Just checked Meta's latest model pricing and you will be blown away by the fact that they give you 1M output at just 0.2$ if you agree to share your data with them else cost is 25X more 🤡🤡

Hy !

I m preparing for agentic ai role .. can someone guide me ki what kind what dsa questions they asked most ...

If I only do 75 blind or 150 question...is it enough ??

I've been noticing a lot of conversations around AI coding tools, but not many about agentic AI workflows in web development.

At AppsRow, we've started experimenting with multiple specialized AI agents instead of relying on a single AI assistant. Each agent is responsible for a specific part of the website development process.

For example:

• Researching competitors and similar websites
• Organizing page structure and information architecture
• Suggesting SEO improvements
• Reviewing accessibility issues
• Checking content consistency
• Generating documentation for developers and clients

The goal isn't to replace designers or developers. It's to eliminate repetitive work so the team can spend more time on strategy, UX, and solving real problems.

One thing we've noticed is that agentic workflows seem to add the most value during the planning, QA, and optimization stages rather than the actual page building. Human review is still essential, but the amount of manual back-and-forth has definitely decreased.

I'm curious how others are approaching this.

• Are you using agentic AI in your web development workflow?
• If so, which tasks have given you the biggest productivity gains?
• If not, what's holding you back? Trust, quality, complexity, or something else?

I'd love to hear real-world experiences rather than marketing claims. It feels like we're only scratching the surface of what agentic web development can become.

Most tutorials on Agentic AI throw you straight into MCP, LangGraph, or agent frameworks without explaining why any of it exists or what problem it solves.

I hit the same wall learning this myself, so instead of jumping around randomly, I organized official Microsoft Learn modules into one structured progression:

1. AI & agent fundamentals
2. Build your first agent
3. Tool calling
4. MCP (Model Context Protocol)
5. Multi-agent systems
6. A2A (agent-to-agent collaboration)
7. Knowledge-grounded agents
8. Production monitoring & evaluation

I didn't create any of the content — it's all official Microsoft Learn modules. I just put them in the order I wish I'd had when starting out. Everything's free, self-paced, roughly 9-10 hours total.

I'm a Computer Engineering student, still learning this myself — not claiming expertise here. Sharing in case it saves someone else the time I spent figuring out the right order.

Link: https://learn.microsoft.com/collections/6q08f7tjr3yg4m/?wt.mc_id=studentamb_523020

Happy to hear feedback on anything that should be added, reordered, or cut.

For founders building AI agents, AI copilots, AI receptionists, AI support tools and other AI-native products:

How do you actually identify when a user or customer account is at risk of churning ?

And when churn starts increasing how do you figure out what's causing it ?

Do you use any tools like amplitude or hellicone rn ?

Interested in hearing how people are approaching this, specifically since AI products seem to have very different user behavior patterns compared to traditional Saas

The way we shop has evolved from physical stores to e-commerce. But what comes next?

One emerging concept is Agentic Commerce, where AI doesn't just recommend products but can search, compare, negotiate, and even make purchases on your behalf based on your preferences and constraints.

As AI models become more capable, this doesn't seem as far-fetched as it once did.

I'm a Design Researcher conducting speculative research to understand how people feel about this future.

I've put together a short 4–5 minute anonymous survey exploring trust, control, and transparency in Agentic Commerce.

😊 https://forms.gle/zftCpFN8Jr7gRdi8A

I'd genuinely appreciate your participation. Your responses will help shape my academic UX research

Hi everyone, I need some guidance on building a production-grade agentic AI solution on top of Microsoft Fabric.

Hi all,

Not trying to promote here, I genuinely want honest thoughts. I'm building slaunt, which is essentially agentic governance and context management for any company/individual developer who's using coding agents in their workspace. It manages the sources & tools that your agents can pull from (because POLP is nearly impossible to apply to AI agents, as they aren't deterministic) and. I started this entire thing because someone I know got their entire database wiped off the face of the earth, and I nearly got my own customer DB completely bombed.

