Introducing Dogwood: runtime verification for AI agents
Introducing Dogwood: runtime verification for AI agents by Marc Brooker, Joseph Tassarotti, and Jean-Baptiste Tristan on 06 AUG 2026 in Open Source Permalink Comments Share Part of what makes AI agents so useful is their ability to interact with the external world by running tools. But these tool calls are also the source of the biggest risks when it comes to making agents safe to use. The best way to address these risks in a dependable and reliable manner is to put a layer of control at the tool-call boundary that regulates what an agent is allowed to do.
- ▪Introducing Dogwood: runtime verification for AI agents by Marc Brooker, Joseph Tassarotti, and Jean-Baptiste Tristan on 06 AUG 2026 in Open Source Permalink Comments Share Part of what makes AI agents so useful is their ability to interact
- ▪But these tool calls are also the source of the biggest risks when it comes to making agents safe to use.
- ▪The best way to address these risks in a dependable and reliable manner is to put a layer of control at the tool-call boundary that regulates what an agent is allowed to do.
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Record
| Original publisher | Amazon Web Services |
| Canonical URL | https://aws.amazon.com/blogs/opensource/introducing-dogwood-runtime-verification-for-ai-agents/ |
| Publication time | Thu, 06 Aug 2026 22:24:14 +0000 |
| Retrieval time | 2026-08-06T22:25:41.656Z |
| Last seen | 2026-08-06T22:25:41.656Z |
| 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 | 3cRp_d0hkGJI · 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 |
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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
Introducing Dogwood: runtime verification for AI agents by Marc Brooker, Joseph Tassarotti, and Jean-Baptiste Tristan on 06 AUG 2026 in Open Source Permalink Comments Share Part of what makes AI agents so useful is their ability to interact with the external world by running tools. But these tool calls are also the source of the biggest risks when it comes to making agents safe to use. The best way to address these risks in a dependable and reliable manner is to put a layer of control at the tool-call boundary that regulates what an agent is allowed to do. By enforcing rules about how agents may use tools, we get rigorous guarantees about the ways agents can affect the external world. To do this effectively, we need a way to precisely specify and enforce rules about agent behavior.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Amazon Web Services.
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