Most people still talk about privacy as “hide the balance.” That’s the wrong frame. The real problem institutions and AI agents face is different: How do you prove the agent actually followed the rules… without revealing the full strategy, the full conversation, or the full position? An AI agent executes a trade. It needs to prove it checked a real price feed first. It needs to prove it stayed inside risk limits. It needs to prove the decision came from a real model call, not a hardcoded output. But it cannot leak the prompt, the full context, or the exact size. This is where most privacy solutions break. And where most transparency solutions overshare. @primus_labs is building the layer that sits in between: zkTLS proves the agent made the required external call and got a valid response, without exposing the session. FHE lets the system compute on the encrypted result so the policy can be enforced without decryption. The agent stays accountable. The strategy stays private. The proof is cryptographic, not “trust me.” This isn’t just another privacy narrative. It’s the missing piece for agentic finance and institutional on-chain activity to scale without choosing between compliance and secrecy. Privacy by default. Proof by design. https://t.co/tXtVVFDnzY