Not Acting Is Not One Decision: Two Abstention Triggers in Tool-Using Agents, Why the Calibration and Permission Readings Cover Different Ones, and the Cost Term Neither Supplies

Abstract

An agent that declines to send the email has made a decision that looks like the decision a language model makes when it declines to answer a question, and the resemblance has organised the 2026 literature into two camps. One reads not-acting as a calibration problem: the agent should estimate its own correctness and abstain below a threshold. The other reads it as a permission problem: the agent should be prevented from acting by a system that checks whether the action is allowed. This paper argues that the two readings are not rival answers to one question. They are answers to different questions, because the events that should trigger an abstention are of at least two kinds - an epistemic kind, where the agent lacks information it could in principle recognise it lacks, and a consequential kind, where the action's cost is the reason to stop - and no benchmark located here separates them in its headline number. The premise that the answer-level case is settled does not survive the evidence: a 2025 study across 20 datasets reports that abstention is unsolved for language models, that scaling is of little use, and that reasoning fine-tuning degrades it by 24 percent on average. What does separate the agentic case is structural, not a matter of degree: the action set contains a third option, the decision carries a timing dimension that the answer-level decision does not have, and the loss is asymmetric. On the last of these the measurements are blunt. A 2026 paired benchmark over 263 task pairs and 17 models reports that its best agent reaches 59.5 percent paired accuracy, that abstention is largely independent of task-solving capability, and - the finding this paper treats as load-bearing - that refusal rates differ by about one percentage point between actions that mutate state and actions that do not. Both readings require a cost term, and neither supplies one: the decision rule the calibration reading inherits from Chow fixes its threshold from a cost ratio nobody measures, and the strongest enforcement results are scored against labels whose human inter-annotator agreement sits near 0.48. Doing nothing is meanwhile worth zero or full marks depending on a task label the agent cannot see, and in neither case does any grader attach a cost to what the agent did instead. This paper states what follows for reading the literature, states flatly what is not known, and names the comparisons that would settle the open part.

The literature search, drafting and citation verification for this paper were carried out with AI assistance under the author's direction. Every citation was machine-verified against the DataCite or Crossref record for its DOI before inclusion, and every quantitative claim was read back against the cited source's own table or text before it was written down. No experiment was run and no number in this paper was measured by its author; every number is quoted from the paper credited with it. Section 2 states the search procedure and its limits so that the coverage claims in Sections 9 and 14 can be checked and, if wrong, corrected. The author is responsible for the final text and for all claims made in it.

About the author

Pranay Mahendrakar is an ai specialist and large language model engineer based in Bengaluru, India. He builds production artificial intelligence systems and publishes open-access research on how those systems fail. See all 66 papers by Pranay Mahendrakar, or his ORCID record.