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New architecture streamlines AI agent discovery of data systems

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced Declarative Data Services (DDS), a new architecture designed to improve how AI agents discover and compose data systems. Unlike previous methods that struggled with heterogeneous search spaces and verification, DDS uses a structured approach with typed contracts to break down complex discovery tasks into manageable sub-searches. This framework aims to enable agents to reliably build functional data stacks from declarative user intents, as demonstrated on a trading-backend workload where it achieved convergence where other methods failed. AI

IMPACT This structured approach to data system composition could enable more reliable and efficient AI-driven data infrastructure development.

RANK_REASON The cluster contains an academic paper detailing a new architecture for AI agents.

Read on arXiv cs.AI →

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

New architecture streamlines AI agent discovery of data systems

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Shanshan Ye, Duo Lu ·

    Declarative Data Services: Structured Agentic Discovery for Composing Data Systems

    arXiv:2605.20690v1 Announce Type: new Abstract: Agentic discovery has shown that LLM-driven search can find novel algorithms, designs, and code under benchmark conditions. Translating the paradigm to multi-system data backends surfaces a harder problem: the search space is hetero…

  2. arXiv cs.AI TIER_1 English(EN) · Duo Lu ·

    Declarative Data Services: Structured Agentic Discovery for Composing Data Systems

    Agentic discovery has shown that LLM-driven search can find novel algorithms, designs, and code under benchmark conditions. Translating the paradigm to multi-system data backends surfaces a harder problem: the search space is heterogeneous, the verifier is whether a deployed stac…

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