Doing market research nowadays, the biggest fear is AI quoting data that's six months old as if it's gospel. That's why many applications are starting to feed their models live web searches, price feeds, and various tools.
But there's a rarely discussed contradiction here: while AI may have the latest info, users often still don’t know what it actually searched for, which tools it called, and if anything was altered along the way.
The model itself is verifiable, but if the external searches and tool calls remain a black box, then that whole chain is only half-verified.
My take is that future research Agents need to not only prove "this sentence was generated by a specific model," but also string together the processes of how they acquired information, chose tools, and formed conclusions.
OpenGradient's LLM SDK already supports tool calls and native web searches, executing requests in a TEE path. This functionality makes a lot of sense in practical workflows.
For instance, if I instruct a research Agent to analyze a specific sector, it first searches for the day's news, then calls a price tool to fetch market data, and finally combines both sets of information to output a judgment. The model requests, system prompts, and final results are all verifiable, plus it can return payment records, ensuring that this analysis indeed followed a specified reasoning path.
Developers can install the Python SDK, prepare their $OPG Base wallet, and complete authorization, then use the `chat` interface to access tools or web searches. Once the results are out, they can also check relevant records in the OpenGradient browser.
This application is quite practical for market daily reports, contract analysis, and risk monitoring, because these tasks are most concerned not with whether AI can speak, but rather whether it’s confidently using outdated data.
Of course, verifying the execution process doesn’t guarantee that the web content is accurate. Search results can still be wrong, and unreliable sources won’t magically become trustworthy just by entering a TEE, plus online searches can rack up call costs.
So I wouldn’t interpret this as "AI research finally getting it right." It’s more like taking a step forward in making the previously invisible research process more transparent: at least we know it really did conduct the searches and made the calls, without being tampered with on the return journey.
$OPG @OpenGradient #OPG
But there's a rarely discussed contradiction here: while AI may have the latest info, users often still don’t know what it actually searched for, which tools it called, and if anything was altered along the way.
The model itself is verifiable, but if the external searches and tool calls remain a black box, then that whole chain is only half-verified.
My take is that future research Agents need to not only prove "this sentence was generated by a specific model," but also string together the processes of how they acquired information, chose tools, and formed conclusions.
OpenGradient's LLM SDK already supports tool calls and native web searches, executing requests in a TEE path. This functionality makes a lot of sense in practical workflows.
For instance, if I instruct a research Agent to analyze a specific sector, it first searches for the day's news, then calls a price tool to fetch market data, and finally combines both sets of information to output a judgment. The model requests, system prompts, and final results are all verifiable, plus it can return payment records, ensuring that this analysis indeed followed a specified reasoning path.
Developers can install the Python SDK, prepare their $OPG Base wallet, and complete authorization, then use the `chat` interface to access tools or web searches. Once the results are out, they can also check relevant records in the OpenGradient browser.
This application is quite practical for market daily reports, contract analysis, and risk monitoring, because these tasks are most concerned not with whether AI can speak, but rather whether it’s confidently using outdated data.
Of course, verifying the execution process doesn’t guarantee that the web content is accurate. Search results can still be wrong, and unreliable sources won’t magically become trustworthy just by entering a TEE, plus online searches can rack up call costs.
So I wouldn’t interpret this as "AI research finally getting it right." It’s more like taking a step forward in making the previously invisible research process more transparent: at least we know it really did conduct the searches and made the calls, without being tampered with on the return journey.
$OPG @OpenGradient #OPG