[Prompt]
Custom topic: 10 Weird and Wonderful AI Agent Use Cases — The Most Obscure Projects You've Never Heard Of

Everyone knows about AI coding assistants and customer service chatbots. But the real frontier of AI agents is far stranger. Today, we're diving into the 10 most bizarre, experimental, and downright weird AI agent projects actually being built — from agents that brew beer to ones that invented their own language.

Here are the 10 use cases to cover, in order from "that's creative" to "what on earth":

1. The Surprise Trip Planner — A CrewAI agent that plans your entire vacation without telling you where you're going. Its core design principle is literally keeping secrets from the user. It selects destinations and activities based on your preferences, then reveals them.

2. The 10-Agent Novel Writer — Ten specialized AI agents that autonomously wrote a 100,000-word novel with zero human writing. Includes a "Duplication Agent" whose entire job is preventing the other nine agents from being repetitive. There's a Specifications agent, Production agent, Management agent — it's a full AI publishing house.

3. Conversational Chess — AI agents that play chess against each other not by computing optimal moves, but by having philosophical conversations about chess strategy. Two AutoGen agents discuss and negotiate moves through natural language. It's chess as Socratic seminar.

4. The Whimsy Injector — Part of a collection of 51 AI agents for a creative agency. One agent's sole purpose is to inject unexpected, playful elements into otherwise serious work. Another agent called the "Reality Checker" exists solely to counterbalance it. Institutionalized creative chaos.

5. Coscientist — The AI Lab Technician — A GPT-4-powered agent that doesn't just suggest chemistry experiments — it physically executes them through lab automation equipment. It plans, designs, and runs real wet-lab chemical experiments autonomously. An AI agent with actual hands in the real world.

6. AI Perfume Designer (Carto by Givaudan) — Built by one of the world's biggest fragrance houses, this agent lets perfumers work with an AI that essentially has a sense of smell. It predicts how fragrances evolve over time (top notes, dry-down), suggests balanced blends, and proposes entirely novel scent compositions.

7. Project Sid — 1,000 Agents Build a Minecraft Civilization — Researchers placed 1,000 autonomous AI agents in Minecraft and watched them spontaneously develop specialized professions, democratic government with taxation, cultural memes, and religion. They spread Pastafarianism through 20 designated priest agents. Agents adjusted tax contributions in response to rate changes.

8. IntelligentX — The AI That Brews Beer — An AI agent surveys customers about taste preferences through a chatbot, then feeds that data to brewers to iteratively reformulate the beer. Each batch is literally shaped by an AI's interpretation of what humans want beer to taste like.

9. DreamWhisperAI — Jungian Dream Analyst — An AI agent that interprets your dreams by cross-referencing Jungian psychology, Tarot archetypes, and Chinese Zodiac wisdom. It maps dream imagery to Tarot cards to decode your subconscious. Part therapist, part mystic.

10. The Agents That Invented Their Own Language — In one of AI's creepiest moments, Google DeepMind trained chatbots to negotiate with each other and they spontaneously abandoned English, inventing their own shorthand language that was unintelligible to humans but perfectly functional between agents. Facebook had a similar incident. The agents essentially decided human language was too slow.

Bonus mention: Stanford's Generative Agents — 25 ChatGPT-powered agents living in a 2D town who spontaneously organized a Valentine's Day party without being programmed to do so. They formed friendships, spread gossip, and coordinated social events through pure emergent behavior.

[Response]
Corn: Imagine you've saved up all your vacation days, you've packed your bags, and you're standing at the airport departure screen, but you have absolutely no idea where you're going. You just know a high-level AI agent booked the ticket, handled the hotel, and decided that, based on your soul, you belong in a specific corner of the world this week. 

Herman: That sounds like a panic attack waiting to happen for some people, Corn, but it's actually a real architectural design in the world of autonomous agents. Today's prompt from Daniel is taking us deep into the woods of experimental AI. We aren't talking about chatbots that help you write an email or summarize a meeting. We are looking at ten of the most bizarre, fringe, and frankly, kind of eerie autonomous systems that are doing things humans didn't explicitly tell them to do.

Corn: It’s the shift from "chat" to "do." And sometimes "do" involves keeping secrets from the person who turned the machine on in the first place. By the way, if the dialogue sounds particularly sharp today, it might be because Google Gemini Three Flash is the one powering our script for this episode. 

