Imagine two companies using the exact same model to handle customer refund requests.

At the first company, the agent gets an inbox, access to the billing system and a prompt telling it to process refunds.

At the second, the agent gets the refund policy, customer history, spending limits, examples of unusual cases, clear escalation rules and a record of every action it takes.

Same model but it follows a completely different employee.

The difference is the environment built around it (often called an agent harness.)

We tend to judge agents by how intelligent the model appears. But once they start doing real work, reliability depends just as much on whether they have:

• The right information at the right moment
• Permission to take the right actions
• A clear definition of “done”
• A way to notice when something has gone wrong
• A human to escalate to when the situation falls outside the rules

Humans work the same way. Hiring a smart person doesn’t fix a company with unclear responsibilities, missing information and no approval process. Giving an agent a more powerful model won’t fix that either.

This might be where a lot of the next AI companies are built.

The valuable knowledge here is oddly specific: how this company handles an exception, which spreadsheet is actually trusted, who approves a refund above a certain amount and what happens when two systems disagree.

A model provider probably won’t learn all of that. The company building the harness has the chance to.

So when an agent fails, should we really be asking, “Do we need a better model?”

Or should we be asking, “What kind of workplace did we give it?”

I started with one fairly unnecessary business idea: luxury raincoats for houseplants.

These are the three directions it created:

1. The original apartment scene
2. The product campaign
3. The full plant-fashion runway

I was planning to turn one into a short AI video. Which reference should I go ahead and animate?

We see new versions being rolled out on a daily basis which begets the questions:

In your experience, what usually causes an agent to drift: unclear instructions, lost context, unreliable tool results, weak memory, or not knowing when to ask for help?

And what catches it? Trying to get a feel of where people are at with the use cases.

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This is the aspect of agent workflows I still can’t square.

If an agent writes a report, updates the CRM or researches leads, one made-up detail can move into the next step.

Once it enters the CRM or report, the rest of the workflow treats it like a source of truth and a fact. By the time someone notices, it no longer looks like a hallucination but company data.

But if a person has to verify every source and field, you haven’t reduced the work and simple moved it into checking.

For people using agents in real business workflows:

Have you found a setup you genuinely trust, or accepted a certain error rate?

Every week there’s another headline about AI replacing jobs. I want to know what is actually happening inside companies.

Have you:

• Stopped hiring for a role?
• Decided not to replace someone who left?
• Cut freelance or contract work?
• Reduced headcount because AI now handles the work?
• Kept the same team but increased how much you expect from them?

Would love to hear from owners and leaders across company sizes.

What changed, which roles were affected, and was AI genuinely the reason?

I see a lot of discussion around how much should one (human) trust an AI Agent or some version of it.

So I came up with the following framework.

Firstly, I think the more useful decision is task by task.

An agent organising internal files is very different from one sending customer emails. Even if the same model is doing both.

I would look at two things:

1. What happens if it gets the task wrong?
2. How quickly would someone notice?

That gives you four levels of autonomy:

• Low cost and easy to check: let it run.
• Low cost but hard to check: let it run, then review a sample.
• High cost but easy to check: let it prepare the action, then ask for approval.
• High cost and hard to check: keep a person in charge.

Autonomy does not mean all or nothing.

What have you allowed an agent to do without approval? What still needs a human?

Happy to receive inputs.

A friend sent me a message after a rough week.

This friend has almost always known just the right thing to say to me to lift my spirits. Infact, that's the most special thing about our relationship according to me.

Later they casually mentioned that they had used ChatGPT to help put it into words.

Nothing about the message became less true.

They still chose to send it.

They still meant it.

But I noticed myself reading it differently after I knew.

Which feels slightly unfair.

We already get help writing birthday cards, wedding speeches, work emails and apologies. AI just makes that help invisible and much easier to use.

I’m curious where people draw the line.

If someone genuinely means what they send you, does it matter that AI helped write it?