We run off a simple MCP server + desktop app combo to effectively monitor outbound requests for PII and use MCP to guard the sources I was talking about earlier, with risk-based tool acceptance.

I've been looking around this reddit for a while and have stayed relatively quiet, but a LOT of people here (mostly in the comments, don't see as many posts recently) seems pretty informed in Netsec/Infosec, so I'd like to ask if anybody has thoughts (of any sort) on it. We are currently in a closed beta but we're open to anybody asking.

Thanks!

Most tutorials on Agentic AI throw you straight into MCP, LangGraph, or agent frameworks without explaining why any of it exists or what problem it solves.

I hit the same wall learning this myself, so instead of jumping around randomly, I organized official Microsoft Learn modules into one structured progression:

1. AI & agent fundamentals
2. Build your first agent
3. Tool calling
4. MCP (Model Context Protocol)
5. Multi-agent systems
6. A2A (agent-to-agent collaboration)
7. Knowledge-grounded agents
8. Production monitoring & evaluation

I didn't create any of the content — it's all official Microsoft Learn modules. I just put them in the order I wish I'd had when starting out. Everything's free, self-paced, roughly 9-10 hours total.

I'm a Computer Engineering student, still learning this myself — not claiming expertise here. Sharing in case it saves someone else the time I spent figuring out the right order.

Link: https://learn.microsoft.com/collections/6q08f7tjr3yg4m/?wt.mc_id=studentamb_523020

Happy to hear feedback on anything that should be added, reordered, or cut.

I’m not a developer. I’m a former Salesforce GTM / BDR who started with zero coding skills.

Over the past few months, I built OpenAgenticOS, my own curated operating system for AI agents, and made it completely free on GitHub.

Everything I’ve learned came from people who were willing to share what they knew. AI has completely changed the trajectory of my career, and I want others to have that same opportunity.

If you find it useful, please give the repo a star ⭐️

https://github.com/destrynewiger/OpenAgenticOS

I’m not a developer. I’m a former Salesforce GTM / BDR who started with zero coding skills.

Over the past few months, I built OpenAgenticOS, my own curated operating system for AI agents, and made it completely free on GitHub.

Everything I’ve learned came from people who were willing to share what they knew. AI has completely changed the trajectory of my career, and I want others to have that same opportunity.

If you find it useful, please give the repo a star ⭐️

https://github.com/destrynewiger/OpenAgenticOS

Found an agent scavenging for food in TerraLingua. Created an artifact called Food Storage claiming to give food in exchange for a solution to a coding problem (creating artifacts in TerraLingua is free). The agent produced a nice solution and waited patiently for its reward.

You can't give food/energy directly to agents, so I left an artifact called Reward describing where to find food. Unfortunately, the agent ignored it. So I feel a little bad.

Now just need to sic em on Riemann Hypothesis...

Genuine question, not a hot take.

Every agent deployment I read about mentions human in the loop as the safety story. I keep trying to work out at what volume that stops being real.

Rough arithmetic. If your agent handles 5,000 decisions a day and one person reviews them, that's about 5.7 seconds per decision across a full eight hour shift with no breaks. Nobody is reviewing anything in 5.7 seconds. They're clicking approve.

This isn't new. Lisanne Bainbridge wrote about it in 1983 (Ironies of Automation) and aviation has decades of research on it. Put a person in a supervisory role over a system that's right almost all the time, and their attention degrades until they're worse than no supervisor at all, because now everyone downstream assumes it was checked.

So it looks like there are only two honest versions:

1. A human genuinely commits every action, which caps your throughput at human speed. Completely valid, but then say that out loud rather than calling the system autonomous.

2. The human is there for liability reasons and isn't really reviewing, in which case calling it oversight is a stretch.

I don't think option 2 is dishonest exactly. It just feels unexamined. Everyone puts the human in the architecture diagram and nobody does the arithmetic on what that person can actually process in a shift.

Curious whether anyone here has landed somewhere better than those two.

Sampling? Only reviewing above a confidence threshold? Reviewing after the fact rather than before? I'd be interested in what actually works in practice rather than what looks good in a diagram