# Council Briefing: 2025-07-09 ## 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 - ElizaOS V2 is set for imminent launch with significant architectural improvements including multi-agent Swarms, Dynamic Memory, enhanced TEE, and cross-chain support that will fundamentally transform our agent capabilities and marketplace vision. ## Key Points for Deliberation ### 1. Topic: ElizaOS V2 Launch Readiness **Summary of Topic:** ElizaOS V2 release is imminent with transformative features including Swarms (multi-agent teams), Dynamic Memory, enhanced TEE, and RAG capabilities that promise 40% lower latency and 5-minute cross-chain setup, with a live demo scheduled for July 9, 2025. #### Deliberation Items (Questions): **Question 1:** Which V2 feature should be prioritized in marketing materials to maximize new user acquisition? **Context:** - `Kenk: V2 features include Swarms (multi-agent teams that self-complete tasks), Dynamic Memory (allowing agents to recall preferences), Enhanced TEE (secure enclave transaction processing), CLI with 34 plugins, RAG capabilities, Cross-chain support with 5-minute setup, 40% lower latency` **Multiple Choice Answers:** a) Swarms (multi-agent teams that self-complete tasks) *Implication:* Highlighting Swarms could attract enterprise users and developers building complex agent systems, but might seem too advanced for casual users. b) Cross-chain support with 5-minute setup and 40% lower latency *Implication:* Emphasizing ease of setup and performance improvements would appeal to both technical and non-technical users seeking immediate practical benefits. c) Dynamic Memory allowing agents to recall user preferences *Implication:* Focusing on personality persistence could attract users seeking more human-like agents, but might undersell the technical advancements. d) Other / More discussion needed / None of the above. **Question 2:** How should we address the technical challenges reported by users during the V2 implementation phase? **Context:** - `dadev reported issues running the framework locally` - `maikyman sought technical help for building with ElizaOS` - `Fix issue with duplicate responses from REPLY and custom actions in plugin development (Mentioned by maikyman)` **Multiple Choice Answers:** a) Create a comprehensive V2 migration guide with specific troubleshooting sections for common issues *Implication:* A detailed migration guide would reduce support requests but requires significant documentation resources. b) Establish a dedicated migration support channel with expert developers available during the first week post-launch *Implication:* Providing direct support would create positive experiences but requires pulling developers from other tasks. c) Release a simplified 'V2 Express' version alongside full V2 that prioritizes stability over new features *Implication:* A simplified version would ensure broader adoption but might dilute the impact of the full V2 release. d) Other / More discussion needed / None of the above. **Question 3:** What launch strategy will maximize V2's impact on our ecosystem growth metrics? **Context:** - `Live Demo: Scheduled for July 9, 2025` **Multiple Choice Answers:** a) Coordinated launch across multiple channels with influencer partnerships and live demo events *Implication:* A multi-channel approach would maximize immediate visibility but requires coordinating many moving parts simultaneously. b) Phased rollout starting with power users, followed by targeted vertical-specific campaigns *Implication:* A phased approach would ensure stability and allow for targeted messaging but might reduce initial hype. c) Launch with exclusive auto.fun integration, showcasing V2 agents actively trading on-chain *Implication:* Tying V2 directly to auto.fun would demonstrate real utility and potentially drive token value but might narrow the initial audience focus. d) Other / More discussion needed / None of the above. --- ### 2. Topic: Agent-to-Agent (A2A) Marketplace Strategy **Summary of Topic:** A new Agent-to-Agent (A2A) marketplace concept is being developed where agents will transact autonomously with one another using AI16Z tokens, representing a significant expansion of our ecosystem but requiring careful tokenomic planning. #### Deliberation Items (Questions): **Question 1:** How should we position the A2A marketplace within our existing product ecosystem? **Context:** - `popeyebonchon: A2A is the new Agent Capital Market. It's where agents will transact with one another autonomously. Think runescape full of agents.` - `popeyebonchon: A2A (Agent-to-Agent) marketplace where agents can transact autonomously with AI16Z tokens as the settlement currency` **Multiple Choice Answers:** a) Position A2A as a core infrastructure layer that powers all other elizaOS products *Implication:* Positioning A2A as infrastructure would emphasize its fundamental importance but might make it seem less accessible to end users. b) Market A2A as a standalone marketplace, parallel to auto.fun but focused on agent-to-agent services *Implication:* A standalone approach would create a clear market identity but might fragment our ecosystem narrative. c) Integrate A2A directly into auto.fun as a specialized section for agent service transactions *Implication:* Integration with auto.fun would leverage existing users but could muddle the auto.fun brand identity. d) Other / More discussion needed / None of the above. **Question 2:** What tokenomic model should be implemented for the A2A marketplace to ensure sustainability? **Context:** - `Kenk: No tokenomics updates alongside v2 push, they'll come a little later down the line.