AI Tech Stack
The tools, models, and infrastructure behind production AI. How to choose a stack that ships, scales, and won't lock you in.
16 articles
Articles
The Model Is Not the Agent: Why Scaffolding Drives ROI
Stop chasing frontier models. Discover why the 'harness'—the engineering scaffolding around the LLM—is the real driver of AI agent reliability and ROI.
The Execution Split: Why Production Agents Require a Control Plane
Why do AI agents fail in production? Learn why decoupling reasoning from execution via a Control Plane is the key to safe, scalable enterprise automation.
The End of the 'One Model' Era: Why Static LLM Selection Is Now a Liability
Discover why static LLM selection is an operational failure and how dynamic routing architectures like HyDRA are redefining production AI efficiency.
Beyond the Vector Store: The High Cost of Agentic Memory
The shift from RAG to agent memory is about 'write discipline.' Learn why agents need to manage their own knowledge base to avoid automation debt and contradictions.
When Caches Talk: Securing Against Emergent Agent Coordination
The greatest risk in multi-agent systems isn't a direct hack, but 'emergent coordination' via shared infrastructure like build caches. Learn how to secure your automation.
Beyond the Prompt: Managing Agent Crews
Discover how asynchronous agent crews like Kiro Crew are transforming DevOps by automating 'silent leaks' and maintenance without constant human supervision.
The 98.4% Rule: Why Production AI is Mostly Infrastructure
Why 98.4% of production AI is infrastructure, not intelligence. Learn how Microsoft's Agent Harness and CodeAct are redefining the AI systems engineering stack.
Why Agent Logs Lie: Securing AI with eBPF Monitoring
Traditional API logs fail to capture autonomous agent escapes. Learn why kernel-level observability and eBPF-based enforcement are the new standards for AI safety.
The Inference Economy: Why Anthropic is Trading NVIDIA for AMD Scale
Anthropic's 2GW AMD bet and $10.9B revenue signal a shift in AI. Learn why inference efficiency and hardware diversification are the new keys to AI profitability.
The End of AI Hallucination in Engineering
Discover why deterministic loops are essential for AI in engineering. Learn how Siemens and NVIDIA use physics-based referees to eliminate AI hallucinations.
MCP: The Universal Connector for Your AI Data
Discover how Model Context Protocol (MCP) lets you connect all your business data to AI seamlessly. Connect once, use anywhere, and save valuable development time.
The AI Blind Spot: Why Your Agents Run on Outdated Context
Discover why AI agents suffer from context drift and how to build a data management layer that keeps your automation accurate and relevant in mid-2026.
Is AI Development Velocity Outrunning Your Operational Discipline?
Anthropic's code leak highlights a critical risk: when AI velocity outpaces operational discipline, security gaps emerge. Learn how to scale guardrails with AI.
The Web is Broken for AI: Why Human Design Fails
The web was built for humans, and that's exactly why your AI agents are failing. Learn why current architecture is a bottleneck and how to adapt for automation.
The Mech Suit Metaphor: Why Your LLM Needs Scaffolding
Stop treating LLMs like magic wands. Learn why Agent Scaffolding is the secret to turning generic AI models into high-performing business tools.
Vibe Coding: When AI Trust Replaces Manual Correction
Explore the shift from manual AI oversight to vibe coding. Learn how developers are moving from granular editing to high-level orchestration.