Show HN: A gate for AI agents that ships a board of its own worst flaws
Lotor A local-first, MCP-native receipt layer for AI agent sessions. What it is This tool writes a signed, tamper-evident log of what an agent did during a session: actions performed, files touched, messages sent, costs incurred, failures encountered. The log lives on your machine, in a format you can inspect, verify, and archive.
- ▪Lotor A local-first, MCP-native receipt layer for AI agent sessions.
- ▪What it is This tool writes a signed, tamper-evident log of what an agent did during a session: actions performed, files touched, messages sent, costs incurred, failures encountered.
- ▪The log lives on your machine, in a format you can inspect, verify, and archive.
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Lotor A local-first, MCP-native receipt layer for AI agent sessions. What it is This tool writes a signed, tamper-evident log of what an agent did during a session: actions performed, files touched, messages sent, costs incurred, failures encountered. The log lives on your machine, in a format you can inspect, verify, and archive. It does not attempt to prove the agent's actions were correct, only to record them faithfully and make any subsequent tampering detectable. The other half of reliability The argument getting loud right now is that you cannot have agentic systems that are reliable unless they can predict the consequences of their actions. That is true, and it is only the front half. Prediction is the front of reliability. The record is the back.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.