SOPStruct: LLM-Driven SOP Standardization

• SOPStruct is a three-phase, LLM-driven framework that transforms unstructured Standard Operating Procedures into formal, graph-based representations.
• It segments SOP text, parses subtasks, and encodes explicit task dependencies into a directed acyclic graph for clear execution order.
• Empirical results show SOPStruct achieves full dependency and input verification, outperforming baselines across multiple benchmark datasets.

SOPStruct is a three-phase, LLM-driven agent for converting free-text Standard Operating Procedures (SOPs) into formally defined, decision-tree or directed-acyclic-graph (DAG) representations. Through segmentation, parsing, and structural encoding of instruction steps, SOPStruct produces machine-readable plans that explicitly capture both logical and temporal task dependencies. The system is designed to address operational inefficiencies arising from inconsistent SOPs and to provide a rigorous, standardized framework for procedure automation and optimization across domains (Garg et al., 28 Mar 2025).

1. Motivation and Standardization Challenges

SOPStruct targets several inherent difficulties in SOP management:

• Heterogeneity in language and formatting: SOPs are often written in informal, inconsistent styles, hampering cross-domain deployment and human comprehension.
• Implicit dependencies and execution errors: Unstructured text leads to missed or unclear task dependencies, increasing error rates during execution.
• Barriers to traditional modeling: Formal representations such as Business Process Modeling Notation (BPMN) and Planning Domain Definition Language (PDDL) demand substantial manual effort and specialized knowledge.
• Cognitive overload: Long, free-text procedures can overwhelm non-technical users.

The design goals are to impose a uniform vocabulary, enforce a single schema-driven (JSON + DAG) structure, and guarantee executional validity by making all dependencies and execution orders explicit and machine-verifiable.

2. System Architecture and Workflow

SOPStruct executes a sequential three-stage pipeline:

• (a) SOP Segmentation: An LLM identifies segment boundaries by detecting context shifts in the full-text SOP $P$, yielding self-contained segments $\{S_k\}_{k=1}^m$ with minimal overlap and full coverage. Formally, $P \to \{S_1, S_2, \dots, S_m\}$.
• (b) SOP Structure Generation: Each segment $S_k$ is further decomposed into a set of subtasks $\mathrm{ST}(S_k)=\{s_{k,1},\dots,s_{k,n_k}\}$. Subtasks are encoded as JSON objects containing:
  • Name and description
  • Dependencies $D(s) \subseteq V$
  • Inputs and outputs (and mapping of dependencies to inputs)
  • Category (one of {Human Input, Information Processing, Information Extraction, Knowledge, Decision})
  • The full plan is assembled as a graph $G=(V, E)$, with $V = \bigcup_k \mathrm{ST}(S_k)$ and $(v_i, v_j) \in E$ if $v_j$ depends on $v_i$. DAG acyclicity is enforced by ensuring no directed cycles: for all cycles $C$, $\sum_{(i,j) \in C} 1 < |C|$.
• (c) Evaluation & Verification: The completed graph undergoes soundness and completeness checks (see Section 4).

Backtracking and error correction are supported: decision-nodes can have multiple outgoing edges, each labeled by a condition that leads to a distinct sub-DAG. Executability is enforced via a valid topological ordering $\pi: V \to \{1, \dots, |V|\}$ where $(v_i, v_j) \in E \implies \pi(v_i) < \pi(v_j)$ (Garg et al., 28 Mar 2025).

3. Representation Formalism

SOPStruct uses an explicit graph-theoretic formalization:

• Graph Construction: Let $V = \{v_1, \dots, v_n\}$ and $E \subseteq V \times V$, $G = (V, E)$.
• Adjacency: $A \in \{0,1\}^{n \times n}$ with $A_{ij}=1$ if $(v_i, v_j) \in E$.
• Logical constraints:
  • Acyclicity: $\forall i, A^k_{ii}=0$ for all $k \ge 1$.
  • Single-entry root node (in-degree 0), leaves (out-degree 0).
• Production rules for branching:

$$\begin{aligned} \text{Node} &\to \mathtt{Task} \mid \mathtt{Decision} \ \mathtt{Decision} &\to [\text{Condition}_1]\;\mathtt{Subdag}_1\;|\;\dots\;|\;[\text{Condition}_k]\;\mathtt{Subdag}_k \end{aligned}$$

• Input–Output Matching: For every edge $(v_i, v_j) \in E$, outputs of $v_i$ must supply all entries in the $inputs\_from\_dependencies$ field of $v_j$ (Garg et al., 28 Mar 2025).

4. Evaluation and Verification Methodology

SOPStruct employs a two-pronged evaluation framework:

• 4.1 Deterministic (PDDL-based) Verification: The structured plan $G$ is automatically translated into PDDL domain and problem files. The domain defines predicates (e.g., $(available\ ?v)$, $(subtask\_output\ ?v\ ?s)$) and actions (e.g., $execute\_subtask$). The problem instance specifies initial state, objectives, and variable mappings. A planner execution certifies:
  • Structured Plan Score (graph connectivity)
  • Dependency Score (correct dependency wiring)
  • Input-from-Dependency Score (inputs/outputs matching)
• 4.2 Non-Deterministic (LLM-based) Completeness: Validation queries an LLM for:
  • Initial State Validation Score: Consistency of "inputs" against the textual initial conditions
  • Goal State Validation Score: Agreement of leaf outputs with the stated goal
  • Plan Completeness Score: Confirmation that no critical steps were missed

5. Empirical Validation and Benchmarking

Experimental evaluation was performed using three benchmark datasets:

1. Nestful API calls (low complexity)
2. RecipeNLG (medium complexity)
3. Business Process Descriptions (high complexity)

SOPStruct is compared to three baselines: zero-shot LLM planning, code-style PROGPROMPT, and BPMN-style LLM. The metrics (Structured Plan, Dependency, Input-from-Dependency, Init/Goal State Validation, Plan Completeness) yield:

Dataset	Zero-shot	Code-style	BPMN	SOPStruct
Nestful API	66	89.7	84	100
RecipeNLG	73.4	90.4	76.4	100
Business Proc.	80.8	66.2	62.2	100

Dependency and Input-from-Dependency scores are 100% for SOPStruct on all datasets, with Initial/Goal/Completeness metrics consistently above 92% (Garg et al., 28 Mar 2025).

6. Applications, Limitations, and Future Directions

SOPStruct is applicable to:

• Translating DAG representations into executable workflow scripts or PDDL plans for automation engines.
• Enabling visual inspection and validation of structured SOPs in human-in-the-loop dashboards.
• Rapid compliance auditing via comparison of structured graphs against regulatory templates.

Limitations include the inability to natively represent genuine loops (unrolled repetition is necessary), reliance on LLM model and prompt quality, and potential need for domain-specific fine-tuning. Planned extensions address hierarchical sub-DAGs, cyclic plans with guards, on-the-fly self-correction mechanisms, and deeper integration with BPMN and other automation platforms (Garg et al., 28 Mar 2025).

7. Significance and Impact

SOPStruct establishes that a carefully architected segmentation–structuring–verification pipeline enables LLMs to robustly convert heterogeneous, unstructured SOPs into validated, machine-executable process graphs. This pipeline achieves systematic standardization and correctness with substantial time and effort savings relative to manual modeling. The methodology presents a scalable path for organizations to bridge the gap between human-authored procedural text and formalized, automatable process plans, offering a foundation for future advances in secure workflow automation and AI-assisted process optimization (Garg et al., 28 Mar 2025).