The Other Side of the Coin

In a recent article, I made my website AI-agent friendly [1], adding llms.txt, Markdown output, and content negotiation to a Hugo site on AWS. That article was about the producer side: how to serve content that agents can consume efficiently.

But I left a question unanswered: what does it look like from the agent’s perspective?

In this article, I’m building two agents. Same task, same website, same model. The only difference: one reads the web the old way, the other uses the infrastructure I just built. The code is written with the Strands Agents SDK [2], an open-source framework from AWS for building AI agents in Python.

The Test That Failed

Last weekend, I pointed an AI agent at my own blog and asked it a simple question about an article I’d just published — my hands-on experiment with self-reflection on Amazon Bedrock [12]: “What scored 3/15 and why?”

The agent received 29,099 bytes of HTML. After stripping navigation, CSS, scripts, headers, and footers, only about 4,600 characters of actual content remained — 69% of the response was noise. The agent consumed 6,083 input tokens, then gave a confused answer about “personal growth.” It couldn’t find the article content buried in the markup.

The Moment I Almost Gave Up

A few weeks ago, I spent 45 minutes teaching my AI agent how to prepare customer meetings. Pulling context from Slack, checking the CRM, looking up LinkedIn profiles, assembling a briefing document. I could have done it myself in 20 minutes.

The next morning, the agent prepared three meetings in 12 minutes. By the end of the week, it had prepared every meeting for five days — while I was still drinking my coffee.

Technology Evolution Doesn’t Move in a Straight Line—It Spirals

The Proud Ops Colleague

Years ago, an Ops colleague proudly showed me something new. ClusterSSH—cssh [1]. A tool that opens multiple terminals to multiple machines—at the same time. You type once, it executes everywhere.

Back then, machines still had names. Ops folks knew their history, their specs, their quirks. They could tell you which server had been acting up last Thursday and what firmware it was running. And cssh? It let them follow the runbook consistently across every node. No more SSH-ing into machines one by one, hoping you didn’t forget a step on node 7.

🎯 “How do we pick the RIGHT AI agent use case?

This is the question I hear most from customers exploring agentic AI.

Here’s the mechanism I run through together with the customer:

The 4-Quadrant Evaluation

When a customer brings me 5-10 agent ideas, we structure each one across four dimensions:

📊 Business Value & Strategic Fit → What pain does it solve? For whom? How often? → Can we quantify the impact? (Revenue, cost, time, quality) → Which KPI moves if this works for 6 months?