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Agent-S: LLM Agentic workflow to automate Standard Operating Procedures

T0 review · 4 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read Three task-specific LLMs automate Standard Operating Procedures, and with GPT-4o-mini the agent picks the expected next action 97.8% of the time.

desk verdict A workable three-LLM architecture for SOP automation with honest prompts, but the 97.8% headline number is an in-sample self-consistency score, not a real-world validation. read the letter →

arxiv 2503.15520 v1 pith:2CNCLREM submitted 2025-02-03 cs.HC

Mandar Kulkarni This is my paper · ORCID
classification cs.HC
keywords LLMagentsStandardOperatingProcedurecustomercareautomationstatedecisionexecutionmemoryGlobalActionRepositorye-commercesellersupportretrievalaugmentedgeneration
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that a Standard Operating Procedure can be automated by an LLM agent that treats every step as either a user interaction or a status check (API call), with a single state-decision LLM choosing each next action from the SOP text plus an execution memory. This matters because SOPs in customer care are usually written documents for humans, and this design would let them become runnable automated flows without retraining a model or imposing a formal workflow schema. The reported evidence is that ChatGPT-4o-mini as the state LLM selected the expected next action 97.8% of the time across 1221 states drawn from synthetic sessions over three e-commerce seller SOPs, and that the agent recovers from failures by repeating the failed step or an earlier dependent step. A sympathetic reading is that this is a practical architecture for converting existing written procedures into LLM-driven workflows.

What carries the argument

The load-bearing mechanism is the pairing of a text-based SOP workflow with a textual execution memory. The workflow is not a formal schema; it is a natural-language block with indentation for sub-flows (e.g., 'if its active or on-hold: ask user to provide listing id'). The state-decision LLM consumes that block together with the execution memory, whose entries are triples of action, observation, and feedback, and outputs the next action. A retrieval model encodes the output and matches it by cosine similarity to an action in the Global Action Repository, which supplies the action type, required parameters, and API endpoint. The action-execution LLM then talks to the appropriate environment, the resulting observation and success or fail feedback are appended to memory, and the loop repeats. This design lets failure handling be reasoned about rather than pre-scripted: invalid IDs trigger a re-ask, API failures trigger a retry, and user questions trigger a branch to external knowledge followed by resumption of the flow.

What would settle it

Take a held-out set of real historical customer-care transcripts for the same three SOPs, feed each user turn and each actual API response to the agent, and compare the agent's predicted next action against the action the human agent took next. If accuracy on those real transcripts falls substantially below the 0.978 synthetic figure, the synthetic evaluation did not capture production conditions and the claim that the agent effectively navigates real SOPs would not be supported.

Watch

Extended reading notes

Core claim

The paper's central claim is that SOP navigation is a language task: if the SOP is written as a plain logical block of text and the system remembers every action, observation, and feedback triple, then a sufficiently strong LLM can decide the next action, and two supporting LLMs can execute it. The specific architecture has three task-specific LLMs: a state-decision LLM that outputs the next action using chain-of-thought, an action-execution LLM that generates user questions, API parameters, messages, or knowledge-base search queries, and a user-interaction LLM that validates inputs, extracts slots, spell-corrects, and acknowledges. A Global Action Repository stores all possible actions and their metadata, and an embedding model matches the state LLM's free-text output to the closest repository action. On the three SOPs, GPT-4o-mini achieved 0.978 state accuracy versus 0.565 for GPT-3.5, while action execution for question generation and parameter extraction was near 1.0 and search-query generation reached 0.951; the paper interprets this as demonstration that an LLM with good reasoning can effectively run the SOP flow, including fault recovery.

Load-bearing premise

The evaluation assumes that synthetic sessions built from randomly sampled user inputs and API responses represent real customer-care interactions closely enough that the 97.8% state accuracy on them predicts how the agent will perform with live users and production system behavior.

