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Research questionHow can LLM coding agents construct persistent, editable platform-native data pipelines instead of one-off scripts?LLM coding agents commonly produce executable scripts that are disconnected from the platform artifacts users need to inspect, modify, and maintain. The resulting gap makes automated workflow construction difficult when persistence and native editability are required.
AI Agents
Code Generation & Program Synthesis
Latest papersRecent research connected to this question, newest first.DataFlow-Harness: A Grounded Code-Agent Platform for Constructing Editable LLM Data PipelinesThe source studies DataFlow-Harness, which uses live platform state, operator information, typed incremental mutations, and synchronized conversational and visual authoring to construct platform-native DAGs. Evidence comes from a 12-task data-engineering benchmark and reports observed reliability, cost, and latency for this system and comparison baselines.research paper · Jul 18, 2026
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How can LLM agents reuse procedural skills across diverse long-horizon tasks without generic or instance-bound memory?How can we train and evaluate LLM agents for tool use across single- and multi-turn workflows with serial or parallel calls?How can LLM orchestrators preserve continuous state when collaborating with non-language agents?How can open-source LLM agents reliably chain multiple tool calls over live public APIs?
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