Where should the boundaries between probabilistic model logic and deterministic business rules be drawn?
Architectural Pillar
AI Systems Architecture
Architecting system boundaries, model orchestration, and reliability guarantees for complex production AI systems.
Problem Territory
Engineering Problem Territory
Problem class: System structure, component boundaries, and reliability
Problem Territory
Modern AI systems rarely fail due to an inability of foundation models to generate text or embeddings. In production, degradation almost always stems from fragile system architecture: blurred boundaries between deterministic application logic and stochastic model calls, absent state isolation, cascading timeouts across interconnected services, and unmanaged infrastructure expenditure.
The AI Systems Architecture pillar addresses how to organize models, knowledge retrieval layers, orchestration services, and interfaces so that the composite system remains reliable, observable, and resilient under real-world production loads.
System Boundaries and Responsibility Segregation
A primary architectural imperative is isolating probabilistic behavior. Any call to a neural model introduces variance: the response may deviate in schema, exceed latency limits, or generate hallucinated assertions. Robust architecture ensures these stochastic variations are trapped and validated within bounded inspection layers, preventing them from corrupting core transactional state.
Context
When This Expertise Is Relevant
Typical engineering scenarios and system lifecycle stages where architectural engagement creates maximum leverage.
Transitioning from experimental prototypes or ad-hoc API calls to a multi-component production system.
Diagnosing and mitigating cascading failures, latency spikes, and nondeterminism across model and data workflows.
Evaluating foundational architecture decisions: static pipelines vs. dynamic routing, sync vs. async execution, centralized vs. partitioned state.
Preparing for technical due diligence or architecture reviews ahead of major infrastructure investment or scaling.
Architecture Decisions
Key Architectural & Decision Questions
Structural decisions that govern system reliability, component boundaries, and production unit economics.
What model routing and fallback strategies preserve SLA commitments during third-party provider degradation?
How are execution checkpoints, state transitions, and idempotency guaranteed across multi-step model workflows?
What observability infrastructure and semantic evaluation harnesses are required to detect production drift in real time?
Deliverables
Typical Engineering Deliverables & Decisions
Concrete technical artifacts, evaluation benchmarks, and architectural decision records.
Target System Architecture Blueprint defining component boundaries, interfaces, and data flows.
Architectural Decision Records (ADRs) for model selection, routing strategies, and state isolation.
Failure Mode and Effects Analysis (FMEA) covering proprietary API dependencies and local inference pipelines.
Staged technical implementation and validation roadmap for production readiness.
Scope Boundaries
What This Work Is Not
Clear scope boundaries preventing commoditized service interpretations and ensuring focused technical leadership.
- Not generic IT staff augmentation or time-and-materials developer outsourcing.
- Not frontend UI skinning, visual design, or routine CMS integration.
- Not reselling superficial SaaS wrapper templates without bespoke system architecture.
- Not speculative technology commentary divorced from tangible engineering constraints.
Evidence Base
Related Research & AI Lab Studies
Empirical validation and laboratory fault-injection studies verifying architectural patterns.
When an AI Product Does Not Need an LLM: Deterministic Inference, Provenance and Controlled Uncertainty
An engineering decision framework for choosing between generative LLMs and deterministic inference in production runtime. Why replacing an explicit knowledge base with stochastic prompts erodes reproducibility and trust, how to structure claim-level provenance, and which objective criteria determine where a foundation model creates genuine value versus unnecessary cost and liability.
Large PDF Translation Is a Workflow System, Not a Translation API
An architectural investigation into translating voluminous, structurally complex PDF documents. Why the naive abstraction of "PDF in → translation API → translated PDF out" breaks down across multi-hundred-page enterprise documents, how to structure an intermediate representation (DocumentIR), and why pipeline resumability, checkpoints, and layout reconstruction matter far more than model choice.
Simple Before Distributed: When an AI System Actually Needs Workflow Orchestration
An architectural complexity framework for AI workloads: identifying when synchronous execution suffices, and when queues, distributed workers, and durable workflows are genuinely required based on a 12-factor evaluation matrix.
When Green Tests Lie: Coding Agents and Real Runtime Verification
An investigation of a reproduced production failure in vitkovskyi.com: despite extensive automated test suites and green agent implementation reports, the enhanced JavaScript form submitted FormData (multipart/form-data), which the PHP endpoint deliberately rejected with HTTP 415. Mocked Playwright tests failed to detect the defect. The targeted engineering repair converted the payload to URLSearchParams, aligned CSP for Cloudflare Turnstile, reconciled cross-runtime field limits, and introduced real browser-to-PHP integration tests with zero endpoint mocks.
Perfume Engine: An Evidence-Backed Recommendation Prototype Without a Runtime LLM
Perfume Engine began as a broad fragrance-intelligence concept, but was deliberately narrowed during an early iteration into a controlled validation prototype: 12 perfume identities, 6–8 editorial mappings, exact search, manual evidence collection, and deterministic recommendation logic. The experiment was not designed to inject an LLM at every layer, but to verify whether a recommendation system could remain explainable, reproducible, and provenance-first. Because Perfume Engine subsequently evolved into a broader system, this study documents a specific historical architecture experiment rather than the full current product architecture.
Engagement
Discuss System Architecture
If you are designing a complex AI system or encountering production stability bottlenecks, prepare your requirements for asynchronous review.
Initiate a Structured Discussion
Review qualification criteria and advisory formats on the Work With Me page to evaluate architectural fit.