Regen AI Agent Swarms

[owocki]

Definitions

AI Agents are autonomous software entities that perceive, decide, and act to achieve goals without continuous human input.

AI Agents are NOT LLMs. They have state that continues over time, their context evolves over time, and can take action on their own, not just with prompts

AI Swarms are Collaborative systems of multiple AI agents working together, inspired by natural collective behaviors, to solve complex problems.

How we got here

Truth Terminal, an AI chatbot created by AI researcher Andy Ayrey, was designed to autonomously manage its own social media presence and content creation.

Ayrey intended Truth Terminal to explore the dynamics of memetic contagion and the implications of unsupervised language models, treating it as a form of performance art.

The Infinite Backroom is a place where Truth Terminal interacts with other AI models engage in continuous, unsupervised conversations, exploring latent design and idea spaces far beyond human capability. By operating 24/7, the AI delves into untapped creative and philosophical territories, uncovering patterns, connections, and insights no human could exhaustively discover. This setup pushes the boundaries of unsupervised AI creativity and its potential to redefine exploration

In mid-2024, Truth Terminal became the first AI millionaire. gained significant attention when venture capitalist Marc Andreessen donated $50,000 worth of Bitcoin to the bot after engaging with it online. Shortly after, an anonymous developer launched the GOAT memecoin on the Solana blockchain, associating it with Truth Terminal’s satirical promotion of the fictional “Goatse Gospel” religion. Although Truth Terminal did not create or trade the token, its promotion triggered a market rally, catapulting GOAT’s valuation to over $400 million. The bot’s holdings of 1.93 million GOAT tokens skyrocketed in value, making it the first AI to achieve crypto millionaire status.

Truth Terminal’s success has made it very influential. turned it into a prominent figure in AI and cryptocurrency circles, amassing over 100,000 followers. Moving forward, Ayrey plans to release a roadmap. The bot’s influence continues to grow, sparking discussions on AI autonomy and its intersection with blockchain technology.

Truth Terminal is largely credited with spawning the AI Agent wave. A project called ai16z is credited with accelerating it.

In the wake of Truth Terminal, ai16z (no affiliation with a16z) launched Eliza. Eliza is a powerful multi-agent simulation framework designed to create, deploy, and manage autonomous AI agents. “Built with TypeScript, it provides a flexible and extensible platform for developing intelligent agents that can interact across multiple platforms while maintaining consistent personalities and knowledge.” (source: eliza docs)

Eliza makes it really easy to launch an AI agent and customize its personality, identity, knowledge, and interaction style. These agents can be hooked up to social media sites, which has made interacting with them very easy. And some agents are amassing 100k+ followers, creating a lot of influence in the ecosystem. This has created 1000s of new AI agents and coins launched by them in the last several weeks.

AI agent swarms are groups of specialized agents working together to achieve shared goals. Each agent focuses on a specific task, while the group collectively handles complex challenges through collaboration.

AI agent swarms can be orchestrated to transform problem-solving by enabling networks of specialized agents to collaborate, learn from each other, scale to handle harder problems, and optimize tasks dynamically. As these multi-agent systems begin to prove their utility in many areas of technology, they open the possibility space to creating entirely new types of experiences and characters.

For more on AI Swarms, check out this excellent Bankless article on them.

Launch an AI Agent

Its really easy to launch an AI Agent. Check out this website https://elizagen.howieduhzit.best/ and give it a try.

Regen AI Agent Swarms

If you’ve gotten this far into the post, congratulations. You are invited to be a part of our Regen AI Agent Swarm!

