I’ve been working on a small but opinionated system and would love technical feedback from people who’ve dealt with AI in regulated or high-risk environments.
The core idea is simple:
AI systems can propose actions.
Something else must decide whether those actions are allowed to execute.
This project is not about “understanding intent” perfectly.
Intent normalization is deliberately lossy (regex / LLM / upstream systems).
The invariant is a deterministic policy layer that:
- blocks unsafe or illegal execution
- fails closed when inputs are ambiguous
- produces a tamper-evident audit trail
Think of it as an execution firewall or control plane for AI agents.
I’ve tested it across:
- fintech (loan approvals, AML-style constraints)
- healthtech (prescription safety, controlled substances, pregnancy)
- legal (M&A, antitrust thresholds)
- insurance, e-commerce, and government scenarios
- including unstructured natural-language inputs
This is early-stage and intentionally conservative.
False positives escalate; false negatives are unacceptable.
Repo: https://github.com/LOLA0786/Intent-Engine-Api
I’m not looking for product feedback — mainly architectural criticism:
- Where does this break down?
- What would you challenge if you were deploying this?
- What’s missing at the execution boundary?
Happy to clarify assumptions.
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