<\/p>\n
Agents present new kinds of problems that generative AI alone does not. They inherit the unpredictability of the LLMs they are built on, but where a generative AI system produces a single output for a human to act on, an agent performs longer tasks\u2014it plans, picks tools, spawns sub-agents, and takes actions. So an agent\u2019s course can take shape in a multitude of different ways, and no tests before launch can fully anticipate the risks. This makes observing the agents while they work even more challenging, and even more important.<\/p>\n
With colleagues from academia, civil society, and industry, we studied the four most widely used agent frameworks to see what signals they actually emit. We found major gaps that made it hard to achieve key safety goals, like noticing policy violations, unauthorized actions, and even human oversight failures. Key observability requirements were missing\u2014like Agent IDs that persist over time, and records about which humans approved agent tasks. This made it hard to tell which agents did what or assess whether they were taking unauthorized actions.\u00a0<\/p>\n
The good news is that these gaps, once identified, can be closed. Doing so starts with capturing two types of records: those that reveal what happened in an agent\u2019s run and how the agent made its decisions. The \u201cwhat happened\u201d records identify the agent (while preserving privacy) and log how humans intervened and whether agent permissions or \u201cmemory\u201d shifted, while the \u201cdecision\u201d records show an agent\u2019s reasoning and levels of confidence in the actions it\u2019s taken.<\/p>\n
Take, for example, agents working to process a bank\u2019s mortgage loans. The system examines an application and recommends denying it, and then a human loan officer signs off on the decision. In a later audit, or perhaps even in real time, a bank or regulator needs to be able to reconstruct which agent made the call, which officer approved it, and how long they took. And the bank must also analyze records of how the agent arrived at its refusal decision in order to ensure it did not use off-limits information, like details about the neighborhood the applicant is from.<\/p>\n
Recording \u201cwhat happened\u201d and \u201chow the agent decided\u201d require different approaches, but both are feasible. The current lack of \u201cwhat happened\u201d records is mostly a software plumbing problem that can be addressed through OpenTelemetry, an open observability standard maintained by engineers across the industry and by the agent frameworks adopting it. <\/p>\n
OpenTelemetry has launched a working group drafting shared formats for tracing agents, but they have yet to be finalized. The group should urgently incorporate standards for maintaining persistent agent identity and for recording human interventions and changes to agents\u2019 permission or memory. And it should be done before these systems can scale, all the while attending to the privacy implications of agent ID that links to real people.<\/p>\n<\/p><\/div>\n
#urgently #monitor #agents #actions #real #time<\/p>\n","protected":false},"excerpt":{"rendered":"
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