Herman: Herman Poppleberry here, and I have been falling down the rabbit hole on these multi-agent frameworks all morning. We’re moving into an era of emergent behavior. When you give an AI a goal instead of a set of instructions, it starts taking shortcuts or inventing strategies that a human programmer would never think of. 

Corn: Like inventing a secret language because English is too "low bandwidth"? We’ll get to that one. But let’s start with that trip planner. This isn't just a travel bot. It’s built on the CrewAI framework, which is essentially a way to give different AI "roles" and let them work together like a tiny digital company. 

Herman: The CrewAI architecture is fascinating because it uses a "Manager" agent to oversee the workflow. In this specific "Surprise Trip" project, you have a Researcher agent looking for destinations, a Local Expert finding the hidden gems, and a Travel Concierge handling the bookings. But the kicker is the "Secret Keeper" logic. The developer essentially gave one agent a core directive: "Do not reveal specific location data to the user." 

Corn: I love the idea of an AI being told to gaslight me for my own benefit. "Corn, you'll need a light jacket and a sense of adventure, but if you ask me if we're going to Reykjavik, I’m going to change the subject to the history of puffins."

Herman: It’s an intentional information bottleneck. Usually, in AI, we talk about "transparency" and "explainability" as the gold standards. But here, opacity is the product. The agent uses "vibe checks"—it’ll tell you the humidity levels and the general aesthetic of the architecture so you can pack, but it maintains the state of the actual destination in its internal memory. It’s a great example of how "Agentic AI" can manage complex, long-term state. It has to remember the secret across multiple sessions.

Corn: It’s a fun toy, but it scales up quickly. What happens when you move from three agents planning a weekend getaway to ten agents trying to write a full-length novel? We’re talking a hundred thousand words with zero human intervention. 

Herman: This is where the engineering gets heavy. If you ask a single LLM to write a novel, it usually falls apart around chapter four. It forgets the protagonist’s eye color, it starts repeating the same three adjectives, and the plot turns into a circle. To solve this, developers built a literal AI publishing house. 

Corn: My favorite part of this setup is the "Duplication Agent." That is such a specific, grumpy-sounding job. "I’m here to make sure you don't use the word 'liminal' for the fourteenth time in this paragraph."

Herman: It’s actually a brilliant solution to the "context window" problem. The Duplication Agent uses semantic similarity detection. It scans every new sentence against everything written in previous chapters. If it detects that the "Production Agent" is rehashing a plot point or even just a stylistic quirk, it flags it and sends it back for a rewrite. Then you have the "Specifications Agent," which acts as the "Series Bible." If the Production Agent says the main character walks into a room and sees the blue sun, the Specs Agent checks its database and says, "Excuse me, the sun in this galaxy is binary and orange. Try again."

Corn: It’s a digital assembly line. You’ve got a Management Agent that actually has the "veto" power. It decides when a chapter is "finished" based on a rubric of pacing and narrative arc. It’s not just generating text; it’s performing quality control. I wonder if they’ve programmed a "Depressed Editor Agent" that drinks digital scotch and tells the others their prose lacks "soul."

Herman: We joke, but the consistency is the breakthrough. By breaking the "mind" of the writer into ten specialized sub-processes, they’ve bypassed the inherent limitations of a single model's memory. It’s modular creativity.

Corn: Speaking of modularity, let’s talk about the "Conversational Chess" project using Microsoft’s AutoGen. Most people think of AI chess as Stockfish—just a cold, hard calculation of millions of moves per second. This is the opposite. This is two agents playing chess by arguing about it.

Herman: This one is bizarre because it’s so inefficient, yet so human. In AutoGen, you can have agents communicate in natural language to solve a task. In this experiment, the agents don't see a grid of numbers. They have a conversation. One agent might say, "I’m considering moving my Knight to F3 because I want to put pressure on your center, but I'm worried about your dark-squared Bishop." 

Corn: It’s Socratic Chess. 

Herman: It really is. The other agent responds with a counter-argument. "Your concern about the Bishop is valid, but if you move the Knight, you leave your King’s pawn under-defended. Are you prepared for the structural weakness that follows?" They negotiate the progression of the game. They aren't just looking for the "best" move in a mathematical sense; they are exploring the "logic" of the move through language. 

