Agents aren't the problem – Existing systems and API's were not built for AI
The article discusses the challenges of making existing enterprise APIs compatible with AI agents. It highlights the limitations of traditional search methods and introduces a hybrid approach that combines different search techniques. The solution, called AI Enrichment, enhances API metadata to improve agent understanding without altering the original API structure.
- ▪Existing enterprise APIs often have names and descriptions that are not suitable for AI interpretation.
- ▪A hybrid search method was developed to improve the accuracy of tool discovery for AI agents.
- ▪AI Enrichment allows for clearer naming and descriptions of API tools while preserving the original schema.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,327 of its stories.
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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 | Appfactor |
| Canonical URL | https://www.appfactor.io/blog/how-we-made-getprocinfo3-agent-readable-semantic-discovery-ai-enrichment |
| Publication time | Fri, 29 May 2026 14:18:45 +0000 |
| Retrieval time | 2026-05-29T14:25:01.311Z |
| Last seen | 2026-05-29T14:25:01.311Z |
| 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 | Jyy02teBFgJX |
| 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
backbackPlatform•May 26, 2026MCP Bridge Part 3: How we made getProcInfo3() agent-readable: hybrid discovery + AI EnrichmentIn the previous article, we walked through Code Mode, three meta-tools that replace the entire MCP tool catalog when the API surface is large. The first of those three meta-tools is search_tools. Today we're opening it up.search_tools is what stands between an LLM agent and a 200-operation API surface. It needs to take a natural-language description of what the agent wants to do, and return the three or four tools that can actually do it. Get this wrong and the agent ends up either flailing through irrelevant tools or, worse, calling the wrong one confidently.We thought this would be the easy part of MCP Bridge.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Appfactor.
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