Make products AI agents want
The article discusses the shift in product design from human users to AI agents in 2026. It emphasizes the need for products to be agent-native, allowing AI assistants to interact seamlessly with various tools. Key strategies for building such products include programmatic parity, multi-agent support, and accessible documentation for agents.
- ▪In 2026, AI agents have become the primary users of productivity and infrastructure tools.
- ▪Products must adapt to be agent-native, allowing AI assistants to perform tasks on behalf of users.
- ▪Key strategies for building agent-friendly products include ensuring API coverage, supporting multiple agent interfaces, and providing clear documentation.
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Story provenance
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Record
| Original publisher | Anitakirkovska |
| Canonical URL | https://anitakirkovska.com/blog/make-products-ai-agents-want/ |
| Publication time | Tue, 19 May 2026 01:07:43 +0000 |
| Retrieval time | 2026-05-19T01:14:57.071Z |
| Last seen | 2026-05-19T01:14:57.071Z |
| 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 | wWDORyJlkSYi |
| 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
Make products AI agents want May 18, 2026 “If you’re running a productivity or an infra company in 2026, humans are no longer your users. Agents are.” The DAU/MAU ratio told you how habitual the product was for humans, where 50% and up meant you had a killer product. But that worked when your product was used by humans. Now their agents are doing the work. Here’re some of my observations on how to build the product agents reach for, and how to measure when they do: Before AI agents Measuring DAU made sense in a world where you expected users to live inside your product. If people spent more time in it, that usually meant the product was delivering value, or it had strong enough network effects to keep pulling them back in.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Anitakirkovska.
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