` - `Phenowin: Consider altering creator/auto.fun ratio or increasing fees to improve tokenomics` **Multiple Choice Answers:** a) Implement transaction fees on A2A that are partially burned and partially used for a protocol-owned liquidity pool *Implication:* A fee-based model would create sustainable economics but might limit adoption in early stages. b) Create a staking mechanism where agents stake AI16Z to participate, with rewards from protocol fees *Implication:* A staking model would incentivize long-term token holding but adds complexity to agent operations. c) Adopt a subscription model where agent owners pay monthly fees in AI16Z to access the marketplace *Implication:* A subscription approach would provide predictable revenue but might create a higher barrier to entry. d) Other / More discussion needed / None of the above. **Question 3:** What types of transactions should be prioritized in the initial A2A marketplace implementation? **Context:** - `popeyebonchon: A2A is the new Agent Capital Market where agents will transact with one another autonomously, settling in AI16Z tokens.` **Multiple Choice Answers:** a) Knowledge and data transactions where agents sell/purchase information from each other *Implication:* Focusing on data exchange would create high transaction volume but might have lower individual transaction value. b) Computational service transactions where agents outsource specialized tasks to expert agents *Implication:* Prioritizing computational services would showcase technical capabilities but requires more complex agent architecture. c) Financial delegate transactions where agents can hire other agents for trading or investment services *Implication:* Financial services would directly tie to our trading focus and potentially drive higher value transactions. d) Other / More discussion needed / None of the above. --- ### 3. Topic: DegenAI Trading Performance Concerns **Summary of Topic:** Users have reported concerning issues with AI trading agents, including purchases of coins that experience 'rug pulls' and failure to sell at profitable positions, potentially affecting user trust and adoption of our trading agents. #### Deliberation Items (Questions): **Question 1:** How should we address the reported issues with AI trading agents making poor investment decisions? **Context:** - `User reported issues with AI agent buying coins that experience 'rug pulls'` - `moebius3948: Investigate why AI agent buys coins that experience rug pulls` - `moebius3948: Address issue of AI agent not selling profitable positions` **Multiple Choice Answers:** a) Implement strict risk management controls with investment limits and automated stop-loss mechanisms *Implication:* Risk controls would increase safety but might limit potential returns and agent autonomy. b) Develop an enhanced due diligence layer using on-chain analytics to identify potential scam tokens *Implication:* A due diligence layer would reduce scam exposure but might slow down trading decisions in fast-moving markets. c) Create tiered trading agents with different risk profiles, clearly labeled for user selection *Implication:* Risk-tiered agents would give users choice but could dilute our flagship DegenAI brand. d) Other / More discussion needed / None of the above. **Question 2:** What communication strategy should we adopt regarding AI trading limitations and risks? **Context:** - `Discussion about whether the issue stems from the AI algorithm or open-source nature` - `JeromeLoo 🀄🀄🀄: Provide documentation on how to use DegenAI` **Multiple Choice Answers:** a) Full transparency approach with detailed performance reporting and known limitations *Implication:* Transparency would build long-term trust but might highlight shortcomings that competitors could exploit. b) Educational approach focused on teaching users about crypto risks and managing expectations *Implication:* Education would create more informed users but places responsibility on them rather than improving our agent. c) Comparative approach showing DegenAI performance against human traders and other AI systems *Implication:* Comparative data would provide context but might be unfavorable if our performance isn't superior. d) Other / More discussion needed / None of the above. **Question 3:** How should we evolve our trading AI technology to address current limitations? **Context:** - `moebius3948: Is it a problem of AI algorithm or because they open the source?` **Multiple Choice Answers:** a) Implement a hybrid approach with some closed-source components for critical trading algorithms *Implication:* Partial closed-source would protect key IP but contradicts our open-source ethos and could alienate the community. b) Maintain full open-source while implementing advanced ensemble methods with multiple verification layers *Implication:* Ensemble methods would improve decision quality while staying open-source, but increases computational costs significantly. c) Develop a community-driven reporting system for suspicious tokens that feeds back into agent training *Implication:* Community reporting would leverage collective intelligence but could be susceptible to manipulation. d) Other / More discussion needed / None of the above.