The ArchiChat blog
Agent drift, knowledge decay, lifecycle management, and how to keep specialized agents current as their domain changes. Many posts use engineering teams as the worked example; the mechanics apply to any professional domain.
Concept ·
AI Agent Drift: What It Is and How to Prevent It
AI agent drift is the gradual degradation of an AI agent's accuracy after deployment, as its domain changes while its knowledge does not. Causes, cost and prevention.
Deep Dive ·
What Is AI Agent Drift? A Developer's Guide
Three types of agent drift, how to detect each one, and why re-prompting is not a fix.
How-To ·
5 Ways to Keep AI Agents Current in a Fast-Moving Tech Stack
From manual re-training schedules to automatic knowledge refresh: a ranked breakdown of what actually works.
Comparison ·
Specialized vs General-Purpose AI Agents: What Enterprise Teams Need
General tools know everything broadly. Specialized agents know your domain specifically. Here's when each wins.
Concept ·
AI Agent Lifecycle Management: Train, Deploy, Update, Evaluate
What it means to manage an AI agent across its full lifecycle, and why deployment is only the beginning.
Architecture ·
Knowledge Packages: A New Model for Keeping AI Agents Accurate
A bounded, versioned, curated set of facts for one professional domain, and why it matters for agent reliability.
Enterprise ·
AI Agent Knowledge Decay: The Enterprise Risk Teams Aren't Measuring
Why knowledge decay is harder to detect at enterprise scale, and how to build a measurement practice around it.