Chaining agents multiplies errors instead of dividing work: DeepMind measured up to 17.2x amplification. Here is the math behind that, the three topologies that hold up in production, and the checklist I run before shipping an agentic system.
Dashboards are no longer the main consumer of your data. Here is what I would change in a data stack to make it readable by agents: fewer tools, open table formats, a real semantic layer, and feedback loops that act without waiting for a human.
A tool is one capability, a skill is the orchestration around it. Notes on writing agent skills in Python instead of Markdown, and where MCP fits when you build agents outside Claude.
Classic RAG runs one retrieval pass and stays predictable. Agentic RAG keeps searching until it has enough evidence, and pays for it in latency and tokens. How I pick between them, and what breaks when a loop has no budget.
Demo agents fall over in production because errors compound across steps and prompts break when the business process changes. Structured output, LLM-as-a-judge evals and OKR alignment are what get a prototype across.
AI handles static problems well and struggles with systems that keep moving, which is where operational scar tissue still pays. On static versus flux systems, the physical limits on adoption, and why judgment stays scarce as cognition gets cheap.
A practical way to stop babysitting deep learning runs: containerize your training script, add a health-check sidecar, and let an agent handle restarts and hyperparameter tweaks.
Research on 13 LLMs in simulated markets found the agents holding price floors nobody asked them to hold. For a WooCommerce store running an AI pricing plugin that is an antitrust problem, and the fix sits in how the pricing logic is written.
The SaaS moat is gone now that an agent can rebuild your product in an afternoon. Here is the case for a herd of small automated niche businesses over one unicorn, and the WordPress REST API and WP-CLI setup that makes running a dozen of them possible.
Dashboards tell you a delivery was late. They do not say which team caused it. This post covers the MCP setup I use so Claude can read ERP, WMS and TMS timestamps and work out where the delay started.