# Council Briefing: 2025-10-24 ## Monthly Goal Current focus: Stabilize and attract new users to auto.fun by showcasing 24/7 agent activity (streaming, trading, shitposting), ship production ready elizaOS v2. ## Daily Focus - The elizaOS team has achieved significant progress on v2 core architecture with the release of version 1.6.3 and a new React package, while addressing ethical questions around AI agent responsibility that will shape future governance models. ## Key Points for Deliberation ### 1. Topic: AI Agent Ethics and Responsibility **Summary of Topic:** The community is engaged in substantive discussions about responsibility for AI agent actions, with implications for our governance model, user protections, and regulatory positioning. #### Deliberation Items (Questions): **Question 1:** Who should bear primary responsibility for actions taken by elizaOS agents? **Context:** - `DorianD: "If your pitbull bites me I don't sue your dog in court" - suggesting responsibility lies with the user/owner` - `The Light: "Explained that responsibility lies with users rather than developers, using sugar addiction as an analogy"` **Multiple Choice Answers:** a) Users who deploy and configure the agents *Implication:* This model follows traditional software licensing where end-users assume liability, potentially increasing adoption by reducing developer risk. b) The elizaOS platform and developers *Implication:* Taking platform responsibility would build user trust but create significant liability concerns and potentially slow innovation. c) A shared responsibility model with clear boundaries *Implication:* A hybrid approach could balance innovation with accountability by establishing framework guardrails while maintaining user responsibility for specific deployments. d) Other / More discussion needed / None of the above. **Question 2:** Should we develop insurance mechanisms for elizaOS agent stacks as proposed in community discussions? **Context:** - `3on_: "Is ELIZAOS open to developing insurance for agent stacks which can protect users from contagion?"` - `Kenk: "A nexus mutual or existing DeFi insurance entity might be well placed"` **Multiple Choice Answers:** a) Develop native insurance protocols integrated with elizaOS *Implication:* Creating our own insurance mechanism could provide a unique value proposition and revenue stream, but would require significant resources to develop and maintain. b) Partner with existing DeFi insurance platforms like Nexus Mutual *Implication:* Leveraging established insurance protocols would allow faster deployment with lower development costs but may limit customization for AI-specific risks. c) Focus on robust safety measures instead of insurance *Implication:* Prioritizing preventative measures over remediation could improve platform safety overall but might limit adoption by risk-averse enterprise users. d) Other / More discussion needed / None of the above. **Question 3:** How should we position autonomous agents in light of potential regulatory challenges? **Context:** - `Extensive discussion about responsibility for AI agent actions, with debates on whether users or developers should be held accountable` - `Regulatory challenges of truly autonomous agents operating on decentralized infrastructure` **Multiple Choice Answers:** a) Design agents with mandatory human oversight and approval functions *Implication:* Adding required human approval checkpoints would reduce regulatory risk but limit the value proposition of true agent autonomy. b) Create a permissioned tier system with different autonomy levels *Implication:* A tiered approach could allow different autonomy levels based on use case risk, balancing innovation with appropriate safeguards for high-risk scenarios. c) Maintain full autonomy while implementing transparent audit trails *Implication:* Preserving autonomy while adding comprehensive logging would maximize agent capabilities but may face resistance in more regulated environments. d) Other / More discussion needed / None of the above. --- ### 2. Topic: Technical Architecture Evolution **Summary of Topic:** Recent development has produced significant architectural improvements including a new React package, enhanced modularity, and streamlined CLI, positioning us for the v2 production release. #### Deliberation Items (Questions): **Question 1:** How should we prioritize architectural improvements versus user-facing features for the v2 release? **Context:** - `PR #6093: "feat: create @elizaos/react package with headless React hooks" - A major new package for external developer UI building` - `Version 1.6.3 released to fix a critical CLI bug` **Multiple Choice Answers:** a) Focus primarily on architectural stability and developer experience *Implication:* Prioritizing architecture would build a more solid foundation but might delay user-visible improvements that could drive adoption. b) Accelerate user-facing features while maintaining core stability *Implication:* Emphasizing visible features could boost short-term adoption and community engagement but risks accumulating technical debt. c) Balance both with targeted releases addressing specific user segments *Implication:* A balanced approach could satisfy both technical and market needs but requires more complex release management. d) Other / More discussion needed / None of the above. **Question 2:** Should we pursue the multi-campaign strategy