Preliminary results are provided demonstrating the potential of Credo's approach and a related research agenda is laid out that the database community is well-positioned to tackle, including cost-based compilation over declarative catalogs and catalog maintenance under model and workload drift.
Abstract
An LLM application depends on both a model and a harness: the program that determines what each call sees, how many calls to make, and which answers to trust. Coding agents can now discover strong harnesses by searching over candidate programs, but the resulting artifact is an opaque block of imperative code whose logical steps, runtime signals, physical execution decisions, and prompt strategies remain implicit and task-specific, forcing subsequent tasks to start the harness search process from scratch. The potential for reuse, however, is substantial. A searched harness encodes significant knowledge, such as the logical steps that work, the signals that matter, the physical operator decisions that adapt execution, and the prompt strategies that are effective, yet this knowledge is buried in imperative code with no inspectable or reusable structure, nor does it carry any provenance or metadata. Credo addresses this problem by recovering a structured declarative description of a searched harness, tagging each extracted primitive with relevant metadata, and cataloguing all of it with provenance. A compiler can then bind stored primitives to generate harnesses for new tasks without having to start the search over from scratch. This paper provides preliminary results demonstrating the potential of our approach and lays out a related research agenda that the database community is well-positioned to tackle, including cost-based compilation over declarative catalogs and catalog maintenance under model and workload drift.
COVENANT is proposed, a compiler-and-interpreter architecture for workflow-aligned agent execution that substantially mitigates workflow misalignment, moving LLM-agent alignment beyond isolated prompt following toward reliable execution of complex and multi-step workflows.
Jin-Cheng Wang, Ming-Kai Zheng, Tao Wei· arXiv.org· 1 citation
A contract-bounded runtime architecture, a source-preserving data substrate, and a falsifiable measurement protocol are contributed, which proposes a cluster-period randomized crossover experiment with a four-state verdict: supported, falsified, conditional-engineering, or inconclusive.
Ya-Xiao Liu, Peng Liu, Yi-Wen Liu et al.· 0 citations
This work proposes CURATE - Composition, User-in-the-loop, Reuse, and Automated Task Execution - a novel human-in-the-loop multi-agent system that uses LLM agents to manage and develop composable workflows across their entire lifecycle.
Nolan Cutler, Chia-Chen Kuo, Nanda Velugoti et al.· 0 citations
It is argued that GraphQL constitutes a principled, testable alternative to function calling for agentic systems, combining lower cost, stronger safety, and improved cognitive robustness.
Viktor Zhakhalov· CEUR Workshop Proceedings, V...· 0 citations
Artic is proposed, an artifact-driven workflow compiler that transforms a natural-language workflow into an artifact-driven workflow in which each step declares the artifacts it reads and writes, constraints gate produced artifacts, and explicit control transfers route execution.
Xiang-Zhe Xu, Hanxi Guo, Guangyu Shen et al.· 0 citations
A pipeline promoting an AI system publishes records claiming the thing evaluated is the thing deployed and that the evidence licensed the transition, and measures whether those records can express that claim and whether it holds where declared.
Rashid Azarang· 0 citations
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