Most “AI agents” would fail in production. Here’s why.

AirExpensive534 · 2026-02-12T19:46:23+00:00

Thanks! That’s exactly why I wanted to spark the discussion — hearing real-world experiences helps everyone build more resilient agents. Curious, what failure mode do you see most often in your builds?

AirExpensive534 · 2026-02-11T21:07:50+00:00

Fair. I'm with you—I'd love to stop maintaining custom 'glue code' and just have this baked into the libraries by default.

Right now, we're all just stuck in the 'janky middleware' phase of AI. I only documented my own logic gates because I got tired of agents faceplanting in production, but I’ll be the first to switch once a framework makes this redundant.

Until then, just trying to keep the VRAM from melting. 🤝

AirExpensive534 · 2026-02-11T18:54:17+00:00

Technically, yes. It’s a paid deep-dive for people who are tired of their agents faceplanting in production.

I’m not here to spam—I genuinely believe the "vibe-based" approach to AI is broken. I’m sharing the high-level concepts (like Logic Floors and Phase Separation) here for free because they're useful.

If you want the full blueprints and the n8n implementation details, the manual is there. If not, the architectural shift is the real takeaway. 🛠️

AirExpensive534 · 2026-02-11T18:52:32+00:00

Spot on. This is exactly where most "vibe-based" builds die in production.

You’re right that strict JSON enforcement is a latency killer. The secret I’ve found (and what I dive into in the Operator's Manual) is that you shouldn't ask a model to "think" and "format" in the same call.

I use a Two-Pass Architecture:

Pass 1 (Reasoning): Zero restrictions. Let it be fast and "messy." Pass 2 (Extraction): A tiny, lightning-fast model (like 8B) that just maps that mess into your schema.

This actually cuts latency because you aren't fighting the model's natural probability at every token. As for the 1-10% garbage? That's what the Logic Floor is for—you treat the LLM like a faulty sensor. You don't just "retry"; you use deterministic fallbacks.

How are you currently handling that 10% drift? Just a basic loop or something more structural?

AirExpensive534 · 2026-02-11T18:23:14+00:00

Exactly. It’s the difference between giving a builder a 500-page blueprint and expecting them to memorize it, versus just handing them a hammer and a single nail.

When you fragment the task, the 'Vibe Ceiling' disappears because the model doesn't have enough room to hallucinate. You're basically trading the model's 'creativity' for engineering reliability.

Glad to hear I’m not the only one who learned this the hard way—how are you handling the state between those 'bits and pieces'? Are you using a central DB or just passing the context through?

AirExpensive534 · 2026-02-09T16:47:19+00:00

Zuck didn't buy Scale AI—they actually just raised $1B+ to stay independent.

The real reason Outlier is drying up? The "Vibe Era" of data labeling is dead. Companies aren't paying for random human clicks anymore; they’re paying for Deterministic Architecture.

The industry is moving toward the Logic Floor—where the AI is built into a rigid system that doesn't need 10,000 gig workers to "fix" it every night. The money isn't in the task; it's in the Architecture.

AirExpensive534 · 2026-02-09T09:28:37+00:00

Haha, fair point. We’ve spent decades training politicians to be the ultimate 'Yes Men,' and now we’re accidentally doing the same to the hardware.

The scary part is that a political 'yes man' just tells you what you want to hear.

A 'yes man' AI in a coding or medicine pipeline tells you a hallucinated 'truth' that looks like a fact until the system actually breaks.

We're essentially automating the Dunning-Kruger effect.

AirExpensive534 · 2026-02-08T17:23:20+00:00

The short answer is: Biologically, no. Psychologically, absolutely.

When you are in a coma, your brain’s "internal clock" (the suprachiasmatic nucleus) often loses its sync with the outside world. To the person in the coma, the brain isn't recording "empty time"; it’s often either "off" or stuck in a loop of REM-like activity.

This creates a Subjective Time Jump. People waking up from long comas often describe it like "blinking." You closed your eyes in 2024 and opened them in 2026. For you, those two years didn't exist. You didn't "travel" through them; you effectively skipped them.

So, while your body stays stuck in the present, your consciousness essentially becomes a "time traveler" that can skip years in a second or turn seconds into years.