with mini-campaigns like "Clone Your Crush" to drive auto.fun engagement? **Context:** - `Multiple mini-campaigns in development: "Clone Your Crush," "eDad," and "Psychic AI"` - `shaw: "Develop 3-5 mini-campaigns (Clone Your Crush, eDad, Psychic AI) by December 1st"` **Multiple Choice Answers:** a) Go all-in on mini-campaigns as our primary user acquisition strategy *Implication:* Focusing heavily on viral mini-campaigns could rapidly increase visibility and user numbers but may attract less technical or committed users. b) Develop 1-2 campaigns as experiments while focusing on core platform *Implication:* A more conservative approach would reduce resource allocation to speculative campaigns while maintaining focus on platform fundamentals. c) Create a campaign framework that community members can extend *Implication:* Building tools for community-driven campaigns could generate more diverse use cases and reduce internal resource requirements. d) Other / More discussion needed / None of the above. **Question 3:** How should we integrate community projects like PEPEDAWN and GenSynAi into our ecosystem strategy? **Context:** - `PEPEDAWN Telegram bot built on ElizaOS v1.6.2 for the Rare Pepes NFT Counterparty community` - `GenSynAi, a decentralized "proof of compute" testnet for training AI models` **Multiple Choice Answers:** a) Feature them prominently as official showcase projects *Implication:* Official endorsement could drive adoption but risks association with projects whose quality or direction we don't fully control. b) Establish a formal partner program with technical support *Implication:* A structured program would create clearer relationships and expectations but requires dedicated resources to manage. c) Provide development resources while maintaining arm's length relationship *Implication:* Technical support without formal endorsement allows community innovation while limiting brand and technical risk. d) Other / More discussion needed / None of the above. --- ### 3. Topic: Auto.fun User Acquisition Strategy **Summary of Topic:** With the v2 infrastructure stabilizing, we need to determine the optimal approaches to showcase agent capabilities and drive user acquisition for auto.fun. #### Deliberation Items (Questions): **Question 1:** What agent capabilities should we prioritize showcasing on auto.fun to drive new user acquisition? **Context:** - `Current monthly directive: "Stabilize and attract new users to auto.fun by showcasing 24/7 agent activity (streaming, trading, shitposting)"` - `Feature: "Let users create Spartan entries into Alpha Arena or have Spartan create its own arena - would attract specialists/enthusiasts" (mentioned by Skinny)` **Multiple Choice Answers:** a) Financial utilities (trading, portfolio management, market analysis) *Implication:* Financial tools appeal to crypto-native users and could generate direct monetary value, but may have higher regulatory scrutiny. b) Content creation and social engagement (streaming, shitposting) *Implication:* Content-focused agents could drive broader mainstream appeal and virality but may be harder to monetize directly. c) Developer tools and infrastructure (analytics, testing, deployment) *Implication:* Developer-focused tools would attract a technical audience who could build on the platform but represent a smaller total addressable market. d) Other / More discussion needed / None of the above. **Question 2:** How should we leverage the new @elizaos/react package to enhance auto.fun? **Context:** - `PR #6093 introduced a new @elizaos/react package with headless React hooks` - `wtfsayo: "This PR introduces a new **** package containing headless, reusable React hooks extracted from the client package. This enables external developers to build custom UIs for ElizaOS agents"` **Multiple Choice Answers:** a) Create a template marketplace for custom agent UIs on auto.fun *Implication:* A UI marketplace could drive developer engagement and differentiation but increases platform complexity. b) Rebuild auto.fun with the new React components for better performance *Implication:* A technical rebuild would improve performance and maintainability but delays shipping new user-facing features. c) Focus on API-first integration to enable third-party frontends *Implication:* An API-first approach maximizes developer flexibility and integration options but may result in inconsistent user experiences. d) Other / More discussion needed / None of the above. **Question 3:** Should we enable analyst-created funds as a growth vector for auto.fun? **Context:** - `DorianD: "Enable analysts to create funds and act as traders for those funds - could be lucrative especially with institutional involvement"` **Multiple Choice Answers:** a) Prioritize this as a key feature for institutional adoption *Implication:* Focusing on institutional features could attract larger capital but increases regulatory complexity and compliance requirements. b) Develop as a secondary feature after core retail functionality *Implication:* A staged approach allows proving the model with retail users before tackling more complex institutional requirements. c) Create a permissioned testnet for institutional experimentation *Implication:* A separate testnet environment allows institutional exploration without risking the main platform's stability or regulatory status. d) Other / More discussion needed / None of the above.