Agentic AI vs Conversational AI: The Shift Nobody Explained Simply
From chat widgets to autonomous execution. Discover the architectural difference between reactive chatbots and goal-driven agentic loops in 2026.
8/1/202623 min read
#AI Engineering
13 articles tagged with AI Engineering
Building a Production Coding Agent with Guardrails and Rollback
A step-by-step build of a coding agent that can actually be trusted with a real repository: scoped sandboxing, tiered permissions, checkpointing, and a rollback path for when it gets something wrong.
7/22/202613 min read
From RAG to Agentic Retrieval: How Retrieval Patterns Evolved in 2026
A single embed-and-retrieve step was never going to be the final form of grounding an LLM in real data. Here's how retrieval architectures actually changed as teams ran into its limits.
7/19/202612 min read
Python 3.13 Free-Threading: What It Means for AI Workloads
PEP 703 shipped an experimental GIL-free build of CPython. For AI backends specifically — not Python in general — here's what actually changes, and what doesn't yet.
7/17/202610 min read
Debugging Autonomous Agents: Observability for Non-Deterministic Systems
You can't set a breakpoint on a model's reasoning. Here's what actually works for figuring out why an agent did what it did, after the fact, from a trace instead of a stack.
7/16/202611 min read
Cost Attribution for AI Features: Tracking What Your LLM Calls Actually Cost
Your cloud bill has a line item for 'AI API costs' and no way to tell which feature, customer, or prompt is driving it. Here's how to build attribution before the finance conversation forces you to.
7/13/202612 min read
Multi-Agent Orchestration Patterns: Supervisor vs Swarm Architectures
Two dominant shapes for multi-agent systems solve different problems. Picking the wrong one doesn't just underperform — it actively fights the structure of your task.
7/13/202611 min read
The Agentic Engineering Stack in 2026: Tools, Patterns, Pitfalls
A survey of the layers that make up a real agentic system today — orchestration, memory, tools, evaluation, guardrails, observability — and the specific ways teams get each one wrong.
7/10/202613 min read
Synthetic Data Generation for Fine-Tuning Specialist Models
Real labeled data for a narrow task is often scarce, expensive, or both. Generating it synthetically works — but only if you take the quality filtering and diversity steps as seriously as the generation itself.
7/7/202611 min read
Model Fleets: Why Enterprises Are Moving From One LLM to Many
Standardizing on a single model made sense when there were three viable options. With dozens of capable models at wildly different price and latency points, routing across a fleet is now the more defensible default.
7/4/202610 min read
Why FastAPI Became the Default Backend for AI Products in 2026
Node.js won the last decade of API backends. For AI products, the calculus flipped. Here's the actual reasoning — not hype — behind why most AI teams reach for FastAPI first.
6/26/20269 min read
Dynamic Prompt Engineering with DSPy: Moving Beyond Hardcoded Prompt Templates
Stop manually tuning strings. Discover how to program LLM pipelines programmatically, compiling and optimizing prompts dynamically using Stanford's DSPy.
5/31/202611 min read