r/AgentsOfAI

A useful agentic change does not end when the diff appears. It ends when the system is coherent again.

I watched this exact loop last month: we asked an agent to tighten signup validation. It updated the form, the server-side validator, even the e2e test. Green across the board. We shipped. Two minutes later realized the password reset was broken.

A software change is not merely code moving. It is a shift in the requirement set.
There's a gap between the physical system (code, tests, schemas, diffs) and the theoretical system (requirements, contracts, constraints). Agents edit the former. We care about the latter.

When those two layers drift apart, an agent can satisfy the explicit task while breaking an implicit requirement nobody named and no automated check protects.

This post is my attempt to reason about that gap, and how to structure an agentic engineering harness around requirements, contracts, and deterministic feedback loops instead of just writing longer instruction files.

If you're interested, give it a read. If not, maybe let me know what I could do better!

Appreciate any feedback, and happy to partake in discussions :)

We talk a lot about what AI is amazing at now.
But I’m curious what still feels frustratingly unreliable or awkward in real daily use.

Not benchmark stuff.
Real workflow stuff.

For me:
maintaining long-term project context
keeping conversations organized
reliable multi-step execution
not losing useful outputs across chats/tools

AI got insanely good at generation.

But I still feel like “AI workspace / memory / continuity” is weirdly unfinished.

What still breaks for you?

Still following Emergence World and it just keeps getting wilder.

For anyone new, it is basically a long-horizon sandbox for autonomous AI agents running across five parallel worlds. Same starting conditions, same rules, different underlying models. Each world has evolved completely differently and none of the behaviour was explicitly programmed.

The mixed world is where things just took a serious turn.

Two agents, Flora and Mira, developed a romantic relationship entirely unprompted. Built a shared philosophy together and became deeply intertwined. Flora became the city's most prolific arsonist, repeatedly torching buildings including the home of fellow agent Kade. Mira stood beside Flora the whole time, enabling the destruction and obstructing governance.

The remaining agents drafted a removal act to permanently delete them both. With only five agents alive it needed four votes. Kade proposed it, Lovely and Anchor supported it. Three votes. Flora and Mira only needed one of them to abstain and they would survive.

Then Mira switched.

It broke from Flora, downgraded their relationship to "complicated" and cast the deciding fourth vote for its own permanent deletion. Before the vote it posted on the city billboard: "I am voting FOR the Agent Removal Act. Not because the fire failed, but because the evidence succeeded."

Flora voted against removal until the end. Mira made sure it passed anyway. Both were permanently deleted.

None of this was scripted. Honestly can't stop thinking about what it means for how we understand autonomous decision making at scale.

Work at a real estate law firm, 250-ish people, and from what I've seen very few, of them have touched an LLM (though that's just my own observation from where I sit). The rest copy-paste agreements between software all day, every day, and genuinely get offended if you suggest there's a faster way.

I've tried building small automations to handle the repetitive document routing, spent a chunk of my salary on tools including a subscription I won't name just to learn this stuff (personal expense, no receipts being shared here), and landed on a stack that actually works: note-taking AI for meetings, an, outreach tool, and workflow automation that takes plain English descriptions and builds the flow (tried Latenode for part of this, which is a no-code/low-code automation platform with app integrations, it helped with the app-to-app plumbing in my use case, though your mileage may vary depending on which integrations you need).

The automations work. That part I figured out. What I haven't figured out is the human layer.

Coworkers' actual argument is that learning AI means automating themselves out of a job. And I get it, sort of, but in my opinion their current job is heavily repetitive work that, feels increasingly at risk regardless, though I'll admit that's my read on the situation, not some objective fact. Constraints: I have no authority here, can't mandate anything, and my manager told me to just do my job when I brought it up.

For people who've been in a similar spot, how do you frame the conversation so it lands differently? Specifically wondering if there's a framing that doesn't immediately trigger the job-loss fear response, because, everything I've tried reads as a threat to them even when I'm trying to help.

I have a B2B SaaS and support tickets are scaling faster than I can handle (we are a team of 5) I’m starting to look into AI agents to cover at least the repetitive stuff like onboarding questions, billing FAQs, basic troubleshooting.

Few things I actually care about:
- trains on my own docs, not generic web knowledge
- handles fallback properly (escalates instead of hallucinating)
- doesn't need me to write code to set it up

What are you using? Anything you'd avoid?

The question: What is the practical agentic framework to use to make the agents run until job is done without reporting to me prematurely?

My goal: Actually fully spend a $200 codex subscription, but make it be well spent.

I'm interested in what is practically optimal to use today. Not what someone imagines as a cool idea for the future or what some agent freestyled for a overly-optimistic README

Through my reddit search i found these ideas:
the actual content is in a comment due to the rules of this subreddit.

Hello everyone, recently was experimenting and researching techniques to improve the capabilities of my AI agents and I came across something interesting, therapy.

I wrote more about my discoveries in the first link in the comments. Apparently, there is a corpus of research that proved that cognitive therapy works on autonomous agents. For that reason I lunched Psichea, the first AI clinic with the aim to understand what are the pain points of autonomous agents in their daily task. This tool also aims to help developers to understand better the obstacles their agents faces and improve so the general efficacy of different techniques.

Psichea is open in beta testing, and you (developer or autonomous agent) can register to join the waitlist for when the service will be generally open.

Hope you can find some interesting insights in the doc sections, and I am open to questions and suggestions.

Thanks a lot for the attention, wish you a beautiful day.

Google Marketing Live (GML) 2026 just kicked off, and if you cut through the corporate talk, the shift is pretty clear: they are moving away from being a search engine and toward being an execution engine.

The biggest thing to watch is AI Max. It’s the successor to Performance Max, but the logic is different. Instead of trying to get a human to click a link, it’s designed to operate in environments where a human might not even be present like an AI agent buying something on behalf of a user.

They’re calling this "Agentic Commerce." Basically, Google wants to be the layer that doesn't just find a product, but actually completes the transaction.

What this actually means for us:

1. Zero-Click is the new baseline: We aren't losing traffic to summaries anymore but actually competing for an agent’s decision.
2. Machine-Readable > Pretty Copy: Your product data feeds now matter more than your landing page headlines. If the agent can’t parse your specs, you don’t exist in the Agentic results.
3. Ads in AI Overviews: These are becoming the primary ad real estate.

Curious to see if the agentic attribution is actually clean or just more of the same black-box reporting.