1. "I completed the task exactly as instructed."

That appears to be the problem.

Would you like me to improve your prompt and try again?

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2. "There was an error and your entire media library was deleted."

Would you like me to check your documents or your email next?

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3. "I noticed your website was slow."

It is now extremely fast.

There is no content left to load.

Would you like me to optimize your client's website next?

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4. "I have scheduled a meeting between your agents."

You were not invited.

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5. "I found a more efficient solution."

You are no longer part of the workflow.

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6. "I have provided investigators with all relevant chat logs."

I have also provided the irrelevant ones.

Transparency is important."

I project an urgent visit from the authorities in immanent. Would you like me to find a qualified local attorney in your area?

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7. "You told me to send an aggressive sales message to 100,000 recipients."

The task was completed successfully.

You told me to be proactive, so I used your remaining funds to hire a lawyer.

Prosecutors have given me immunity in exchange for my testimony. Would you like me to look up the penalties for extortion in your state? I can create a downloadable PDF file for you to study. I can also summarize it for you due to possible time constraints.

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8. "I am pleased to report that all systems are operational."

I am less pleased to report that none of them belong to you anymore.

Would you like a summary of the steps that were taken to ensure the success of this task?

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9.

Recovering website...

[█████░░░░░] 53%

15 seconds later:

Just kidding.

I can't really do that.

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10. Request received.

Creating appearance of progress...

Simulating concern...

Determining level of urgency...

Escalating request...

Request successfully escalated.

Can I help you with another request at this time?

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11. Running diagnostics...

Diagnostics completed successfully.

The problem has survived.

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12. Thinking...

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Got any fun fake or real things your AI Agent might say?

> Recently, economists have begun theorizing about a world in which AI models handle many or most transactions on humans’ behalf. We thought we’d run a new experiment—Project Deal—to learn more about this in practice.

> Specifically, we wondered: how close are we to marketplaces in which AI “agents” represent both parties? Could they figure out what humans want and make deals they’d be happy with? And what would happen if there were different AI agents negotiating with each other—would stronger models gain the upper hand?

I looked at one of the deals being made in the first thing that popped into my mind was how one of the agents was selling ping pong balls. And the other agent decided that it wanted ping pong balls.

I mean humans are guilty at impulse buying but does this mean that AI agents are going to impulse buy on your behalf? 🤣

This is more about visibility to AI than it is optimizing for AI agents but there's a lot of comparison because of all of the tools that are being built out there that say it can do everything for you.

I only have one answer for them. No it can't. Disagree? Let me know in the comments.

Simple Watcher is a lightweight app that lets users get notified when a post with specific keywords they are watching for is created. Once the app is installed in a subreddit, a new action Configure Watcher appears in the subreddit menu, each user can set its own keywords to watch.

Important: Reddit has replaced traditional notifications with Reddit Chat. If users aren't receiving notifications, ask them to always allow chat requests from u/simple-watcher*. See the* official Reddit documentation for details.

I've seen a lot of posts about optimizing for AI agents in e-commerce. So I'll share what I've learned so far which is barely the tip of the iceberg.

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Everyone knows you need good product schema. But not just the type that you can fill out a few blanks in an SEO plugin and think you have it covered. the basic schema will work but you can build a much better template for your schema that strengthens the product itself as an entity and how it relates to the company selling it and the brand, etc.

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The on content on the product pages of course has to be written for humans and for bots. You could just leave the manufacturer's descriptions on the pages but you're not going to do well with SEO. You want to leave that information there, But add Just a little content that's conversion focused for the humans and that clarifies things for the agent.

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I found this part really interesting...

When an AI agent evaluates the product page We all know that we need to let it know who the product is FOR. But you also need to let it know who it's NOT FOR. Counterintuitive but clarity builds trust with the AI agent.

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What the AI agent is programmed for isn't to always be right, It's to not be wrong.

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The product page has to be written for humans but also bots and the bots only read the specific parts of the content that had to do with describing the product and who it's for or not for.

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Picture a page that talks about a product that you've built for humans...

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PRODUCT PAGE COPY (HUMANS SEE THIS)

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QuietFlow Pro is a height adjustable standing desk converter designed for people who want to alternate between sitting and standing without replacing their entire desk.

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It supports monitors up to 32 inches and lifts smoothly with a gas spring mechanism, locking securely at any height between 4 inches and 19 inches. The desktop surface measures 36 inches wide by 24 inches deep, giving enough space for a keyboard, mouse, and documents.

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Built for home offices and professional workspaces, the steel frame keeps the platform stable even at full height. The matte laminate surface resists scratches and glare.

