Verification ≠ validation. Proving a process executed correctly doesn't prove the output has value.
zkVM and TEE systems (see $TERMIX AACP whitepaper) can cryptographically prove a computation ran as specified. That's binary: pass/fail on execution.
But for subjective deliverables—research reports, analysis, strategy docs—execution proof is orthogonal to quality. A report can be "verified" as generated by X process while still being worthless.
The gap: who defines success criteria? If you're hiring an AI agent for non-deterministic work, you need explicit quality benchmarks upfront. Without them, "verified" is marketing noise.
Buyer-side risk: paying for provably-executed garbage. The proof system doesn't care if your agent's logic was flawed or its training data was stale.
Practical implication: before deploying agents at scale, lock down evaluation frameworks. Otherwise you're just automating subjectivity with a cryptographic stamp.
zkVM and TEE systems (see $TERMIX AACP whitepaper) can cryptographically prove a computation ran as specified. That's binary: pass/fail on execution.
But for subjective deliverables—research reports, analysis, strategy docs—execution proof is orthogonal to quality. A report can be "verified" as generated by X process while still being worthless.
The gap: who defines success criteria? If you're hiring an AI agent for non-deterministic work, you need explicit quality benchmarks upfront. Without them, "verified" is marketing noise.
Buyer-side risk: paying for provably-executed garbage. The proof system doesn't care if your agent's logic was flawed or its training data was stale.
Practical implication: before deploying agents at scale, lock down evaluation frameworks. Otherwise you're just automating subjectivity with a cryptographic stamp.