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New Enrich-Retrieve-Rank pipeline scales agent capability discovery

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced the Enrich-Retrieve-Rank (ERR) pipeline to improve capability discovery in large agent ecosystems. This method addresses the limitations of in-context routing, which struggles as the number of components (Models, Agents, Tools, and Skills) grows into the thousands. ERR involves an offline enrichment step to create searchable profiles from metadata and an online retrieve-then-rank process to efficiently return a ranked list of capabilities. The pipeline demonstrates significantly better performance and cost-efficiency compared to traditional in-context routing methods, particularly at scale, and is already in production as a core component of a multi-agent platform. AI

IMPACT This pipeline could enable more efficient and scalable discovery of tools and agents within complex AI systems, improving their overall functionality.

RANK_REASON The cluster contains a research paper detailing a new technical approach for AI systems.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New Enrich-Retrieve-Rank pipeline scales agent capability discovery

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The cluster contains a research paper detailing a new technical approach for AI systems.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Nazib Sorathiya, Daniel Zhang, Bardiya Akhbari ·

    Enrich-Retrieve-Rank: Scaling Capability Discovery Beyond In-Context Routing

    arXiv:2608.22695v1 Announce Type: cross Abstract: Agent ecosystems now include thousands of MATS components (Models, Agents, Tools, and Skills), yet their discovery still relies on in-context routing. These systems read a registry (names, hints, or descriptions, as context budget…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Bardiya Akhbari ·

    Enrich-Retrieve-Rank: Scaling Capability Discovery Beyond In-Context Routing

    Agent ecosystems now include thousands of MATS components (Models, Agents, Tools, and Skills), yet their discovery still relies on in-context routing. These systems read a registry (names, hints, or descriptions, as context budget permits), pick a candidate, invoke it, and retry …

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