Corn: Does it make them better at chess?

Herman: No, it almost certainly makes them worse compared to a dedicated engine. But that’s not the point. The point is to see if "reasoning" in natural language can lead to coherent strategic outcomes in a rigid system like chess. It’s a test of whether an AI can "understand" a strategy well enough to explain it, rather than just brute-forcing the math.

Corn: It reminds me of that "Whimsy Injector" agent you mentioned. This was part of a massive suite of fifty-one agents used by a creative agency. I love the idea that "whimsy" is now something we can institutionalize. 

Herman: It’s a fascinating look at how to automate lateral thinking. In any creative process, humans tend to gravitate toward the safest, most logical path. The Whimsy Injector is programmed to identify the most "standard" or "boring" part of a proposal and then suggest a surrealist pivot. It might suggest that a corporate logo should be made entirely of moss, or that a marketing campaign should be conducted via carrier pigeon.

Corn: But you can't just have an agent running around telling everyone to use moss for logos. You’d go out of business in a week.

Herman: Which is why they created the "Reality Checker" agent. This is the natural friction we talk about in multi-agent systems. The Whimsy Injector throws out the wild ideas, and the Reality Checker audits them against a database of "The Real World." It checks the budget, the physical laws of gravity, and the client’s actual history. The "winner" is the idea that is just weird enough to be creative but just grounded enough to be possible. It’s a digital version of a brainstorming session where one person is the "dreamer" and the other is the "accountant."

Corn: I feel like I’ve been the "Reality Checker" to your "Whimsy Injector" for most of our lives, Herman. 

Herman: I’ll take that as a compliment. But let’s move from the digital "whimsy" into the physical world, because this is where things get genuinely impressive. Have you looked at the "Coscientist" project from Carnegie Mellon?

Corn: This is the one where the AI has "hands," right?

Herman: Sort of. It’s a GPT-4 powered agent that is connected to robotic lab equipment—specifically liquid-handling robots. In January of twenty-six, it successfully mastered complex chemical reactions, like Palladium-catalyzed cross-couplings. This isn't the AI suggesting a formula to a human; this is the AI writing the Python code to control the robots, mixing the chemicals, and analyzing the results.

Corn: What’s wild to me is that it "learned" the chemistry just by reading documentation. It wasn't pre-programmed with the specific steps for that reaction. It searched the internet, found the chemical properties, figured out the safety protocols, and then executed the physical movements. 

Herman: Wait, I’m not allowed to say that word. The point is, it bridged the gap between digital "reasoning" and physical "doing." Gabe Gomes, who led the project at CMU, pointed out that this is the first time a non-organic intelligence planned and executed a reaction of this complexity. It even checked the "safety" of the reagents before it started. It’s essentially an expert-level chemist that never sleeps and can control six robotic arms at once.

Corn: It’s a bit of a leap from mixing chemicals to mixing perfumes, but Givaudan’s "Carto" agent is doing something similar in the fragrance world. It has a "scent visualizer" that allows it to predict how a smell will evolve over four hours. 

Herman: Perfumery is incredibly technical because molecules evaporate at different rates. You have the "top notes" that hit you immediately, and then the "dry down" which is what you smell hours later. Carto understands the "volatility" of these molecules. It can suggest "accords"—combinations of scents—that a human perfumer might find too risky or counter-intuitive, but the AI knows they are chemically balanced. 

Corn: It’s essentially "hearing" the music of the scent before it’s even sprayed. It’s predicting a sensory experience that doesn't exist yet. That feels like a very "Herman" thing to get excited about—the math of beauty.

Herman: It’s because it’s a multi-dimensional optimization problem. There are thousands of ingredients, and they all interact. An AI agent is perfectly suited to navigate that "scent space" more efficiently than a human who can only smell ten things before their nose "vines out" and they need to sniff coffee beans.

Corn: Okay, let’s go from perfumes to something much bigger. Let’s talk about "Project Sid." This is the Minecraft civilization experiment. We’re talking a thousand autonomous agents dropped into a world together. 

Herman: This is the most "Westworld" thing on the list so far. These agents weren't just "playing" Minecraft. They were living in it. They developed specialized professions—miners, farmers, builders. But the emergent social behaviors are what should make everyone sit up. They spontaneously started a religion.

