, orchestration, integrations, policy, evaluation, observation, and user experience while managing cost, latency, reliability, and portability.
Framework → Lab
A focused test of connected enterprise capabilities
The capability model describes what an enterprise needs. This Lab creates practical evidence across the highlighted areas below.
Architecture
Provides the enterprise structural context and reusable platform boundaries.
Data
Provides governed information, semantics, provenance, retrieval, and agent context.
AI & Automation
Defines use cases, lifecycle, evaluation, autonomy, and operating patterns.
Security
Defines identity, policy, access, trust boundaries, observation, and resilience.
Execution
Turns patterns into testable, operable engineering capabilities.
What I’ve applied
Concrete practices and inspectable evidence
The work combines implementation patterns, controls, evaluation, and current product evidence; it does not rely on expertise claims alone.
Model Layer
Model access, routing, gateways, task fit, portability, latency, reliability, and cost tradeoffs.
Context Layer
Structured and unstructured sources, retrieval, RAG, embeddings, semantic meaning, metadata, permissions, provenance, freshness, and memory.
Orchestration Layer
Prompts, agents, tools, workflows, models, state, and coordination patterns.
Integration Layer
APIs, MCP, enterprise systems, mediated tool access, and interoperability boundaries.
What I’m learning
Applied depth comes from operating constraints
These are working lessons for technology leaders evaluating how AI moves from experiments into secure, governed production work.
- 01Applied AI architecture is partly the discipline of deciding where probabilistic intelligence adds value.
- 02Deterministic software is often safer, faster, cheaper, and easier to govern when it is sufficient.
- 03The architecture should preserve bounded model escalation without forcing every problem through an LLM.
- 04Evaluation and observability must span context, models, tools, policies, costs, and user outcomes.
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