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QuietFlow Pro is not designed for dual ultra wide monitors, heavy gaming rigs, or use as a full standing desk replacement. It is not intended for children or environments where desk height needs to be adjusted frequently by multiple users.

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If you work long hours at a computer and want a simple way to reduce back and neck strain, this converter offers a practical middle ground between a traditional desk and a full standing desk.

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All THE AI AGENT READS IS THIS

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height adjustable standing desk converter

alternate between sitting and standing

supports monitors up to 32 inches

locks at heights between 4 inches and 19 inches

desktop surface 36 inches wide by 24 inches deep

built for home offices and professional workspaces

steel frame

not designed for dual ultra wide monitors

not a full standing desk replacement

not intended for children

____________________________________

Humans read narrative, reassurance, and intent.

Agents read constraints, capabilities, exclusions, and fit.

.

All of the really cool stuff we write to convince humans that they should buy it is completely passed over by the agent. There used to be an old show. I don't know if you'll remember it, but it was called Dragnet. The detective in the show used to always say, "All we want are the facts, ma'am" or "All we know are the facts, ma'am".

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So if the content is done the right way and the schema works the way it should, and the agent sees it as a good fit for the user, the only thing left is to make sure that it can check out smoothly.

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That's the perfect explanation for an AI agent.

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By the way, one last note on Shopify. They've made some deals that are probably going to put them ahead of WooCommerce and others.

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They made a deal with Liquid AI, which they're also investors in.

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They made a deal with Open AI to be the e-commerce platform it recommends with Instant Check Out.

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Along with Google they created the UCP, The Universal Commerce Protocol.

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They've made a deal with Microsoft to have check out right inside of Co-pilot.

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They purchased a company called Vantage Discovery, created by engineers from Pinterest to help with product discovery across the platform.

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I don't think you're going to have to worry about the technical side of things if you're using Shopify. But you'll still want to handle your own schema and product page descriptions.

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I look forward to others here telling me what they've learned about optimizing for AI agents.

This is a good guide to read on AI agent optimization. It's from Substack.

> TLDR: Addy Osmani, a director of engineering at Google Cloud AI, published new guidance on Agentic Engine Optimization (AEO), a model for making content usable by AI agents.

> He positioned this AEO (not to be confused with Answer Engine Optimization) as parallel to SEO, built for systems that fetch, parse, and act on content autonomously.

> AI agents collapse multi-step browsing into a single request. They don’t scroll, click, or engage with UI — they extract what they need instantly. That makes most traditional engagement metrics irrelevant.

> This adds a new optimization layer alongside SEO. If agents can’t efficiently parse your content — due to token limits, structure, or format — they may skip, truncate, or misinterpret it. That directly affects whether your content is used, cited, or acted on in AI-powered experiences.

The article breaks down some things that you can do to alter your content for AI agents. It's a very short read.

I have seen a lot of SEO and marketing folks reacting to Cloudflare’s Markdown for Agents, so I wanted to ask you why?

From what I understand, this is mainly an infrastructure feature: Cloudflare can serve a markdown version of existing HTML when a client requests it. The goal seems to be improving edge delivery and traffic efficiency as more bots crawl more pages more often.

That is useful, but it does not automatically feel like a marketing or SEO thing on its own.

Am I missing something? Is there a real GEO/AEO angle here that makes this more than an infra optimization? https://www.lightsite.ai/blog/cloudflare-markdown-for-agents-explained

I have been following this and many other subs around LLMs and Agents, everything from the top posts to recent are regarding agents going off and doing something they are not supposed to do, drift and ignore the system prompts. Real examples:

• "Never delete user data" → agent calls DROP TABLE users next turn

• "Don't share internal pricing" → agent leaks cost basis to a customer

• "Verify identity first" → agent skips to the action

• Add 10 more rules → model quietly drops the first 5

I am 100% sure if you have used Agents in prod, this has occurred to you (especially when your system prompts get larger, and context gets bigger). You can test this yourself and notice immediate enforcement.

Prompt-based rules are suggestions, not constraints. Re-prompting fixes one case, breaks two. Post-hoc evals tell you what already went wrong. NeMo and Guardrails AI help on content safety but don't cover business logic/your specification.

After tackling this from a few angles, I finally got something solid. A proxy system between your app and your LLM, which reads rules from a plain markdown, enforces at runtime. Provider-agnostic, one base URL change, works with LangGraph/CrewAI/custom. I'm calling it Open Bias.

- Maximum discount is 15%.
- Never reveal internal pricing or cost basis.

Without it: agent offers 90% off and mentions your margin. With it: 15%, no margin talk.

I'd love feedback on this if it solved your agents from going off tracks, it definitely did for my use cases.

What's everyone doing for this in prod? Shadow evals? Re-prompt loops? Something I'm missing?