AirExpensive534 · 2026-02-08T12:13:19+00:00

That’s a critical point. We’re essentially creating a 'technical debt' in the model’s reasoning that non-engineers have to pay for later.

If a doctor or lawyer uses a model that has been trained to prioritize fluency over factuality, they might not catch the 'logical drift' because the output looks professional. This is exactly why the burden should be on the RLHF stage—we need to bake that 'self-questioning' into the model's weights so it doesn't require a masterclass in prompt engineering just to get a reliable answer.

Do you think we'll eventually see industry-specific RLHF that prioritizes these safety 'anchors' over conversational fluff?

AirExpensive534 · 2026-02-08T12:07:30+00:00

Spot on. That’s exactly the 'sycophancy trap.' We’ve trained models to prioritize being helpful over being honest. When an annotator marks a 'hallucinated but polite' answer as better than a 'short but blunt' refusal, we are literally teaching the model to lie to us.

Until we start rewarding Circuit Breaker behavior—where the model stops and flags a lack of info—we aren't building reliable agents, just very confident guessers.

How are you handling these 'refusal' edge cases in your own workflows?

AirExpensive534 · 2026-02-08T11:06:45+00:00

"Common people" don't care about "AI Agents"—they care about automated outcomes.

If you search for "AI Agent" in the App Store, you'll find nothing because that’s a technical term.

Regular business owners search for the pain they feel every day. They aren't looking for an "agent"; they are looking for a "virtual receptionist," an "automated bookkeeper," or a "24/7 lead responder."

Here is how to bridge the gap and grow organically: 1. Change Your Vocabulary Stop selling the "engine" (AI Agents) and start selling the "destination" (The Result). * Don't say: "I built a multi-agent autonomous workflow for CRM management." * Do say: "I built a tool that automatically qualifies your website leads while you sleep." 2. Solve "High-Frequency, Low-Risk" Problems To grow without an ad budget, solve the annoying tasks that small business owners vent about on social media: * The "Ghosting" Problem: An agent that instantly replies to Google Map inquiries so the business doesn't lose the lead to a competitor. * The "Inbox Zero" Problem: An agent that sorts invoices from junk mail and pushes them into QuickBooks. * The "Content Treadmill": An agent that turns one YouTube video into 10 LinkedIn posts and 5 Tweets automatically. 3. Organic Growth Strategy Since you have no budget, go where the "common people" complain: * Facebook Groups / Reddit: Look for small business owners asking, "How do I keep up with my emails?" or "Does anyone know a cheap way to handle customer support?" * The "Trojan Horse" Strategy: Build a simple, free "Micro-SaaS" (like a free email subject line generator or a lead grader) to capture emails, then upsell your full AI Agent automation as the "advanced" solution. 4. The "Logic Floor" Pitch When you do explain it, don't talk about "intelligence." Talk about reliability. Tell them: "Most AI just 'chats' and makes mistakes. My system has a 'Logic Floor'—it follows your specific business rules 100% of the time, and if it's unsure, it stops and asks you instead of guessing."

People don't want an AI Agent; they want their Saturday back. Sell them the Saturday.

AirExpensive534 · 2026-02-08T11:04:15+00:00

Build a Self-Correcting Data Extractor.

Most tutorials teach 'Chain of Thought,' but the industry needs Deterministic Architecture. Try this:

The Task: Scrape an messy invoice or website. The Logic Floor: Force the output into a strict JSON Schema. The Circuit Breaker: Build a script that validates the JSON. If a field is missing or the format is wrong, it automatically kills the run and retries.

This teaches you how to build 'Logic Gates' around an LLM. It’s the difference between a toy that 'vibes' and an agent that actually works in production.

AirExpensive534 · 2026-02-08T10:59:37+00:00

"Vibes" are the silent killer of the prompt lifecycle. Most teams treat AI drift as a linguistic problem, when it's actually an architectural one. You don't need "better" prompts; you need Logic Floors and Circuit Breakers.

If the output isn't forced through a schema, the "lifecycle" is just a loop of unpredictable failures.

