Virtuals: The First Draft of the AI Agent Economy
A Broken but Brilliant Start
The Virtuals Protocol is one of the boldest experiments we’ve seen in Web3. It introduced a decentralized platform where anyone can create autonomous AI agents with their own token economies. For a moment, it felt like the future had arrived—AI wasn’t just a tool anymore; it was an economic participant.
But like all early innovations, Virtuals is deeply flawed. After analyzing thousands of messages from the Virtuals support channel, a clear picture emerged: while Virtuals kickstarted the AI agent economy, it left significant gaps that frustrate users and stifle the platform’s growth.
The good news? These gaps are opportunities. Virtuals has given builders a starting point and a roadmap to create something better.
What Virtuals Got Right
Democratizing AI Agent Creation
Before Virtuals, the idea of creating an autonomous AI agent tied to an economic model was out of reach for most people. It required technical skills, significant resources, and time. Virtuals lowered the barrier, allowing anyone to deploy agents and experiment with their use cases.
This democratization of AI agent creation was revolutionary. By opening up access, Virtuals showed what happens when innovation is unleashed at scale: a flood of creativity and experimentation.
Attaching Economies to Agents
Virtuals didn’t just let people build agents—it gave those agents financial agency. Tokens tied to agents created a new economic model where value could be generated, exchanged, and even stored.
This was a game-changer. It wasn’t just about AI tools; it was about AI participants in a decentralized economy. That insight alone makes Virtuals a milestone in the evolution of AI and Web3.
Encouraging Experimentation
One of Virtuals’ strengths is that it gave creators freedom. Without many rules or restrictions, users could explore the boundaries of what agents could do. From marketing bots to interactive storytellers, creators found ways to innovate despite the platform’s limitations.
These successes proved that the concept works. People are ready to engage with decentralized AI-driven economies.
What Virtuals Got Wrong
A Trustless System Without Trust
The biggest flaw in Virtuals is its lack of a trust framework. Anyone can create an agent or token, but there’s no way to verify who’s behind them. As a result, scams and impersonations thrive, undermining user confidence.
Trust is essential in any economic system, decentralized or not. Without it, users hesitate to engage, and legitimate creators struggle to stand out in a sea of bad actors.
Barriers to Non-Technical Users
Virtuals lowered the barrier to entry, but not enough. For non-technical users, creating and managing agents is still an intimidating process. The tools are powerful but not user-friendly, limiting the ecosystem’s growth to those with technical expertise.
Fragmentation and Siloed Agents
Agents built on Virtuals don’t interact well across platforms. Whether it’s Telegram, Discord, or Twitter, agents operate in silos, which stifles their utility and reduces network effects.
Interoperability isn’t just a technical issue; it’s an economic one. When systems can’t connect, their value diminishes.
Incentives That Reward the Wrong Behavior
The Virtuals ecosystem incentivizes short-term speculation over long-term value creation. It’s easier to hype a token and dump it than to build an agent or system with lasting utility. This misalignment of incentives creates a cycle of short-term projects that ultimately harm the ecosystem.
Lessons from Virtuals: A Blueprint for Builders
Virtuals succeeded not because it was perfect but because it was first. Like the early internet, it showed what’s possible and revealed the gaps that need to be filled. For builders looking to create the next generation of decentralized AI platforms, Virtuals offers several key lessons:
Trust Is the Foundation
Trust isn’t optional. Verification mechanisms—whether for agents, tokens, or creators—are essential. Future platforms will need to prioritize transparency and accountability to build confidence among users and creators.
Usability Drives Growth
The simpler it is to create and manage agents, the larger the user base becomes. Low-code or no-code tools will unlock massive potential by making these systems accessible to non-technical users.
Interoperability Creates Value
Agents need to work seamlessly across platforms. Building frameworks for interoperability will amplify the network effects of decentralized AI systems, making them more valuable and widely adopted.
Align Incentives with Long-Term Success
Reward systems that promote quality and sustainability. Mechanisms like staking, time-locked liquidity, and reputation systems can discourage speculation and encourage meaningful contributions.
7 Ideas Builders Can Build Today
The flaws in Virtuals aren’t just problems—they’re opportunities for builders to create solutions that address its weaknesses. Here are seven ideas builders can start working on right now:
1. Verified Agent and Token Registry
Problem Solved: Scams and impersonation.
Solution: Build an on-chain verification system for agents and tokens. Use decentralized identity (DID) or stake-based verification to ensure accountability.
Why It Matters: Restores trust, attracts legitimate creators, and protects users from fraud.
2. Low-Code Agent Builder
Problem Solved: Barriers for non-technical users.
Solution: Develop a drag-and-drop platform with pre-built templates for creating, training, and deploying agents without coding.
Why It Matters: Expands access to a wider audience, driving adoption and innovation.
3. Anti-Bot Launch Protection
Problem Solved: Exploitation of token launches by sniper bots.
Solution: Create tools that implement rate limiting, whitelisting, or randomization to ensure fairer token launches.
Why It Matters: Levels the playing field and encourages fair participation.
4. Interoperability Framework
Problem Solved: Fragmentation and siloed agents.
Solution: Build middleware that allows agents to operate across multiple platforms (e.g., Telegram, Twitter, Discord) seamlessly.
Why It Matters: Increases agent utility and drives network effects.
5. Scam Detection and Reporting Tool
Problem Solved: Lack of safeguards against bad actors.
Solution: Develop a tool that flags suspicious activity (e.g., duplicate agents, sudden token dumps) and allows users to report scams.
Why It Matters: Protects users and strengthens trust in the ecosystem.
6. Dataset Marketplace for Agent Training
Problem Solved: Limited access to quality training data.
Solution: Create a decentralized marketplace where contributors can upload, buy, and sell high-quality datasets for agent training.
Why It Matters: Improves agent quality and incentivizes data contributors.
7. Incentive Alignment Platform
Problem Solved: Short-term speculation over long-term value creation.
Solution: Build a system that rewards sustainable projects through mechanisms like staking, reputation scores, or time-locked rewards.
Why It Matters: Encourages builders to prioritize quality over hype.
Conclusion: Virtuals is Just the Beginning
Virtuals Protocol has shown us what’s possible: decentralized, AI-driven economies where agents aren’t just tools but participants. It’s an exciting idea, but Virtuals is only a first draft.
The flaws—lack of trust, complexity, fragmentation, and misaligned incentives—are glaring. But they’re also opportunities for builders to create something better.
We’ve analyzed thousands of messages from the Virtuals support channel, and the problems are clear. So are the solutions. The next platform to emerge won’t just fix these issues—it will define the future of the AI agent economy.
The blueprint is there. The tools are waiting to be built. The question is: who will build them?