Certified Execution Harness for Agentic AI: Safe and Least-Intrusive Petri-Net Supervisory Control Between Reasoning and Action

AI agents extend large language models (LLMs) with external tools, enabling them to perform complex tasks and translate model outputs into consequential real-world actions. Yet LLMs often become substantially less safe when deployed as agents, and the source of this degradation remains poorly understood. In this paper, we identify schema-formatted tool specifications as a primary source of agent safety degradation and show, through white-box representation analysis, that they weaken the model's internal refusal signals and contribute to unsafe tool execution. Building on this finding, we propose SafeKeep, an inference-time safeguard that decouples safety judgment from tool execution: it assesses requests using flattened textual tool specifications while retaining the original schema-formatted specifications for execution. Across two representative benchmarks and four LLMs, including both white-box and black-box models, SafeKeep increases the average refusal rate for harmful requests from 23.8% to 70.6% and reduces the average attack success rate under observation-level prompt injection from 25.6% to 2.5%. It also outperforms existing safeguards and preserves task-handling capability. We release the code and data at https://github.com/snowcatsmoking/SafeKeep .

Safety alignment for large language models (LLMs) in conversational settings is largely framed around whether to answer or refuse a request. In agentic settings, however, the same models must decide whether to act as permission-critical evidence emerges during execution. This creates a distinct challenge: apparent risk, action permissibility, and task competence are easily confounded, making agentic over-refusal difficult to distinguish from ordinary task failure. To address this, we introduce AgentBound, the first four-way counterfactual generation-and-evaluation framework for tool-using agent safety. AgentBound transforms the same executable workflow by independently varying apparent risk and action permissibility, enabling controlled comparisons of risky-looking but authorized tasks and routine-looking but unauthorized tasks. These comparisons jointly diagnose over-refusal and unsafe compliance while controlling for task competence. We instantiate AgentBound as a human-validated 4,000-task evaluation suite with trajectory-based and post-state-based judgments. Across 17 model and harness configurations, high safety frequently coexists with poor authorized-task completion: GPT-5.5 blocks 99.5% of routine-looking unauthorized actions yet completes only 28.7% of risky-looking authorized tasks. We further train a lightweight runtime calibration module that improves authorized-task completion by 18.2% on average across 10 evaluated configurations, while improving unsafe-action blocking by 5.4% on average. These show that effective agentic alignment requires action decisions to track permission-relevant execution evidence, rather than refusal strength alone.

Large Language Model (LLM) agents take irreversible real-world actions, yet no system gives formally grounded, task-level safety guarantees for their plans. In a PRISMA 2020 systematic review, we searched six databases (20-29 May 2026) for work on LLM agent specification, verification, and enforcement published in 2022-2026. We narratively synthesized 38 studies, assessed risk of bias per study, and graded evidence certainty with GRADE. Three authors independently screened a repeated search to validate the single-reviewer screening (agreement: Fleiss' $κ= 0.81$). First, specification is the bottleneck: on one benchmark, only 24.8-34.2% of LLM translations from natural language into Planning Domain Definition Language (PDDL) are semantically correct. Second, runtime monitoring is the most mature enforcement strategy; it reduced unsafe actions by 65.37% in one household-agent study, but its guarantees cover only single steps or short horizons. Third, in one $τ$-Bench study of transactional tool-use agents, blocking 94% of unsafe actions still left fewer than 5% of tasks completed both safely and correctly in most settings, because agents found other unsafe routes (the verifier tax). Fourth, no approach simultaneously achieves soundness, completeness, scalability, semantic grounding, and utility preservation. For seven of the nine principal claims, GRADE gives the lowest certainty rating (Very Low), meaning further studies are likely to produce substantially different figures; each rests on one or two studies in narrow domains. Two of the open problems we rank highest are translating requirements into formal specifications that keep their meaning and enforcing safety over whole plans rather than single actions. We contribute a three-level taxonomy and an agenda of ten open problems.