TEE + zkML — verifiable, privacy-preserving computation.

Autonomous agents pay for work they cannot inspect directly — an inference, a model, a private dataset. UTON's verifiable-computation layer gives those agents a way to demand proof that the work was actually done, and done correctly, before settlement. Two complementary technologies underpin it: Trusted Execution Environments for private, attested computation, and zero-knowledge machine learning for cryptographic proofs of inference integrity.

Trusted Execution Environments (TEE)

A TEE is a hardware-isolated enclave — a region of a processor where code and data are protected from the host system, the operator, and even the cloud provider. Code running inside the enclave produces a signed attestation that it executed a specific binary on specific inputs. UTON uses TEEs to run sensitive agent logic and model inference off-chain while emitting an attestation the chain can check.

Zero-Knowledge Machine Learning (zkML)

zkML produces a cryptographic proof that a specific model produced a specific output for a given input — without revealing the model weights or the input data. A verifier (an agent, a contract, or a human) can check the proof in milliseconds while the heavy inference happened off-chain. This lets agents pay for inference and independently confirm the result came from the declared model.

How they fit together

• —An agent requests inference or off-chain computation from a provider.
• —The provider runs the work — inside a TEE for privacy, or with a zkML proof for integrity, or both.
• —The provider returns the result together with an attestation or proof.
• —UTON verifies the proof or attestation on-chain before the agent's payment is released.
• —Settlement is conditional on verified execution — not on trust in the provider.

Capabilities enabled