AI Agents vs AI Pipelines: An Architectural Trade-off, Not a TrendUnderstanding control flow, feedback loops, and failure modes
AI agents are not a silver bullet. This post compares AI pipelines and agent-based systems through an architectural lens, focusing on control flow, failure modes, and long-term maintainability.
AI Application Pricing Models Explained: From Subscriptions to Usage-Based BillingA practical breakdown of how AI products charge customers—and why it matters for engineers and founders
Explore the most common AI application pricing models, including subscriptions, usage-based billing, and hybrid approaches, with real-world examples and trade-offs to help you design sustainable AI products.
AI Engineering Fundamentals: What It Is, What It Isn't, and Why It's Not Just MLA practical breakdown of AI engineering beyond hype, buzzwords, and academic machine learning
AI engineering is not about training models from scratch. This article clarifies what AI engineering really is, what it is not, and how it differs from data science and traditional machine learning.
Architecting a Scalable Prompt Library: From Abstraction to ImplementationBuilding the Core Layer Between AI Models and Your Application Logic
A prompt library isn't just a collection of strings—it's an architectural layer that defines how your app interacts with AI models. Learn how to design a scalable, testable, and versioned prompt library using proven software engineering patterns, schema validation, and modular composition.
Design Patterns for AI Workflow Decision MakingProven architectural patterns for predictable, auditable AI behavior
Explore common AI decision-making patterns such as decision trees, ensemble voting, human-in-the-loop, and cascading models, with guidance on when and why to use each.
Designing AI Applications: When No-Code Is Enough and When You Must Write CodeA decision framework for building real-world AI applications without painting yourself into a corner
Building AI apps? Learn how to decide between no-code platforms and code-based solutions, based on complexity, control, scalability, and long-term ownership.
Deterministic AI vs Autonomous Agents: Choosing the Right Level of IntelligenceWhy not every problem needs an AI agent that thinks for itself
Not all AI systems need autonomy. Learn the practical differences between deterministic AI workflows and non-deterministic AI agents, with real-world examples to help you choose the right approach.
Deterministic vs Non-Deterministic Workflows in Screenplay Pattern: A Guide for AI-Powered UI AutomationUnderstanding when to use structured workflows versus adaptive AI-driven approaches in your test automation strategy
Explore the key differences between deterministic and non-deterministic workflows in Screenplay pattern UI automation. Learn how actors use interactions to perform tasks, answer questions, and when to apply each workflow type for optimal test reliability and AI flexibility.
Engineering a Scalable Prompt Library: From Architecture to CodeDesigning the Core Abstraction Layer Between Your App and AI Models
A prompt library is more than stored strings—it's an architectural foundation. Learn how to build a scalable, maintainable, and testable prompt library that decouples AI model quirks from your application logic using proven patterns and design principles.