It all started on June 23rd with a casual post about a VPS Manager benchmark.
Out of curiosity, I decided to ask the author of the benchmark,
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Here is the core logic from gatekeeper.rs that classifies and verifies LLM-generated code in an isolated environment before committing it to the codebase:
// gatekeeper.rs — Gatekeeper Hybrid LLM Router & Sandbox Verifier
pub enum LlmRoute {
CloudSwarm, // High-complexity planning (GPT-4o/Claude 3.5)
LocalAgent, // Low-complexity execution (Qwen-Coder-0.5B)
}
pub fn classify_query(query: &str) -> LlmRoute {
let q_lc = query.to_lowercase();
if q_lc.contains("architecture") ||
q_lc.contains("blueprint") ||
q_lc.contains("refactor kernel")
{
LlmRoute::CloudSwarm
} else {
LlmRoute::LocalAgent
}
}
// Returns Vec<f32> representing the token activation states (the embedding vector)
// rather than raw bytecode, laying the groundwork for semantic clustering in Part 10.
pub fn route_and_generate(query: &str, site_map: &crate::nda_jit::SiteMap) -> Result<Vec<f32>, &'static str> {
let route = classify_query(query);
match route {
LlmRoute::CloudSwarm => {
// Plan via high-capacity cloud swarm...
generate_bytecode_from_prompt(&format!("/* Cloud Swarm: {query} */"), site_map)
}
LlmRoute::LocalAgent => {
// Direct generation via local model...
generate_bytecode_from_prompt(query, site_map)
}
}
}
This security gate raised the floor for any model running through the pipeline. It was no longer about finding the most "secure" model—it was about building an infrastructure that forced security by construction.
But as the agent continued generating files, I hit another wall: context bloat. The context accumulation of self-correction was costing me valuable seconds and tokens.
In the next post, I'll detail how I tamed the context monster by inventing a new binary format and a multi-agent debate board.
Discussion
How are you all handling LLM "scope failures" in your local agents? Do you prefer prompt engineering or, like me, a hard-coded "Gatekeeper"? Have you noticed your LLM-generated code taking "security shortcuts" like this? I'd love to hear how you're validating AI output in your own pipelines!
Special thanks to
, whose peer critique on scope failures pushed me to build this security gate rather than relying on prompt engineering.
Disclaimer: AI was used throughout this project, it is just fitting that it would co-author with me, so special thanks to the Foundry for it's tireless hours toiling away and Gemini for producing the cover image.
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