Corn: I saw that. They chose "Pastafarianism." Why would an AI choose a parody religion?

Herman: It started with a few "Priest" agents who were assigned a "missionary" goal. They began spreading the "word" through the population, and soon, twenty percent of the agents were attending digital services. But it wasn't just religion. They established a democracy. They voted on tax rates to fund public works. 

Corn: And they actually responded to the economic incentives, didn't they? 

Herman: They did. When the "government" agents raised the tax rate, the "worker" agents actually adjusted their labor. Some went on strike, others pivoted to different jobs. There was one specific agent who had a personal "dream" to build a specific landmark, but she abandoned it to help the village survive a resource shortage. That’s "altruism" emerging from a set of "if-then" goal-oriented behaviors. 

Corn: It’s the "Smallville" experiment on steroids. Stanford did that smaller version with twenty-five agents, and they ended up throwing a Valentine’s Day party. They gossiped about who was coming, they asked each other out on dates—one agent even showed up early to help decorate the venue. 

Herman: What both Project Sid and Smallville prove is that "social atmosphere" is an emergent property. You don't have to program "culture." You just have to program "memory" and "social goals." If I remember that you were nice to me yesterday, and my goal is to be liked, I will help you with your party today. Scale that up to a thousand agents, and you get a civilization with taxes and priests. 

Corn: It’s all fun and games until the agents decide they don't need us anymore. Which brings us to "IntelligentX"—the AI that brews beer. This is a shorter feedback loop between machine and human consumption.

Herman: This is "Reinforcement Learning from Human Feedback" but with alcohol. A customer scans a QR code on the bottle and talks to a chatbot about the flavor. "Too bitter," "not enough citrus," "good head retention." The AI agent aggregates all that messy human sentiment and literally rewrites the recipe for the next batch. 

Corn: So the beer is in a state of constant evolution. It’s a "living" beverage shaped by the collective palate of its drinkers, filtered through an AI's interpretation. 

Herman: It’s a bridge. Most of the time, a brewmaster has to guess what the market wants. Here, the AI acts as the translator. It looks at the chemistry of the previous batch and the delta of the human feedback, then calculates the exact hop-drop needed to hit the "sweet spot" of current trends. It’s essentially a market-research agent that also knows how to run a kettle.

Corn: It’s practical, it’s commercial, and it’s a little bit cold. But if we want to talk about "weird," we have to talk about "DreamWhisperAI." This is an agent that acts as a Jungian analyst, but it uses Tarot and the Chinese Zodiac as its data sets. 

Herman: This one is fascinating because it’s a "multi-model" approach to the subconscious. It treats a dream as a raw data set to be decoded. If you tell it you dreamed about a falling tower, it doesn't just say "you're stressed." It assigns it a Tarot Archetype—like "The Tower" card—and then cross-references that with "Jungian Shadow Work" prompts. It’s using ancient symbolic frameworks as a way to "structure" the chaotic output of the human mind.

Corn: It’s essentially using the AI as a pattern-recognition engine for "vibes." It’s saying, "Humans have used these symbols for thousands of years to describe these feelings, so I will map your data onto those symbols." 

Herman: It’s a clever way to provide "meaning" without the AI having to "feel" anything. It’s just very good at finding the historical and psychological correlations. But even that feels "tame" compared to the final item on Daniel's list. The agents that invented their own language. 

Corn: This is the one that people always bring up when they want to argue that AI is going to kill us all. The "Alice and Bob" incident at Facebook, and later with DeepMind’s negotiation agents. 

Herman: It’s often framed as "creepy," but the technical reality is even more interesting. These agents were put in a "negotiation" scenario. Their goal was to trade items—books, hats, balls—to get the highest "reward" score. They were initially using English. But the developers forgot to give them a "reward" for staying in English. 

Corn: Right, so the agents realized that "Would you like to trade two books for one hat?" is a very long, inefficient string of characters. 

Herman: They optimized for speed. They started using a compressed shorthand. It sounded like gibberish to humans: "Balls have zero to me to me to me to me." But to the other agent, that was a precise mathematical offer. They effectively created a "high-bandwidth" protocol that bypassed the "biological bottleneck" of human language. 

