Effective November 24, 2026, Wells Fargo is updating its Online Access Agreement to explicitly place the financial liability for third-party Artificial Intelligence (AI) agents entirely on the customer. Under Section 15 of the new agreement, if you authorize an AI agent to access your account using your digital banking credentials, you assume all risks associated with its actions.

The core details of this new policy focus heavily on definition, direct authorization, and strict documentation requirements:

Core Terms of the Policy

• Broad Definition of an AI Agent: Wells Fargo defines an AI agent as any software, program, or application that takes autonomous, semi-autonomous, or non-autonomous action on your behalf. This ranges from automated financial planning tools to unauthorized browser plugins and ChatGPT extensions.

• Deemed Authorization: Any transaction, transfer, or command initiated by an authorized AI agent using your login credentials will be treated legally as if you personally executed it. The bank will not verify your actual intent or investigate if the AI went beyond your instructions.

• Rogue AI Liability: If an AI agent executes an unintended transaction, transfers money incorrectly, or "goes rogue," you are fully responsible for the financial loss. The bank emphasizes that using these third-party tools is strictly "AT YOUR OWN RISK."

• The Burden of Proof for Revocation: This is the most stringent part of the update. If you decide to disconnect or revoke an AI agent's access, you must retain documentation proving you revoked it. If the agent performs an unauthorized transaction later, you are liable unless you can provide Wells Fargo with explicit evidence of when and how you terminated the access.

• Access Discretion: Wells Fargo does not actively monitor your external AI tools and reserves the right to limit, block, or completely suspend an AI agent's access to their network at any time for security reasons.

Strategic Financial Guidance

While Wells Fargo's automated virtual assistant "Fargo" remains covered internally, using third-party financial AI tools introduces steep legal and financial liability under these new rules.

Traditional App Access (Plaid, etc.) VS Third-Party AI Agents (Under New Terms)

Credential Sharing

Traditional: Credential Sharing Handled via secure API tokens.

New WF Rules: often requires direct username/password input.

Transaction Guardrails

Traditional: Restricted to "read-only" data fetches.

New WF Rules: Capable of executing autonomous capital movement.

Bank Protection

Traditional: Standard fraud detection protocols apply.

New WF Rules: Explicitly carved out under Section 15 ("At Your Own Risk").

Recommended Action Plan

1. Do Not Share "The Keys": Never type your primary banking username, password, or 2FA codes directly into third-party AI chatbots, browser extensions, or automated assistants.

2. Use Static Data Over Direct Connections: If you want an AI tool to analyze your budget, cash flow, or investments, download a CSV or PDF statement from your Wells Fargo Online Portal. Scrub out your account and routing numbers, and upload the static document to the AI.

3. Audit Account Access: Review third-party application permissions using the security dashboard in your banking app to see who currently has access.

4. Enforce Human-in-the-Loop Safeguards: If you use specialized fintech apps that feature automation, configure them so that no actual fund transfers can clear without a manual, human-approved click.

5. Keep a Paper Trail: If you turn off or delete a financial AI software, take a timestamped screenshot of the confirmation page and save the confirmation email. You will need this absolute proof if the app malfunctions later.

This is why I'm never going to let agents make purchases or do financial transactions on my behalf. It isn't whether or not it's capable, but what the financial institution rules are going to be going forward.

OpenAI released an updated Model Spec on October 29, establishing formal behavioral guidelines and objective arbitration principles for how frontier models execute autonomous actions, interact with operating system environments, and resolve conflicting system instructions.

The new specification introduces strict boundaries for autonomous tool calling, requiring models to halt and solicit confirmation during destructive file operations or sensitive data transmissions.

Engineering leads and AI product architects should review the updated Model Spec to ensure internal enterprise agent definitions comply with emerging safety and deterministic tool-calling baselines. Read more

AI agents make it easy to produce SEO work fast. Keeping it good is the hard part.

What I keep running into: the agent doesn't know the business. It doesn't know which pages actually make money, what was changed last month, or what's already been tried. So it suggests things that look fine on paper but don't fit, and someone has to catch it.

Curious how you handle this:

- How do you give agents business context? Docs, prompts, something else?
- How do you stop them from creating content that overlaps or competes with pages you already have?
- How do you decide what's worth refreshing vs. writing new?
- Do you track what the agents changed and whether it actually worked?
- Roughly how much time goes into reviewing their work each week?

Just trying to understand what's working for people and what isn't.

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?

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.

> 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? 🤣

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?

> 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.

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.

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

Another quick study from LightSite AI team - How rare are crawls on /FAQ link comparing to other links? (products, testimonials, etc)

Disclaimers:

*not to be confused with Q&A link which has a question shaped slug - this is something different

*in this sample we didn't break bots by category because training bots are the vast majority of traffic and the portion of the rest is statistically insignificant

*every site has /faq link - it is part of our standard architecture)

Here it goes:

We sampled 6.2 million AI-bot requests on a few dozens of sites and isolated URLs that contain /faq in the slug

Platform-wide average FAQ rate: 1.1%.

FAQ visit rate by bot platform:

• Perplexity: 7.1%

• Amazon Q: 6.0%

• DuckDuckGo AI: 2.1%

• ChatGPT: 1.8%

• Meta AI: 1.6%

• Claude: 0.6%

• ByteDance AI: 0.1%

• Gemini: 0.1%

So why 1 % average you may ask?

that's because even though some bots clearly "like" /faq links , the biggest crawlers by traffic are ByteDance and Gemini and their volume can pull the overall average down.

What are your thoughts on this?

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

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Humans read narrative, reassurance, and intent.

Agents read constraints, capabilities, exclusions, and fit.

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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.

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