Editorial extensions

If this is right

  • A new SOP can be onboarded by writing its workflow as a plain-text logical block and adding its actions to the shared Global Action Repository, with no new prompts or per-state models.
  • The agent is designed to handle failures by repeating a failed API call, re-asking for invalid user input, or returning to an earlier dependent step, and it terminates gracefully after two repeated attempts.
  • User questions raised mid-flow are answered by branching to an external knowledge source and then resuming the same SOP step, so the conversation need not restart.
  • Because the SOP is just text and the action repository is shared across procedures, the same state-decision prompt should carry over to any workflow representable as a DAG, not just the three e-commerce seller SOPs tested.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper reports live-chat results only as 'good accuracy' and does not give a live per-state number; a direct extension would be to report live state accuracy with the same metric used for synthetic sessions so the 0.978 figure can be compared under real input distributions.
  • The state LLM's choice of which previous action to repeat is effectively a learned repair policy; comparing those choices with the recovery moves human agents make in historical transcripts would show how closely the agent mirrors human judgment.
  • Because the Global Action Repository and state prompt are SOP-agnostic, a cheap test of domain-independence would be to apply the architecture to an SOP from another industry using only a new workflow text and new repository rows.
  • The current loop-prevention rule stops after two repeats of the same action; varying this threshold per action type and measuring resolution rates would tell whether the same constant is safe for API retries and user-input re-asks.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes Agent-S, an LLM-based agentic workflow for automating Standard Operating Procedures (SOPs) in customer-care operations. The architecture uses three task-specific LLMs (state decision, action execution, user interaction), a Global Action Repository (GAR), textual execution memory with action/observation/feedback entries, and external environments (API tools, user interface, RAG-based knowledge source). The SOP is supplied as a plain-text logical block with indentation. The system is evaluated on three e-commerce seller SOPs using 220 synthetic chat sessions (1,221 states) and an unspecified set of in-house live chat sessions. The state LLM achieved 0.978 accuracy with GPT-4o-mini versus 0.565 with GPT-3.5, and per-task action LLM accuracies are reported for question generation, parameter extraction, and search query generation. The authors conclude that the agent can effectively navigate SOP flows and is scheduled to go live after engineering integration.

Significance. The contribution is an applied system design with concrete engineering choices: a shared global action repository, a single state-decision prompt across SOPs, and a fault-tolerant mechanism that repeats dependent steps or seeks external knowledge on failure. The paper includes all LLM prompts in the appendix, which is a useful reproducibility asset. If the evaluation were strengthened, the work would provide practical evidence that plain-text SOPs can be automated by a compact three-LLM agent. However, the current evidence is not yet convincing for the central claim: the synthetic evaluation is a self-consistency check on traces generated from the same SOP text that the agent is prompted to follow, the live evaluation is not quantified, and no baseline or ablation isolates the contribution of the LLM reasoning. The authors are transparent about the manual labeling procedure and about conditioning action LLM accuracy on correct state decisions, which is commendable but does not remove the need for a stronger evaluation.

