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 heterogeneous, the verifier is whether a deployed stack actually runs, and composition knowledge is unevenly captured in pretraining. Unbounded agentic discovery, a coding agent iterating on failure-log feedback, fails to converge consistently on a working stack even when iteration and explicit composition knowledge are adde
The paper addresses current challenges in deploying LLM-driven agents for complex multi-system data backends, indicating ongoing research at the frontier of AI agent capabilities.
This work highlights the critical hurdles in translating agentic discovery from controlled benchmarks to real-world, heterogeneous data environments, affecting the scalability and reliability of AI agents.
The explicit recognition of 'declarative data services' and 'structured agentic discovery' suggests a new approach to making AI agents more effective and robust in composing complex data systems.
- · AI platform developers
- · Enterprises with complex data stacks
- · Data architects
- · AI agent researchers
- · Companies relying on manual data integration
- · Unstructured AI agent development
- · Legacy system integrators
Improved efficiency and reliability in AI-driven data system composition, reducing manual integration efforts.
Accelerated development of sophisticated AI agents capable of managing and optimizing enterprise data infrastructure autonomously.
The emergence of 'AI-defined data centers' where agents dynamically configure and manage entire data ecosystems based on declarative goals.
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Read at arXiv cs.AI