r/agenticAI

I spent about eight years inside credit card authorization. eCommerce and not POS. Something about agentic checkout has been bothering me and I want to know whether anyone here has a better answer than I do.

Take the controls we actually run. Device fingerprinting. Behavioral biometrics, meaning mouse movement, typing cadence, how long someone sits on the CVV field (which always gets me and I have to yell out to my kids to give this to me quick). Velocity rules. 3DS step-up. One-time passcodes to a phone. Risk models trained on how real people move through a checkout.

Every one of those infers that a human is present and behaving normally. None of them verifies that the purchase was wanted.

That held for thirty years because a human was always eventually there. Even card-not-present, someone typed the number. Presence was a decent proxy for intent, so nobody had to separate the two.

Agentic checkout removes the human on purpose. The signals then go one of two ways. Some disappear, because there is no mouse movement and no hesitation to measure. The rest get emulated, and emulated cleanly, because an agent produces consistent timing and a stable device signature every single run. A model trained to catch the anomalous human has nothing to catch when there is no human and no anomaly. A compromised agent looks the same as a working one.

I have been calling this the presence assumption. Authenticating the human, treated as though it authorized the transaction.

What I cannot resolve is what carries the decision once presence is gone. Options I keep seeing, and my problem with each:

• Agent identity and attestation. Tells you which agent is calling. Says nothing about whether this purchase sits inside what the person asked for.
• Merchant-side allowlists. Do not travel, and agents shop across merchants.
• Spend caps at the card. Blunt, and they do not survive an agent splitting a purchase.
• Post-hoc dispute. Fine on cards. Useless when the action does not reverse.

Real question, not rhetorical. For anyone running fraud ops or building on the agent payment rails, what are you planning to lean on is the absence of presence? Is anyone checking the transaction against the mandate the human actually granted, and does that hold up operationally? This becomes a huge liability in itself. I am sure there are solutions out there, at least in parts. Please illuminate.

Create a free MCP server for your website or organization. It allows direct integration with any other LLM or agent.

https://robauto.ai/create-mcp

Anyone has any good suggestion ( free of cost) may be youtube playlist that covers majority of agentic AI along with hands on.

I found Venkata reddy ai classes, but I am not sure how helpful it would be so before starting, I want to have people's input

Thanks

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So I am a chemical biologist in my work i accumulate a large amount of text , data , imaging files . And while looking through the net I found local agents which can be self hosted can be of much use

I am hoping that it can

1 Arrange and keep a track of my files

2 Keep me updated about my schedule, and and modify events on command

3 scrape through internet to see what's new about peptide chemistry

4 get me any information from the files and protocols saved

I have a intel i7 6700k and 32 gigs of ddr4 with a nvdia k620 with 2gb vram

Can you give this newbie some tips

Hey guys,

I need to automate a very very long AI workflow where my main claude session will act as orchestrator then use sub agents, but that still ends up filling the context and causing hallucinations after a couple of hours (like after 2-3 hours) and 10 or so sub agents.

Is there a better way?

I want to actually do very long agentic work and occasionally be able to chat and steer its direction or give ideas to work on/validate from my part as well.

Any resources or tips would be much appreciated!

Hello,

I am working as an AI/ML Intern at a software company that builds ERP software based on companies' needs, such as schools, Construction sites, Hospitals, Fuel Pumps, and so on. I need help building an AI Chatbot which can be integrated with an ERP application. The company uses .NET as their main language for building applications.

For example: an ERP application for a school

There are user roles: Admin, Principal, Teacher, Accountant, Student/Parent, Driver

Student lifecycle: Admission → Academic → Attendance → Examination → Result

ERP Modules: Admission Management -> Admission, Registration, Entrance Admit Card, and so on, such as Billing Management, Transport Management, and Examination Management

The task is to integrate an AI system into the ERP web application in which:

1. A user, such as an admin, principal, or accountant, inputs a question in natural language like "Give me the names of students whose remaining dues are over 5000 since last month" or "How many students are present today" or "Show me a graph of student attendance for the last 3 weeks"

2. The input is then converted to a relevant database query and searches for all the required details from the user input. The ERP uses Microsoft SQL to store all its data.

3. The AI generates output in natural language for the user and shows the relevant answer.

Restriction: No user should be able to alter any of the data in the database. It should be read-only.

I’ve been watching the hype around agentic AI build up over the last few months, and honestly, I feel like I'm taking crazy pills. Every second post on tech subreddits is someone claiming their "multi-agent framework" autonomously wrote an entire SaaS app or solved complex enterprise workflows.

When you actually look under the hood of 95% of these projects, it's literally just a standard LLM wrapped in a basic while loop, chained together with hardcoded if/else prompts, hammering an API until it brute-forces a passable answer.