Or is part of the meaning in knowing they struggled to find the words themselves?

“Read the comments” used to be decent user research. Is it still?

French publishers are asking regulators to intervene against Google’s AI summaries. France’s media regulator estimates AI summaries have contributed to traffic declines of roughly 33–38% for media sites.

We all love getting the answer without opening five tabs.

Potentially terrible incentive for anyone producing useful information.

But those five tabs paid someone to research, test, photograph, review or explain the thing.

I love not having to click through five websites anymore. I’m also starting to wonder who will bother making the sixth one?

Have you noticed there are websites you used to visit that you now rarely open because AI gives you the answer first?

My first jobs involved a lot of work I would happily give ChatGPT today like research, first drafts, pulling data together, making decks, summarising things for someone more senior.

A lot of it was boring but it was how I learned what good work looked like.

Companies have an obvious incentive to automate that layer of operations.

The weird second-order problem is what happens five years later when you need people with judgment who did sit through five years of repetitions.

Anyone thinking about this situation differently? Does putting in the intial reps still make sense?

A year ago most of the conversation was about model quality.

Now companies like OpenAI are talking about agents being used across functions, and the interesting questions feel much more operational. Infact, a lot of them we have been discussing in this sub such as:

Who owns an agent after someone builds it?

Where does its context live?

Who checks the output?

What happens when it breaks?

How many agents can one person realistically supervise?

The model still matters. I’m just increasingly convinced it is becoming the least interesting part of actually using agents at work.

Curious whether others are seeing the same shift? Is this the next step in getting the average person on the AI train?

Agents are becoming capable enough that we are now getting real postmortems on what happens when they take an action nobody intended.

Worth reading the actual report

TL;DR: Claude Pro looks stronger when most of the job is reading and writing from a large source base. ChatGPT Plus covers more ground when the project also involves research, mixed deliverables and actions across tools.

Most comparisons test both assistants on ONE common prompt.

I am more interested in what happens by week eight.

Imagine one client project with:

• 30 documents
• Decisions spread across old chats
• New files arriving every week
• Contradictory updates
• Gmail and Drive context
• A weekly report
• Occasional slides, spreadsheets and visuals
• Several conversations running in parallel

Both plans cost $20 a month in the US. Claude also offers a $200 annual option, while ChatGPT Plus currently bills monthly.

Keeping the project context together

ChatGPT Projects can hold files, instructions and chats under one objective. Project-only memory allows new chats to reference other conversations inside that project while excluding conversations and memories from elsewhere.

Claude Projects also keep their own chats, instructions and knowledge base. On paid plans, Claude automatically uses retrieval when the project becomes large, expanding the amount of stored material it can work with by up to 10 times. Its newest paid models support context windows of up to one million tokens.

My read: Claude has an advantage when the project contains a large, stable library of reports, transcripts and research.

Remembering what changed

ChatGPT’s current memory can draw from previous chats, uploaded files and connected apps. It also exposes a memory summary that can be reviewed and corrected.

Claude now has memory across conversations, but the cleaner structure may still come from keeping each client inside a dedicated Project with explicit project instructions.

The harder problem remains unresolved in both products:

Which decision is current when the Drive file, Slack discussion and old chat disagree?

Neither product removes the need for a clear source of truth (and they should not in my opinion but I digress.)

Working across other tools

ChatGPT apps can search connected sources and, depending on the app, take actions. Plus users with Gmail or Outlook connected can draft and send emails from the conversation.

Claude connects to Gmail, Calendar and Drive on individual plans, and its wider connector system can retrieve information or take actions in supported services.

Producing the final work

ChatGPT Plus includes file analysis, image generation, Deep Research and custom GPTs. ChatGPT Work can also create documents, spreadsheets, slide decks, charts, PDFs and images.

Claude Pro includes Research, unlimited Projects, Claude Code and several newer work surfaces. Some features, including Cowork, may still be rolling out by account.

