Show HN: Agent Chaperone – Screen AI agent tool calls and results with Jev
agent-chaperone A calibrated firewall for AI agent tool calls. agent-chaperone screens an AI agent's tool calls before they run, and the tool results those calls return before the agent reads them. Every decision comes back as a probability, every threshold lives in a policy file rather than a prompt, and every judgment is written to a local log with the numbers that produced it. It starts in shadow mode, which blocks nothing.
- ▪agent-chaperone A calibrated firewall for AI agent tool calls. agent-chaperone screens an AI agent's tool calls before they run, and the tool results those calls return before the agent reads them.
- ▪Every decision comes back as a probability, every threshold lives in a policy file rather than a prompt, and every judgment is written to a local log with the numbers that produced it.
- ▪It starts in shadow mode, which blocks nothing.
Hacker News (AI / LLM) files mainly under ai. We currently carry 5,888 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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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/agent-chaperone/agent-chaperone |
| Publication time | Mon, 21 Sep 2026 16:34:27 +0000 |
| Retrieval time | 2026-09-21T16:43:49.422Z |
| Last seen | 2026-09-21T16:43:49.422Z |
| 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 | xOMAtanOIrN1 · 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
agent-chaperone A calibrated firewall for AI agent tool calls. agent-chaperone screens an AI agent's tool calls before they run, and the tool results those calls return before the agent reads them. Every decision comes back as a probability, every threshold lives in a policy file rather than a prompt, and every judgment is written to a local log with the numbers that produced it. It starts in shadow mode, which blocks nothing. You read your own log first and decide what you would have wanted stopped. agentchaperone.dev carries the same material as pages, including the measured results and a guide for each way people set this up. What it is not Read this part before the rest. Not a sandbox. It cannot stop a server from doing something the call did not describe.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.