Formal Verification of Agentic Systems over Operational Data
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
Agentic systems driven by large language models (LLMs) are increasingly deployed in real-world workflows where they act on persistent operational data. Before deployment, these systems need to be verified against business requirements that govern workflow execution and data evolution. However, existing approaches do not provide such system-level guarantees, as they mainly constrain or analyse behaviour at the agent's interface level. We study here the verification of agentic systems comprising a single LLM and a tool orchestration harness over relational operational data. We formalise them as Stateful Tool-Enabled Agentic Deployments (STEADs), give their semantics, define the problem of verifying them against First-Order Computation Tree Logic (FO-CTL) specifications, and show that it is undecidable. We identify sufficient conditions for exact preservation of FO-CTL specifications under a finite-domain restriction, over which verification is PSPACE-complete. The key requirement is that renaming opaque identifiers in the data must correspondingly rename the selected tool calls. We show that LLM-driven agents can violate this condition and introduce a canonical deployment wrapper that guarantees it for arbitrary base agents while preserving already-equivariant behaviour. We prove that computing canonical representations required by this construction is graph-isomorphism-hard. Finally, we illustrate our framework on an LLM agent orchestrating a case-management workflow.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 14 of its 30 references that are in the map. First posted on arXiv 4 Aug 2026.
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14 of 30 references.
References · 14
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 1,908
- 2023Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formattingcited by 778
- 2025τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environmentcited by 558
- 2023State of What Art? A Call for Multi-Prompt LLM Evaluationcited by 299
- 2024ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilitiescited by 272
- 2019Algorithms for Verifying Deep Neural Networkscited by 221
- 2025AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agentscited by 196
- 2025ShieldAgent: Shielding Agents via Verifiable Safety Policy Reasoningcited by 144
- 2020SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutionscited by 72
- 2025Enforcing Temporal Constraints for LLM Agentscited by 31
- 2026The Attack and Defense Landscape of Agentic AI: A Comprehensive Surveycited by 26
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- 2026Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory
- 2026Formal Semantics for Agentic Tool Protocols: A Process Calculus Approach
- 2026Automating Formal Verification with Agent-Guided Tree Search
- 2026Verification as an Architectural Layer for LLM Agents: A V-Model Design, and a Pilot Study of Its Deterministic Core
- 2026Tool Forge: A Validation-Carrying Toolchain for Governed Agentic Execution
- 2026Maat: Independent Deterministic Contract-Based Governance for Multi-Agent LLM Workflows