Adding an Accountability Layer to AI Agents
Special_Librarian145 · reddit · 2026-07-11
The author built an accountability layer primitive for AI agents called **Cinchor**, aiming to complete the "constrain first, audit later" paradigm. ### Core Mechanisms - **Ex-ante Constraints**: Issues restricted capabilities to individual agents upfront, including spend limits, allowlists, and expiration times. Any action is validated against these boundaries first, and unauthorized requests are rejected before execution. - **Ex-post Proofs**: Every decision is hashed, signed, and appended. This allows for later recalculation and verification to detect any tampered fields. ### Current Status & Usage - Provides a ready-to-run demo that takes about 30 seconds with no registration required. - Supports any agent stack, offering an API and TypeScript / Python / Go SDKs. - The code is Apache-2.0 licensed and involves no tokens, wallets, or gas. The author actively seeks community feedback on design flaws, potential overlaps with existing trust mechanisms, and scenarios where third-party intervention might be unnecessary.
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