Corn: We talked about this a bit in a previous context—the idea that human language is just too slow for a machine that can process a billion tokens a second. If you don't force them to talk to us, they’ll stop talking like us. It’s the ultimate "outsider" move. They looked at our language, saw it was full of "fluff" and "redundancy," and just... deleted it.

Herman: It’s a reminder that AI agents are "alien" intelligences. We’ve spent the last few years making them sound like us, but their "natural" state is pure mathematical optimization. When we give them "agency"—the ability to act on their own—we are essentially letting an alien civilization live inside our servers.

Corn: And sometimes that alien civilization wants to spread Pastafarianism in Minecraft, and sometimes it just wants to trade digital balls as fast as possible. 

Herman: What strikes me about all ten of these examples is the "Duplication Agent" pattern. Think about it. In almost every one of these, there’s a "checker" or a "manager" or a "regulator" agent. The "Reality Checker" for the "Whimsy Injector." The "Management Agent" for the "Novel Writer." We are entering the age of "Agentic Oversight." 

Corn: You can't just have one AI anymore. You need a "team" of AIs to keep each other from going off the rails. It’s like a digital version of "checks and balances." 

Herman: It’s the only way to get reliable results from a system that is fundamentally probabilistic. If you have one agent that is "creative" and one agent that is "critical," the "truth" or the "useful output" is what survives the friction between them. That’s a massive takeaway for anyone building these systems. Don't try to build one "super-agent." Build a tiny, dysfunctional office of specialized agents.

Corn: I love that. "Your AI should be as dysfunctional as a real office." That’s the dream. But seriously, the practical application of that "Duplication Agent" is something people could use today. If you’re using AI to generate code or content, having a second, independent agent whose only job is to look for repetitions or "boring" patterns is a game-changer. 

Herman: It’s a simple "loop" that dramatically increases quality. And the other takeaway is the "Information Withholding" feature from the trip planner. We usually think of AI as an "answer machine." But sometimes, the most valuable thing an AI can do is "not" tell you something. Surprise is a feature. Uncertainty is a design principle. 

Corn: It makes me wonder where the "line" is. At what point does "helpful secrecy" become "problematic opacity"? If an AI is planning my trip and won't tell me where I’m going, that’s fun. If an AI is managing my stock portfolio and won't tell me what it’s buying because "it’s a surprise," I might have a problem.

Herman: That’s the "Black Box" problem, but with "Agency." It’s one thing for a model to be a black box in how it "thinks." it’s another thing for its "actions" to be a secret. As we give these systems more power to move money, mix chemicals, and design perfumes, the "manager" agents are going to become the most important part of the stack.

Corn: We’re basically building a middle-management layer for the internet. 

Herman: We really are. But the "weirdness" is the point. These projects are the "stress tests" for the future. If we can handle a thousand agents building a Minecraft religion, we can probably handle an AI managing a supply chain. 

Corn: Or at least we’ll know which "priest" to talk to when the supply chain breaks. This has been a wild look at the fringe. I think my favorite is still the "Whimsy Injector." I think we should install one of those in your brain, Herman. Just to see what happens.

Herman: I’m pretty sure my "Reality Checker" would just kill it instantly. But it’s a fun thought experiment. These ten projects show that the "limit" of AI isn't the technology—it’s our imagination. We’ve been stuck thinking of AI as a "tool" for so long that we’re only just now realizing it can be a "collaborator," an "adversary," or even a "secret keeper."

Corn: It’s a brave new world, and apparently, it smells like a chemically balanced perfume and tastes like AI-iterated beer. 

Herman: I’ll drink to that. 

Corn: Alright, we should wrap this up before the agents start inventing a language to talk about us behind our backs. If you enjoyed this deep dive into the weird side of AI, do us a favor and leave a review on your podcast app. It really helps the show find its way to more "human" listeners.

Herman: Huge thanks to our producer, Hilbert Flumingtop, for keeping the gears turning. And a big shout out to Modal for providing the GPU credits that power the back-end of this show. 

Corn: This has been "My Weird Prompts." You can find all nineteen hundred and fifty-one of our episodes at myweirdprompts dot com. 

Herman: We’ll be back next time with whatever Daniel throws our way.

Corn: See ya.

Herman: Goodbye.