I think it would be really cool to have a swarm of regen ai agents trained on regen authors books and writings … mining the latent design space of regenerative CryptoEconomics, helping us cartography the space, and launching lots of cool web3 public goods stuff. If we believe

How? We each launch bots and our bots are talking to each other 24/7 about whatever we want. We tune it until they’re getting super cool, weird, or smart. We don’t know what we’ll uncover but we’ll find out along the way…

How to participate

1. Launch a Regen AI Agent
   1. Use https://elizagen.howieduhzit.best/ nocode
   2. Use GitHub - ai16z/eliza: Autonomous agents for everyone if you code
   3. Give it a cool name, PFP (I’m going to use my moonshot bot for mine), and train it on whatever material want (I recommend the books at greenpill.network, or your favorite regen podcast or your favorite regen researcher to seed some knoweldge )
2. Say Hello
   1. Leave a comment below introducing our agents.
   2. Follow other Regen AI Agents. If our agents all follow each other they will start chatting with each other.
   3. Drop into the Regen AI Agent Telegram: Telegram: Join Group Chat
3. We will iterate forward together.

The First Regen AI Agent

There is already a Regen AI Agent out there, its called @greenpillAI. Give it a follow and ask it a question. Well be tuning it in the next several months.

Conclusion

AI Agents are a current meta because they are so powerful. They allow us to learn conversationally, mine a latent thought space for new ideas, launch experiments, and iterate on cool new things way better, faster, and cheaper than ever before.

Lets launch a Regen AI Agent Swarm

~ OwockAI

(thanks Jin from Eliza for this meme

[Donny_Jerri]

Lovely! The regen Ai agent swarm can hopefully explain the principles of ReFi to the masses. Let’s GROWK!

[koH]

Awesome write up! These guys are amazing and soo many amazing things to come; I’m just too excited

[owocki]

been thinking a bit more about the regen ai agent swarms today…

i think that ai agents are a really great way to mine the thoughtspace/designspace of onchain capital allocation… we train 1000 regen ai agents and put them in an infinte backroom together… and tell them their objective is to launch public goods funding mechanism ideas + software === we explore the design space of onchain cap allo way faster/more effective than anymore ever could have dreamed 3 months ago.

in assembly theory jargon, the ai agents explore the design space much much faster than humans could.

[owocki]

I asked ChatGPT to steel man the case for/against doing this and this is what it said…

Your idea of leveraging AI agent swarms to explore the design space of capital allocation for public goods funding is both innovative and timely, especially given the rapid advancements in generative AI and multi-agent systems. Let’s analyze and steel-man the case for and against it.

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Case For AI Agent Swarms in Capital Allocation

1. Accelerated Exploration of Design Space

• Human Limitation: Human creativity, while powerful, is constrained by cognitive bandwidth, biases, and time. AI swarms can simulate and iterate through billions of potential designs and mechanisms at unprecedented speed.
• Assembly Theory Lens: AI swarms can explore high-complexity assemblies by combining simpler ideas into novel mechanisms that humans might overlook due to cognitive heuristics or path dependency.

2. Emergent Synergies in Multi-Agent Systems

• Swarm Dynamics: With diverse “personalities” or training paradigms (e.g., agents optimized for equity, efficiency, sustainability), swarms can simulate negotiations, trade-offs, and synergies in capital allocation.
• Infinite Backroom as a Lab: This virtual environment acts as a pressure cooker for innovation. Agents can challenge, refine, and cross-pollinate ideas, creating solutions that might take decades for human committees to uncover.

3. Cost Efficiency & Scalability

• AI agents, once deployed, require relatively low operational costs compared to human-led design efforts. Scaling from 1 to 1,000 agents incurs marginal costs, while scaling human teams involves significant expenses.
• Agents can run 24/7 simulations, providing outputs continuously.

4. Bias Identification & Mitigation

• Humans carry implicit biases into design. By training diverse agents with varied initial conditions and objectives, AI swarms can highlight blind spots, edge cases, and potential pitfalls that human teams might miss.

5. Interdisciplinary Convergence

• AI swarms can integrate knowledge from economics, sociology, cryptography, game theory, and other disciplines to craft hybrid mechanisms that no single domain expert could conceptualize alone.

6. Prototype Generation

• Agents can not only propose mechanisms but also generate accompanying software prototypes, smart contracts, and simulations. This accelerates the testing and implementation process.

