Goodfire says its new ‘inside-out’ monitors catch rogue AI agents at a fraction of the cost
The standard way to keep an AI agent in line is to have a second AI read over its shoulder. It’s been the default approach, but it can get expensive fast when agents run for hours and process the equivalent of several novels’ worth of text. Goodfire, a startup focused on interpretability (figuring out how AI models work internally), launched a cheaper option on Thursday: monitors that watch what’s happening inside an AI model as it works, rather than just reading what it writes.
- ▪The standard way to keep an AI agent in line is to have a second AI read over its shoulder.
- ▪It’s been the default approach, but it can get expensive fast when agents run for hours and process the equivalent of several novels’ worth of text.
- ▪Goodfire, a startup focused on interpretability (figuring out how AI models work internally), launched a cheaper option on Thursday: monitors that watch what’s happening inside an AI model as it works, rather than just reading what it write
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| Original publisher | TechCrunch |
| Canonical URL | https://techcrunch.com/2026/10/08/goodfire-says-its-new-inside-out-monitors-catch-rogue-ai-agents-at-a-fraction-of-the-cost/ |
| Publication time | Thu, 08 Oct 2026 16:00:00 +0000 |
| Retrieval time | 2026-10-08T16:12:12.551Z |
| Last seen | 2026-10-08T16:12:12.551Z |
| 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 | mEqJWnagIRZd · 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
The standard way to keep an AI agent in line is to have a second AI read over its shoulder. It’s been the default approach, but it can get expensive fast when agents run for hours and process the equivalent of several novels’ worth of text. Goodfire, a startup focused on interpretability (figuring out how AI models work internally), launched a cheaper option on Thursday: monitors that watch what’s happening inside an AI model as it works, rather than just reading what it writes. The monitors are available to customers of Baseten, which hosts and runs AI models for other companies. Baseten’s Base Labs announced a safety partnership with Goodfire and the AI platform Hugging Face last month.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at TechCrunch.
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