TL;DR
This position paper outlines a common abstraction layer that can substantially lower the effort for designing agents with constrained creativity, and demonstrates early promise of this paradigm for two SysOps use cases: Root Cause Analysis and Network Configuration Generation.
This work formalizes minimum-sufficient execution and the Agent Cognitive Redundancy Ratio (ACRR), and proposes E3 (Estimate, Execute, Expand): the agent estimates an initial operating point, executes a minimum viable path, and expands scope only when verification fails.
This is the first paper from POLIS, an ongoing research programme studying algorithmic institutions for multi-agent systems, and asks which parts of an AI institution produce safety and how they do it.
A prototype of a Plan Mode for spreadsheet programming is built and evaluated against a non-planning baseline and it is found that using Plan Mode led to a reduction in refinement and a better perception of the tool across dimensions of creativity support and human-machine collaboration.
This paper synthesizes 27 benchmark, taxonomy, and audit papers (2023-2026), spanning 19 distinct benchmarks, into a cross-cutting taxonomy of agent limitations, the first synthesis that integrates evidence across tool use, planning, long-horizon reasoning, multi-agent coordination, safety, and measurement validity into a single, unified taxonomy of LLM agent limitations.
This work presents the first systematic evaluation framework for agentic abstention, and identifies failure modes such as post-hoc abstention, in which agents execute irreversible actions before recognizing abstention triggers.
A unified, taxonomy-driven, and deployment-oriented survey of agentic AI systems, synthesizing recent advances through a modular reference architecture and a four-dimensional taxonomy that characterizes agents along the axes of autonomy, tool use, collaboration, and safety–governance is presented.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