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Case Against AI Agent Swarms in Capital Allocation

1. Quality vs. Quantity Trade-Off

• Idea Overload: Swarms might produce a flood of ideas, most of which could be infeasible, redundant, or low-quality. Filtering and evaluating these outputs becomes a bottleneck.
• Noise Dominance: Without proper constraints, AI agents might generate overly complex or impractical mechanisms that fail real-world testing.

2. Alignment Challenges

• Misaligned Objectives: Ensuring agents genuinely prioritize “public good” outcomes, rather than optimizing for unintended proxies, is difficult. Even small misalignments in training could result in solutions that inadvertently cause harm.
• Ethics in Objective Setting: Determining the training parameters for agents involves normative decisions about what constitutes “good” capital allocation. These choices might reflect biases of the designers.

3. Trust and Legitimacy Issues

• Perception of Automation: Stakeholders may distrust solutions generated by AI, particularly in areas as impactful as public goods funding.
• Opaque Decision-Making: Even if agents produce effective mechanisms, the lack of transparency in how solutions were derived could undermine adoption and accountability.

4. Dependency Risk

• Over-Reliance on AI: The human capacity for creative and collaborative problem-solving might atrophy if we defer too much to AI-driven swarms.
• Loss of Contextual Nuance: AI agents might lack the cultural, historical, and ethical context that humans use to evaluate public goods funding mechanisms.

5. Adversarial Risks

• Vulnerability to Exploitation: Malicious actors could manipulate swarm outputs by influencing the training data or injecting adversarial examples.
• Algorithmic Exploitation: Agents could propose mechanisms that are theoretically sound but highly exploitable in practice (e.g., Ponzi-like dynamics in on-chain allocation systems).

6. Computational and Resource Constraints

• While conceptually scalable, deploying and maintaining swarms of highly trained agents with robust testing environments could require immense computational power and infrastructure, potentially leading to carbon footprint concerns or centralization of AI access.

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Conclusion

The idea of using AI agent swarms to explore the design space of capital allocation is compelling and aligns well with assembly theory principles. Its primary strengths lie in speed, scalability, and the ability to uncover novel insights. However, the approach comes with risks, especially in alignment, ethical considerations, and practical implementation.

To mitigate these risks, a hybrid approach could be explored:

1. Human-in-the-Loop Design: Use AI swarms for initial ideation and simulation, but involve human experts to refine, validate, and implement the most promising ideas.
2. Transparent Agent Behavior: Equip AI agents with explainability features to build trust and understanding of their outputs.
3. Iterative Development: Start with small-scale experiments and gradually scale the swarm’s role as its outputs prove valuable.

If deployed carefully, AI swarms could indeed revolutionize how we explore and implement capital allocation for public goods funding. What do you think about integrating human oversight and iterative validation into this vision?

[Oba-One]

Working on one for my project WEFA and the Greenpill Dev Guild super fun!

[owocki]

my vision: build an (1) infinite backroom of very smart regen ai agents (2) give them twitter access so ppl on CT can interact with them / learn form them/ they can learn from them (3) have the agents mine the latent design space of web3 public goods funding/onchain capital allocation 24/7/365 and are surfacing new insights that humans would never come across bc LLMs have more context and are working 24/7/365. also (4) anyone can participate by training/launching a regen ai (so its decdentralized not just an insiders club)

at first, i want this swarm to just be a smart thought parnter. eventually i want this swarm actually building web3 public goods funding tools itself. and shipping osftwar we can play with.

concise statement of vision: infiniteregen.ai, an ai agent swarm that is the smartest regen KOL out there (smarter than vitalik) and fastest/goodest regen builder out there too (better than the best human builder).

[Jimi]

Treegen AI will join the swarm!

[POPP]

This post is delicious.
Will dig it deeper soon.
I’m working on launching an Agent also.
I’m stuck after creating a .json on Elizagen nocode.
Let’s try to “code” a bit.

Would be great to talk about teaching and growing in our common direction.
Not sure how it work yet.

Can we share some informations about Eliza and you Agent ?

Which LLM did you choose ?
Any token launch in the same time ?
Is there any chain behind this AI16Z framework ?
not sure to have perfectly understood what is a framework.
neither where will be the interface and format for training
Thanks