Here are my main issues with the current state of "agents":

1. Non-deterministic nightmare: Giving an AI loop permission to execute code or call tools autonomously without deterministic guardrails is a recipe for infinite loops, blown API budgets, and broken production environments.
2. It's faster and cheaper to just write code: Why spend 3 hours engineering systemic prompt orchestration, fallback states, and context management for an agent to do a task when a 20-line Python script or a standard API integration does it in 5 milliseconds deterministically?
3. Junior devs are using it as a crutch: People who don't understand architecture are throwing "agents" at problems they don't know how to solve manually, creating unmaintainable codebases they can't debug when the agent inevitably hallucinates mid-execution.

I’m not saying autonomous systems will never work, but right now, "Agentic AI" is mostly a buzzword used by startup founders to raise venture capital and devs trying to pad their resumes.

Are people actually using agentic workflows in mission-critical production systems with real ROI, or are we all just pretending standard software engineering principles don't apply anymore? Change my mind.

, and I've been building something I'm calling FlowRoom. Roast it.

The idea: most AI tools are one person typing to one chatbot. FlowRoom is a shared room where multiple people and multiple AI agents work together on real, long-running operations and anyone can drop into the same live session to watch an agent work, redirect it mid-task, or hand it off to someone else.

First use case I'm targeting is industrial shift operations (think mining/processing plants big here). A fault gets reported, an agent picks it up, extracts the details, assigns a technician, flags anything expensive for human approval, and rolls it into the shift report. Humans stay in the loop for anything irreversible.

Long-term vision is that it's not just mining ,it's a customizable platform where any team (NGOs, sales, legal, schools) builds their own workflow without code.

What I actually want roasted:

• Is "multiplayer AI agents" a real need or a solution looking for a problem?
• Is starting with mining/industrial ops smart, or am I picking a brutal first customer?
• Everyone and their dog is building "AI agent platforms" right now. Why would this survive?

Be honest, I'd rather get torn apart here than by the market.

1 upvote

I’m trying to speak with people who have crossed a specific line with AI agents.

Not agents that summarize information or recommend an action.

I mean agents that can actually change something in a production system.

For example:

- update a CRM or ERP
- modify an order
- issue a refund
- create or change a ticket
- write to a database
- trigger a workflow
- call an API that creates a real side effect

I’m interested in what changes once you give an agent that level of autonomy.

How are you handling retries when the outcome of the first attempt is unclear?

If someone disputes an action months later, can you reconstruct what the agent knew, what it decided, what it sent, and what actually happened?

Do you treat agent actions differently from ordinary backend operations, or has your existing infrastructure been enough?

And who owns this internally?

I’m specifically looking to learn from teams already dealing with this in production.

If that describes what you’re working on, I’d love to compare notes. Happy to do a short call, or I can send a few questions over DM if async is easier.

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I d like to give my take on a narrative that's picked up a lot of momentum: the idea that we're heading toward an "Internet of Agents" — a shared layer where companies publish agents that other companies (or anyone) can discover and rent, presumably paying per token or per call. AGNTCY, ( Linux Foundation with Cisco, Dell, Oracle,...) is the most visible push in this direction — identity, discovery, messaging, observability, all built so agents from different vendors can find each other and interoperate across org boundaries.

I personally don't buy the endgame a lot of people are projecting onto this.

Just to be clear: interoperability standards for agents talking to each other, or to tools, have obvious value — MCP-style stuff solves a real problem. I'm not skeptical of "agents should be able to communicate using a shared protocol." That part makes sense to me.

What I don't buy is the sort of "marketplace of agents" framing — the idea that Company A will routinely rent and run Company B's autonomous agent inside its own workflows. A few reasons:

An agent isn't like compute or an API. Renting compute or calling an API is a well-understood trust boundary — you know exactly what you're getting and what it can touch. An agent is an autonomous decision-maker with its own goals, tool access, and failure modes. Letting a third party's agent operate inside your org is a much bigger trust ask than anything we've normalized so far.

A lot of the infrastructure here is solving a problem the premise itself creates. Identity, cryptographic verification, access control across organizational boundaries — these are exactly the features you need if you've already decided agents will be shared across companies. If that doesn't happen at scale, a meaningful chunk of this complexity doesn't need to exist. It's solving a self-inflicted problem rather than a pre-existing one.

Ownership, not rental, matches how companies already behave. Most enterprises are already cautious about running open-source models they don't fully control. Handing decision-making authority to another company's agent is a heavier lift than that. My bet is companies will keep building and owning their own agents in-house — even if slower and more expensive — because the liability and control stay contained. This mirrors how automation has generally gone: companies consume standardized data/APIs, but keep the actual decision logic in-house.

So my take: standardized communication between agents, yes. A functioning cross-company rental market for agents, not convinced. Feels more ideological ("open internet of everything") than something with real enterprise demand behind it.

I d like to know what you all think, maybe I miss something about this whole thing.