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 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.
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.
From Scripted to Smart: Evolving UI Automation Workflows with Screenplay Pattern and AILeveraging deterministic foundations and non-deterministic AI capabilities for next-generation test automation
Learn how to design UI automation workflows that combine the reliability of deterministic Screenplay interactions with the adaptability of AI-driven non-deterministic approaches. Practical strategies for grouping tasks, handling questions, and choosing the right workflow pattern.
How to Build a Versioned, Testable, and Model-Agnostic Prompt LibraryA Practical Guide to Structuring, Versioning, and Evaluating AI Prompts at Scale
Learn how to architect a prompt library that evolves safely. This guide covers version control, dynamic templating, prompt evaluation pipelines, and model adapters to ensure your prompts remain consistent, traceable, and adaptable across AI models.
LLM Integrations in Practice: Architecture Patterns, Pitfalls, and Anti-PatternsHow to integrate large language models into real systems without creating fragile, expensive messes
Integrating LLMs into production systems is an engineering problem, not a demo exercise. This post covers proven integration patterns, common mistakes, and what not to build with LLMs.
LLMs Are Not Products: Why AI Applications Matter More Than ModelsUnderstanding the real difference between large language models and production-grade AI applications
Large language models get the spotlight, but AI applications deliver real value. Learn why LLMs alone are not products, how AI workflows turn models into systems, and what AI engineers should focus on when building scalable, reliable AI applications.
Microservices vs. Monolithic Architecture in AI Agent Systems: A Comprehensive Decision FrameworkChoosing the Right Architectural Pattern for Your Multi-Agent AI Infrastructure
Explore the trade-offs between microservices and monolithic architectures for AI agent systems. This guide provides a practical decision framework with real-world examples, performance benchmarks, and best practices for scaling intelligent agent workflows.