Governance-as-Code: Managing Agentic AI with a Distributed Dual Proxy Gateway
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The rapid adoption of large-language models (LLMs) has shifted enterprise AI from model-centric experimentation to production-scale, policy-constrained deployments. Traditional “LLM-as-a-service” governance breaks down when LLMs are embedded in agentic execution loops that plan, act, observe, and adapt while calling external tools. This paper traces the architectural evolution from embedded wrappers and sidecar proxies to a multi-plane, dual-proxy gateway, in which lightweight edge proxies and heavyweight core proxies cooperate to provide low-latency guardrails, global policy enforcement, and verifiable attestation chains. We introduce a governance-as-code approach that compiles compliance workflows into WebAssembly (Wasm) modules. Edge proxies execute these modules, obtain cryptographic signatures from specialized microservices, and forward only fully attested prompts to the core proxy, which ultimately forwards them to the LLM. Micro-benchmarks show that Wasm-mediated validation adds ≤ 20 ns overhead for CPU-bound tasks and ≈ 120 ns when serializing complex data types negligible relative to LLM inference times. The design achieves auditable, decentralized governance with good performance, laying the groundwork for high-assurance, tool-using agentic AI in the enterprise.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it