Michael best

AI agents are moving beyond answering questions. They are increasingly being designed to interact with external platforms, retrieve information, execute instructions, and perform financial operations. This creates exciting opportunities, especially within DeFi and automated digital services. But there is an important question we cannot ignore: How do we know an AI agent actually performed the actions it claims to have performed? Getting a convincing final answer is not enough. Imagine an AI agent instructed to check a price oracle before executing a trade. It might return a result that looks correct, but how can a user or smart contract confirm that the required oracle was actually queried? What if the agent skipped an important step, used outdated information, or returned a manipulated result? This is the execution verification problem highlighted in the @primus_labs documentation. Primus explores how zkTLS can make important API interactions during AI agent execution verifiable. The idea is to generate cryptographic proofs showing that a particular HTTPS request was made and that the server returned a specific response. These proofs can capture important details such as the endpoint, relevant request parameters, and key response values without revealing private credentials or unnecessary session information. For example, an AI agent operating under a particular execution policy may be required to query a specified oracle before making a trade. At the relevant stages, the agent can generate proofs of the required API interactions. Those proofs can then be checked by a verification contract or an off-chain verification service. The system can evaluate whether the required steps were completed in the correct order before accepting the agent's final output. This changes the focus from simply trusting what an AI agent says to examining evidence of what it actually did. I believe this distinction will become increasingly important as autonomous systems handle more valuable operations. AI agents need more than intelligence. They need accountability, verifiable execution, and systems that make their important actions easier to audit. By applying zkTLS to agent execution, @primus_labs is exploring an important foundation for more trustworthy AI-powered applications. The future of AI should not depend on blind trust. It should be built around verifiable actions.