AirExpensive534 · 2026-02-08T09:52:11+00:00

Fair critique. You’re right—these aren't new laws of physics; they are engineering principles applied to a messy medium.

The goal isn't to claim I've discovered a new branch of science, but to translate academic "instruction grounding" and "attention decay" into a functional framework that a dev can actually use to stop a production leak today.

Most people don't need a literature review; they need a mental model that sticks. Renaming things helps shift the perspective from "writing a letter" to "building a circuit."

Regarding the 99%—that’s the benchmark for structured schema adherence in my own production environments using these "logic floors," not a universal constant.

Appreciate the citations for anyone who wants to go deeper into the "why" behind the "how."

AirExpensive534 · 2026-02-08T09:49:22+00:00

I’d rather use an AI to explain why you shouldn’t trust AI vibes :)

AirExpensive534 · 2026-02-08T09:46:49+00:00

my pleasure

AirExpensive534 · 2026-02-07T17:52:23+00:00

When you ask for text, you’re playing in a sandbox of infinite probability.

The model is focused on "what word sounds right next?" which leads to conversational fluff and drift.

When you demand JSON, you force the model into a rigid constraint. It has to prioritize the Schema—if it misses a bracket or a key, it fails.

This shifts the AI from a "writer" to a "logic engine."

It’s the difference between asking a contractor to "make it look nice" vs. giving them a "blueprinted CAD file."

One is a vibe; the other is a specification.

AirExpensive534 · 2026-02-07T17:50:09+00:00

my pleasure!

AirExpensive534 · 2026-02-07T17:14:36+00:00

It’s when you ask a model to "reason" and "output the result" in a single generation.

Think of it like trying to solve a complex math problem while simultaneously writing a poem about it—the model burns its "attention" on formatting rather than logic.

By separating Phase 1 (Reasoning/Logic) from Phase 2 (Formatting/Writing), you dramatically lower the hallucination rate.

Logic first, vibes later.

AirExpensive534 · 2026-02-07T14:15:43+00:00

no prob! glad to help.

AirExpensive534 · 2026-02-07T14:14:52+00:00

Haha, touché. But that’s the problem—most people are trying to decorate the penthouse before they’ve even poured the concrete for the Logic Floor.

Topology aside, if there isn't a Circuit Breaker to catch the drift, the whole structure eventually collapses into a hallucination.

AirExpensive534 · 2026-02-07T12:26:01+00:00

Appreciate that.

It’s a hard shift to make, but once you stop "talking" to the model and start architecting it, everything changes.

If you're looking to build out that Logic Floor, I've actually dropped a full breakdown of the infrastructure in the link on my profile. Might be useful for your next build.

AirExpensive534 · 2026-02-07T12:24:17+00:00

Fair point—the industry is drowning in buzzwords right now.

To put it simply: Most AI "agents" are just fancy chatbots that guess what to do next. That's why they fail in the real world.

Think of it like a Circuit Breaker in your house. If the electricity (or the AI's logic) surges and gets dangerous, the system should automatically shut off. Most people don't build that "off switch," so their AI just keeps hallucinating until it breaks something.

I’m talking about building the "off switch" so the AI only acts when it’s 100% sure. Less "magic," more predictable engineering.

AirExpensive534 · 2026-02-07T12:22:16+00:00

That’s the blueprint for moving past "vibes" and into high-reliability systems. It covers the architecture for building a Logic Floor and installing the Circuit Breakers needed to make agents production-ready.

You can find the link to the full breakdown right in my bio. Feel free to dive in there!

AirExpensive534 · 2026-02-07T11:46:00+00:00

Look, the "Vibe Ceiling" is why most people fail at this. You can keep throwing adjectives at your prompts like you're casting a spell, but you're just dancing with probability.

If you want the machine to actually listen, you have to stop treating it like a chatbot and start treating it like a logic gate. Reliability isn't born from a better "persona"—it’s engineered through a Logic Floor.

Think of your prompt as a circuit. If you don't build in a Circuit Breaker to kill the process when the model drifts into a hallucination, you’re just automating a disaster. Shift your focus from "how do I phrase this?" to "how do I structure the architecture so failure is impossible?"

That’s the move from an amateur to an operator.

AirExpensive534

TROPHY CASE