How we might build defensible AI agents just became more concrete
"AWS's Byron Cook claims this approach can catch 'nearly 100% of all hallucinations.'"
Imagine the workflow for a new AI agent that operates in a regulated function. The first input isn't a user prompt, but the team's meticulously crafted policy document—the rulebook for compliance. This document becomes a core part of the product architecture, not just a reference file.
This is the process suggested by AWS’s move to make its Automated Reasoning Checks generally available, as detailed in VentureBeat. When a user query comes in, the LLM generates a response. But before that response is finalized, the system runs a formal verification. It uses mathematical proofs to check if the answer is logically consistent with the rules you embedded in Bedrock Guardrails. AWS's Byron Cook claims this approach can catch "nearly 100% of all hallucinations."
For product teams, this means the output is more than just an answer; it's an answer that comes with a validation receipt. It’s a practical application of neurosymbolic AI, using the neural net for its powerful pattern-matching and a symbolic system to enforce hard constraints. So for us, the compliance conversation shifts. Instead of trying to measure and mitigate bad outputs after the fact, we can design the system to provably prevent them from happening in the first place, based on the rules we define.