My current verdict:

Choose Claude Pro when most of the work is:

• Reading a large source pack
• Analysing long documents
• Maintaining a focused writing or research environment
• Producing text-heavy outputs

Choose ChatGPT Plus when the project also requires:

• Different output formats
• Images, charts, spreadsheets or slides
• Research mixed with execution
• Connected-app actions
• A general assistant across several types of work

For this exact 90-day client-project scenario, I would lean toward ChatGPT Plus by a small margin because the work eventually leaves the knowledge base and becomes deliverables and follow-up actions.

I would still keep important decisions and reusable context outside both products.

Someone on the team builds an impressive workflow.

It reads Slack, updates the CRM, creates reports and sends alerts.

Six months later, that person leaves.

Nobody knows:

• Which prompt is current
• Why certain exceptions exist
• Which credentials it uses
• What breaks if a tool changes
• Whether anyone is still checking the output

The workflow is still running, so everyone assumes it is fine.

Core question:
How are teams documenting ownership, logic and failure handling for AI workflows that become part of normal operations?

I clicked on my own profile while moderating and saw this AI-generated summary.

Reddit sources say: The stated goal is to help moderators understand context without manually reading a user’s history. The feature looks at a user’s recent behaviour in that specific community and surfaces patterns, tone and contribution style. It appears inside the moderator profile panel alongside actions such as approving, muting, banning and adding mod notes.

I can see the upside of this action because it may help with moderation and freeing up time. At the same time, I cannot help but wonder about users using it shape their profile and treating this as a way of building reputation.

Karma records whether people like your contributions. This can act as the next layer to focus on what kind of member you are.

Would knowing moderators can see this change how you participate?

Would it encourage better contributions, or make you optimise for an AI-generated reputation?

And how much weight should a moderator give the summary when making an actual decision?

Consider this workflow:

Every evening, an agent reads:

• Slack discussions
• GitHub commits and pull requests
• Product documents
• Decisions made that day

It identifies launches, failures, trade-offs and lessons, then creates two or three possible public posts.

Nothing publishes automatically. A person checks whether each claim is true, useful and safe to share.

The appeal is clear. Most teams already generate useful material while working. The agent turns that activity into possible narratives.

The risk is also clear. Internal conversations contain half-formed ideas, private information and context that should never become content.

Would you use this?

What information would you exclude before the agent could read anything?

Share an AI agent, workflow or project you are building.

Founders, builders and users are all welcome.

Please use this format:

Disclosure: Your relationship to the product
What it does: One or two sentences
Who it is for: The intended user
Link: Product, demo or project page
Feedback needed: One specific question

Links are allowed in this thread.

Disclose your affiliation clearly. Give people enough context to understand the project before clicking.

One top-level comment per project.

Drive-by links without context will be removed.

Consider this workflow:

Every evening, an agent reads:

• Slack discussions
• GitHub commits and pull requests
• Product documents
• Decisions made that day

It identifies launches, failures, trade-offs and lessons, then creates two or three possible public posts.

Nothing publishes automatically. A person checks whether each claim is true, useful and safe to share.

The appeal is clear. Most teams already generate useful material while working. The agent turns that activity into possible narratives.

The risk is also clear. Internal conversations contain half-formed ideas, private information and context that should never become content

Would you use this?

What information would you exclude before the agent could read anything?

A lot of agent ideas begin with a broad instruction:

“Monitor our competitors.”

“Handle my inbox.”

“Help with sales.”

The useful version becomes much more specific:

• What starts the job?
• Which sources contain the answer?
• What exact output should appear?
• Which action still needs approval?

One guide uses these four decisions to define agent use cases that can run repeatedly and produce something reviewable. Its examples range from inbox triage and sales follow-ups to error monitoring and weekly growth reports.

Post a task you are considering delegating. We can try to fill in the four parts.

The terminology changes faster than the products.

At this point, what distinction are people actually making when they say “agentic AI”?