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Author: Sarah Chen

How AI Agents Are Changing WordPress Development

WordPress powers over 40% of the web, yet the development workflow behind it has barely changed in two decades. You still SSH into a server, edit PHP files, click through admin panels, and cross your fingers that a plugin update won’t break something at 2

AI Agent Safety: Preventing Automated Systems from Causing Harm

In April 2025, an AI agent we built for a client autonomously sent 2,300 emails to the client’s customer list. The content was correct—it was a promotional campaign the client had approved. But the agent was supposed to send it to a 500-person test segment,

Procurement Intelligence: How AI Is Reshaping Enterprise Purchasing

Enterprise procurement is a $13 trillion annual spend category globally, and most of it is managed with spreadsheets, email threads, and institutional knowledge locked in the heads of senior buyers. Over the past 18 months, Harbor Software has built procurement intelligence systems for three enterprise
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Multi-Model AI Architectures: Routing, Fallbacks, and Cost Control

Using a single AI model for every task is the equivalent of using a single database for every data pattern. It works until it does not. GPT-4o is excellent at complex reasoning but costs 15x more than GPT-4o-mini for simple classification tasks. Claude 3.5 Sonnet

Observability for AI Applications: Beyond Traditional APM

Traditional application performance monitoring (APM) tools — Datadog, New Relic, Grafana — are designed for request-response systems where latency, error rates, and throughput tell you most of what you need to know. AI applications break this model. An LLM call that takes 3 seconds and
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Building Durable Workflows: Surviving Crashes and Deploys

A durable workflow is a sequence of steps that completes exactly once, even if the process executing it crashes, the server restarts, or a deployment rolls out mid-execution. This sounds like a niche concern until you inventory the workflows running in any production system: payment

Edge Computing for AI Inference: When and Why

The Latency Problem That Started Everything A client came to us with a straightforward request: add real-time defect detection to their manufacturing line. The camera captures a part every 200 milliseconds. A classification model determines pass or fail. The constraint: the entire pipeline — image

Designing APIs for AI Agents: New Patterns for a New Consumer

AI agents are a new category of API consumer. They are not humans clicking through a UI, and they are not scripts following a predetermined sequence of API calls. They are semi-autonomous programs that decide at runtime which endpoints to call, in what order, and
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