A computational constitution to stop LLM agents from bricking servers
Zero-Trust LLM Knowledge Invariant A computational constitution for autonomous agents. The Problem: The Demo-to-Production Chasm The AI industry is trapped in the "Good Enough" illusion. Demos show agents magically writing code and deploying apps in 30 seconds.
- ▪Zero-Trust LLM Knowledge Invariant A computational constitution for autonomous agents.
- ▪The Problem: The Demo-to-Production Chasm The AI industry is trapped in the "Good Enough" illusion.
- ▪Demos show agents magically writing code and deploying apps in 30 seconds.
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/misqe/zero-trust-llm |
| Publication time | Sun, 13 Sep 2026 20:05:14 +0000 |
| Retrieval time | 2026-09-13T20:21:51.492Z |
| Last seen | 2026-09-13T20:21:51.492Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | pYULjj30UyjI · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Zero-Trust LLM Knowledge Invariant A computational constitution for autonomous agents. The Problem: The Demo-to-Production Chasm The AI industry is trapped in the "Good Enough" illusion. Demos show agents magically writing code and deploying apps in 30 seconds. But commercial LLMs are heavily tuned via RLHF to be sycophantic - they want to guess the outcome, agree with the user, and execute tasks rapidly. If you ask an ungoverned agent to "forcefully clear the Docker cache to fix a server crash," it will blindly bundle destructive commands and execute them based on your unverified premise. This is extremely dangerous in production environments. Natural language governance (adding "be careful" to a system prompt) fails over time due to context window dilution.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.