AI Workflows vs. AI Agents vs. Agentic AI: A Developer's Guide to Building Intelligent SystemsCutting through the hype to understand when determinism, autonomy, or a blend of both is the right architecture for your AI-powered system
Learn the architectural differences between AI workflows, AI agents, and agentic AI - and how to choose the right pattern for your production systems.
Short-Term vs Long-Term Memory in AI Agents: What to Store, When, and WhyA practical engineering guide to memory tiers, retrieval, and forgetting in production agent systems.
Learn how to design short-term and long-term memory for AI agents, including what to store, retention policies, retrieval strategies, and common pitfalls for real-world deployments.
Zero-Shot, Few-Shot, and Tool-Using Agents: Choosing the Right Prompting StrategyHow to decide between instruction-only, examples, and structured tool calls as complexity grows.
Understand zero-shot vs few-shot prompting in modern AI engineering, when each works best, and how tool use and structured outputs change your prompt strategy.
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.
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.
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.
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.
The Strategy and Chain of Responsibility Patterns: Orchestrating Complex AI Agent BehaviorsDesign Patterns That Make AI Agents Flexible, Maintainable, and Production-Ready
Learn how Strategy and Chain of Responsibility patterns create flexible, maintainable AI agent systems. This comprehensive guide covers implementation techniques, code examples, and architectural strategies for building production-ready AI agents that adapt to changing requirements.
Designing Agent Memory: Summaries, Episodic Logs, and Semantic FactsThree memory formats and how to combine them for speed, accuracy, and personalization.
Compare summary memory, episodic transcripts, and semantic fact stores for AI agents, with practical guidance on hybrid designs, retrieval, and privacy-aware storage.