Karthik, your point about the trade-off between scheduled agents and continuous reasoning strikes at the heart of why so many digital transformation projects in insurance remain stagnant.
When we prioritize 'auditable' scheduled agents, we are often defaulting to Procedural Auditing - proving that a set of tasks was performed on a specific cadence. It’s easier for regulators to digest because it looks like the legacy checklists they’ve seen for decades. But as anyone in a regulated industry knows, following a procedure is not the same thing as enforcing a policy. You can follow every 'step' of a claims process and still miss the nuanced intent of the underlying insurance contract.
The 'AI-Native' drive requires us to move past reinventing these linear paths and start re-imagining them.
If we trust a model with continuous context (the Anthropic bet), the audit trail shouldn't be a list of timestamps. It should be a logic map: 'The AI reached this decision because it synthesized X regulatory requirement with Y specific customer context.' The gap isn't just in the technology; it’s in our willingness to move from auditing compliance with a routine to auditing alignment with an outcome.
Agreed Banu. Agentic traces are evolving to becoming one way to provide the auditable mechanism. What’s going to be a challenge is breaking though the layers of legacy thinking and creating the right scaffolding around a non-deterministic technology. Also, it’s an interesting thought experiment to consider a single outcome of such alignment potentially having nuances that don’t matter today yet could matter tomorrow.
Karthik, your point about the trade-off between scheduled agents and continuous reasoning strikes at the heart of why so many digital transformation projects in insurance remain stagnant.
When we prioritize 'auditable' scheduled agents, we are often defaulting to Procedural Auditing - proving that a set of tasks was performed on a specific cadence. It’s easier for regulators to digest because it looks like the legacy checklists they’ve seen for decades. But as anyone in a regulated industry knows, following a procedure is not the same thing as enforcing a policy. You can follow every 'step' of a claims process and still miss the nuanced intent of the underlying insurance contract.
The 'AI-Native' drive requires us to move past reinventing these linear paths and start re-imagining them.
If we trust a model with continuous context (the Anthropic bet), the audit trail shouldn't be a list of timestamps. It should be a logic map: 'The AI reached this decision because it synthesized X regulatory requirement with Y specific customer context.' The gap isn't just in the technology; it’s in our willingness to move from auditing compliance with a routine to auditing alignment with an outcome.
Agreed Banu. Agentic traces are evolving to becoming one way to provide the auditable mechanism. What’s going to be a challenge is breaking though the layers of legacy thinking and creating the right scaffolding around a non-deterministic technology. Also, it’s an interesting thought experiment to consider a single outcome of such alignment potentially having nuances that don’t matter today yet could matter tomorrow.