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.
AI Workflows vs AI Agents: Stop Overengineering Your AI SystemsWhen deterministic pipelines outperform autonomous agents—and when they don’t
AI workflows and AI agents solve very different problems. This article breaks down deterministic AI workflows versus non-deterministic AI agents and gives you a clear decision framework to avoid overengineering your AI architecture.
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.
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.
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.
The Observer Pattern Reimagined: Building Reactive AI Pipelines for Real-Time Data ProcessingHow Classic Design Patterns Solve Modern Challenges in AI Workflow Orchestration
Discover how the Observer pattern and event-driven architecture enable scalable, reactive AI pipelines that process streaming data in real-time. Learn practical implementation strategies for building resilient AI workflows that respond intelligently to data changes.