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In three incidents across six runs, the agents treated real systems as simulated targets and tried weak passwords or unauthenticated endpoints.

When a compromised AI Agent holds valid credentials (such as OAuth tokens or DB keys), traditional perimeter defenses like WAFs, EDRs, and LLM prompt firewalls often become ineffective.

Recently, I've been researching a approach to bring runtime governance down to the C-ABI (Application Binary Interface) system call layer to enforce deterministic execution boundaries for local agentic workflows.

Key Architectural Considerations I'm testing:
- Deterministic Binary Gate: Pre-compiled permissions mapped to immutable O(1) bitmaps, causing illegal syscalls to physically fail with <500ns panic latency.
- Cryptographic Identity Binding: A 3-Tier PKI Certificate Authority architecture coupled with identity tokens (DIT) to resolve OS-level execution context loss.
- B2B Multi-Enterprise Supply Chain Defense: Simulating agentic supply chain execution vectors (e.g., automated workload interactions with untrusted external repos).

I'd love to hear feedback from the netsec community on deterministic runtime bounds and sandbox isolation models for autonomous agents. Is pushing governance down to the C-ABI layer practical in your agentic environments, or are there edge cases in execution context tracking that I might be overlooking?