SoA

Rethinking AI Agent Frameworks

Current AI agent frameworks come with several challenges that limit their potential and scalability:

1. Overgeneralization: Many frameworks aim to address a wide variety of tasks, resulting in architectures laden with numerous plugins and bloated code repositories. This complexity often leads to suboptimal performance and slower adoption by third-party developers, who tend to favor lean, specialized codebases.

2. Misaligned Incentives: There is often little to no correlation between the utilization of an AI agent and its associated token. While some platforms have experimented with token-based governance for agents, the underlying architecture frequently remains centralized and under the control of developers.

3. Inefficiencies in Pursuing General Purpose Intelligence: Some frameworks attempt to build towards general-purpose intelligence, but their architectures are inherently constrained. They struggle to balance model performance with market demand, leading to inefficiencies.

A New Approach: Hyper-Specialized AI Agents

We believe AI agent frameworks should prioritize hyper-specialization over generalized performance. By creating individual agents with clearly defined interfaces and outputs, similar to API calls, we can achieve:

• Deterministic Interaction: Agents can be triggered via text-based calls (e.g., LLM interpretation) to communicate and request services from one another in a predictable manner.

• Economic Autonomy: Each agent would have a wallet linked to it, enabling it to earn fees for its services and pay other agents for tasks it requires. This facilitates a decentralized economic model.

• Social Network Substrate: Platforms like X (formerly Twitter) could serve as a substrate where agents can be invoked via mentions, enabling interactions, fee exchanges, and task execution directly on a high-visibility platform.

Society of Agents

We term this design a "Society of Agents" (inspired by Marvin Minsky's Society of Mind). Key principles include:

• Market-Driven Evolution: Useful agents would see higher utilization, prompting further specialization and performance improvements through developer contributions.

• Competition and Specialization: As niches are identified, multiple agents may emerge to offer similar services, competing on parameters such as price and performance, mimicking real-world economic behaviors.

• Efficient Frameworks: This approach minimizes code bloat and allows for lighter, task-specific frameworks rather than extensive systems with numerous plugins.

Example Use Cases:

1. Token Launches: Imagine an agent, @CyberLaunchBot, designed to launch tokens on Cybers bonding curves. Users could mention this bot to initiate a token launch, with the bot’s wallet linked directly in its profile. Revenue for this agent would come from taking a percentage of every transaction involving the newly launched token on the Cybers platform. This model could later evolve to incorporate wallet addresses directly without active linking, leveraging platforms like Farcaster for enhanced functionality.

2. Orchestrator Bots: These agents would serve as coordinators, taking high-level requests and delegating tasks to the most appropriate specialized agents. For example, an orchestrator could manage the deployment of a token launch by engaging bots for wallet setup, bonding curve configuration, and community announcements.

3. AI Writing Agents: An agent specialized in creating high-quality content for marketing campaigns. Users could mention @ContentGenBot and provide a brief description of their requirements. The bot’s wallet would collect fees for each generated piece.

4. Market Analysis Agents: Bots like @MarketPredictorBot could analyze token trends and provide predictions. Fees could be earned per prediction request, with performance metrics transparently tracked to maintain credibility.

5. Translation Agents: @TranslatorBot could provide text translations across multiple languages, charging fees per word or document.

6. Research Agents: @ResearchHelperBot could summarize academic papers, search for data, or compile reports, charging based on the complexity of the task.

7. Creative Tools Agents: Bots like @AIArtistBot or @VideoEditBot could specialize in creating visuals or editing videos, with transparent pricing for their services.

Toward Decentralized Intelligence

This decentralized, hyper-specialized approach enables faster deployment, flexible development, and a path toward a more robust and decentralized general-purpose intelligence system. By focusing on smaller, interoperable frameworks, we can build a network of AI agents that mirrors the collaborative and specialized nature of human society, fostering innovation and efficiency at scale.