Composing Verifiable Conceptual Models via Building Blocks: Towards Design-Time Verification of Agentic AI Workflows

• URL: http://arxiv.org/abs/2606.21565v1
• Date: Fri, 19 Jun 2026 16:03:53 GMT
• Title: Composing Verifiable Conceptual Models via Building Blocks: Towards Design-Time Verification of Agentic AI Workflows
• Abstract summary: Agentic AI systems orchestrate multiple-based agents through workflow architectures that coordinate decisions, tools, and external actions.<n>While current platforms emphasize runtime safeguards, little support exists for verifying during system design.<n>We propose a designtime verification approach that models agentic compositions interact as reusable building blocks and checks their compatibility through twelve structural rules.
• Score: 39.47298454012977
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Agentic AI systems orchestrate multiple LLM-based agents through workflow architectures that coordinate decisions, tools, and external actions. While current platforms emphasize runtime safeguards, little support exists for verifying workflows during system design. From a Modeling \& Simulation perspective, this gap is analogous to composing conceptual models without verifying whether their building blocks interact coherently. We propose a design-time verification approach that models agentic workflows as compositions of reusable building blocks and checks their compatibility through twelve structural rules. We implemented these rules in a software prototype and evaluated them using two openly released datasets: 48 workflows with known design flaws and 168 variants that preserve workflow logic but alter graph structure. Results show that our verifier reliably detects violations even when flawed designs are obscured through structural transformations such as splitting tasks between agents. Future works could combine our verification with community repositories of building blocks to compose safe agentic workflows.

Related papers

• Recovering Software Architecture Intent from Historical Work Items using Generative AI: A Mixed-Methods Industry Case Study [2.5501791028999583]
  This work investigates the recovery of C4 architecture diagrams from historical agile work items using an LLM-based pipeline.<n>The semi-automatic five-step workflow employs a prompt chain, bidirectional traceability, and Chain-of-Thought reasoning.<n>The proposed workflow demonstrates the practical viability of LLM-assisted architectural recovery based on development process artifacts.
  arXiv  Detail & Related papers  (2026-08-28T14:56:00Z)
• Formal Verification of Agentic Systems over Operational Data [59.98246281888422]
  Agentic systems driven by large language models (LLMs) are increasingly deployed in real-world where they act on persistent operational data.<n>Existing approaches do not provide such system-level guarantees.<n>We study here the verification of agentic systems comprising a single LLM and a tool orchestration harness over relational operational data.
  arXiv  Detail & Related papers  (2026-08-04T13:01:30Z)
• Agentic Designer: Progressive Multi-Agent Collaboration for Structure-Aware Interior Layout Generation [84.5255199507027]
  Agentic Designer is a progressive framework that formulates structure-aware interior layout generation as an iterative and constraint-verified decision process.<n>By layout decomposing into modular stages of proposal, verification, synthesis and adjustment, the framework coordinates three specialized agents, a Generator, an Evaluator, and a Refiner.<n>This mechanism enforces stepwise geometric validation and correction before each placement is committed, thereby preventing error accumulation.
  arXiv  Detail & Related papers  (2026-07-23T02:46:11Z)
• Knowledge-Centric Agents for Workflow Generation [73.4340391112949]
  We propose a knowledge-centric framework that learns to invert, inject, and infer with knowledge across multiple abstraction levels.<n>Our method produces with richer node diversity, more coherent structures, and higher execution success rates than existing systems.
  arXiv  Detail & Related papers  (2026-07-17T11:03:01Z)
• From Question Answering to Task Completion: A Survey on Agent System and Harness Design [67.8241530947325]
  This survey examines LLM-based agents through a model-harness lens.<n>We first clarify the functional definition of agents and the implementation view of an LLM-based agent as a foundation model and an execution harness.<n>We then analyze the limits of model-centric scaling, trace four paradigms of agent engineering, and decompose the execution harness into six coupled runtime responsibilities.
  arXiv  Detail & Related papers  (2026-06-14T05:40:16Z)
• TopoPilot: Reliable Conversational Workflow Automation for Topological Data Analysis and Visualization [4.62716665682001]
  TopoPilot is a reliable and agentic framework for automating complex scientific visualization.<n>TopoPilot incorporates systematic guardrails and verification mechanisms to ensure reliable operation.<n>In evaluations, TopoPilot achieves a success rate exceeding 99%, compared to under 50% for baselines without comprehensive guardrails and checks.
  arXiv  Detail & Related papers  (2026-03-26T05:56:53Z)
• Multi-Agent Procedural Graph Extraction with Structural and Logical Refinement [66.51979814832332]
  model formulates procedural graph extraction as a multi-round reasoning process with dedicated structural and logical refinement.<n>Experiments demonstrate that model achieves substantial improvements in both structural correctness and logical consistency over strong baselines.
  arXiv  Detail & Related papers  (2026-01-27T04:00:48Z)
• Towards a Declarative Agentic Layer for Intelligent Agents in MCP-Based Server Ecosystems [0.0]
  This paper presents a model-independent architectural layer for grounded agentic systems.<n>The proposed layer, DALIA, formalises executable capabilities, exposes tasks and constructs deterministic task graphs.<n>By enforcing a clear separation between discovery, planning and execution, the architecture constrains agent behaviour to a verifiable operational space.
  arXiv  Detail & Related papers  (2026-01-24T12:15:49Z)
• CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents [60.98294016925157]
  AI agents are vulnerable to prompt injection attacks, where malicious content hijacks agent behavior to steal credentials or cause financial loss.<n>We introduce Single-Shot Planning for CUAs, where a trusted planner generates a complete execution graph with conditional branches before any observation of potentially malicious content.<n>Although this architectural isolation successfully prevents instruction injections, we show that additional measures are needed to prevent Branch Steering attacks.
  arXiv  Detail & Related papers  (2026-01-14T23:06:35Z)
• Monadic Context Engineering [59.95390010097654]
  This paper introduces Monadic Context Engineering (MCE) to provide a formal foundation for agent design.<n>We demonstrate how Monads enable robust composition, how Applicatives provide a principled structure for parallel execution, and crucially, how Monad Transformers allow for the systematic composition of these capabilities.<n>This layered approach enables developers to construct complex, resilient, and efficient AI agents from simple, independently verifiable components.
  arXiv  Detail & Related papers  (2025-12-27T01:52:06Z)
• AgentCompass: Towards Reliable Evaluation of Agentic Workflows in Production [4.031479494871582]
  We present Agent, the first evaluation framework designed specifically for post-deployment monitoring and reasoning of agentic pipeline.<n>Agent achieves state-of-the-art results on key metrics, while uncovering critical issues missed in human annotations.
  arXiv  Detail & Related papers  (2025-09-18T05:59:04Z)
• Executable Ontologies: Synthesizing Event Semantics with Dataflow Architecture [51.56484100374058]
  We demonstrate that integrating semantic event semantics with a dataflow architecture addresses the limitations of traditional Business Process Management systems.<n>The boldsea-engine's architecture interprets semantic models as executable algorithms without compilation.<n>It enables the modification of event models at runtime ensures transparency, and seamlessly merges data and business logic within a unified semantic framework.
  arXiv  Detail & Related papers  (2025-09-11T18:12:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.