major comments (4)
  1. [Section 4, Table 2] The 0.978 state accuracy is measured on synthetic sessions generated from the same three SOP texts that are provided verbatim to the state LLM in the prompt (Appendix A.1), and the 'expected step' is manually assigned using those same texts. This is a closed-loop self-consistency check: it tests whether GPT-4o-mini can follow a cleanly written flowchart when branch conditions in the synthetic API responses and user inputs are sampled to satisfy the SOP logic. It does not measure navigation of ambiguous, contradictory, or previously unseen real-world situations. To support the claim of effective SOP navigation, please evaluate on held-out real or realistically noisy sessions with independent ground truth, or at minimum report inter-annotator agreement on the manual labels and compare the synthetic input/response distribution with actual chat logs.
  2. [Section 4, Table 2] No baseline or ablation is reported. A deterministic controller that reads the current execution memory and the SOP text and outputs the next line according to the last feedback and observation would likely achieve high accuracy on these synthetic traces, because the data generation ensures the observations satisfy branch conditions (e.g., 'if its inactive,' 'if API call failed'). Without a baseline, the 0.978 result does not demonstrate that the LLM's logical reasoning—rather than simple text following—is responsible for the performance. Please include a rule-based baseline or an ablation that removes or degrades the execution memory, and show that the LLM agent significantly outperforms it.
  3. [Section 4, Table 3] The action LLM accuracy is evaluated 'only for the cases where the state LLM decision is correct,' and parameter extraction accuracy is explicitly dependent on the user LLM's slot extraction accuracy. These are conditional metrics, not end-to-end pipeline accuracies. A single state-LLM error can invalidate an entire session, and the reported component accuracies do not quantify overall task success. Please report an end-to-end session success rate (e.g., fraction of sessions that reach the correct termination without any erroneous step), or provide an error decomposition that shows what the unconditional accuracy would be.
  4. [Section 4, live chat evaluation] The phrase 'We observed good accuracy for the session success' is the only description of the live chat evaluation. Since the synthetic evaluation is a self-consistency check, the live chat results are the only potentially independent evidence for the claim of real-world effectiveness. Please provide the number of live sessions, the definition of session success, the quantitative success/accuracy rate, a breakdown of failure modes, and the annotation procedure (including any inter-annotator agreement). Without these details, the live evaluation cannot be independently assessed.
minor comments (5)
  1. [Appendix A.1, A.2, A.3] The prompt texts contain a JSON formatting typo: 'Output JSON should have keys "thought, "next_action"' is missing the closing quote and bracket. It should be '"thought", "next_action"' and the JSON example should be closed properly. Since the appendix is provided for replicability, this should be corrected.
  2. [Table 3] The column header 'gpt4o-mini' should be written as 'gpt-4o-mini' for consistency with Table 2 and the text.
  3. [Section 4] The phrase 'all possible API responses' overstates the coverage: the set of API responses was constructed by the authors and is finite. Please clarify the sampling distribution and state which responses (success, invalid ID, API failure, etc.) were included and how many variants were used.
  4. [Section 3] The action repetition limit (more than two repetitions triggers termination with a grace message) is a free parameter with no sensitivity analysis. Reporting how the success rate and user experience vary with this limit would strengthen the design claims.
  5. [Section 1 and Section 3] The paper describes the SOP as a Directed Acyclic Graph, yet the agent explicitly repeats actions on failure, which creates cycles in the execution trace. Please clarify whether the DAG statement applies only to the SOP definition or also to the agent's execution semantics.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper makes an empirical instruction-following claim, and no derivation or fitted parameter reduces to its own inputs.

full rationale

The paper contains no mathematical derivation, no fitted parameters, and no load-bearing self-citations. The central experimental claim is that GPT-4o-mini as the state LLM achieves 0.978 accuracy on synthetic SOP sessions (Sec. 4, Table 2). That claim is an empirical measurement of whether the model predicts the next action expected by the authors, not a result derived from its inputs by construction. The ground-truth label ('expected step') is defined by the same SOP workflow text that is supplied to the state LLM in the prompt, so the evaluation is essentially a self-consistency check of instruction following on a deterministic flowchart. This is a legitimate limitation about benchmark validity and generalization to live settings, but it is not circularity: the prediction is not definitionally equal to the label, no parameter is fitted to the test set and then renamed a prediction, and no prior result by the authors is invoked to force the outcome. The live-chat evaluation is reported only qualitatively, which weakens the evidence base, but a missing strong external benchmark is a correctness or validity concern, not a circularity concern. No quote can be exhibited showing Eq. X reducing to Eq. Y or a fitted input being called a prediction, so the honest finding is no significant circularity.

Assumptions & free parameters 1 free parameters · 5 assumptions · 0 invented entities

The system relies on several domain assumptions about LLM reasoning, embedding retrieval, and synthetic data coverage, plus one hand-chosen threshold. There are no physically invented entities; the Global Action Repository is a software design component, not a new postulated entity.

free parameters (1)
  • action repetition limit = 2
    The agent terminates the flow after the same action is repeated more than two times; chosen by hand, not tuned or justified by data (Section 3, paragraph 4).
assumptions (5)
  • domain assumption SOPs can be represented as Directed Acyclic Graphs (DAGs) and as logical blocks of text without a strict schema.
    The entire approach depends on this representation; the paper states it in Section 1 and uses it in Section 2.2.1.
  • domain assumption A state LLM with chain-of-thought prompting can reliably infer the next action from the SOP text and execution memory.
    This is the core mechanism; the paper evaluates it but does not prove it, and it depends on the LLM's reasoning ability (Section 2.2 and Section 4).
  • domain assumption The e5-base-v2 embedding model and cosine similarity correctly map the state LLM's text output to the correct action in the Global Action Repository.
    The action retrieval relies on this model (Section 2.3); no retrieval accuracy is reported.
  • domain assumption The synthetic data generation covers all possible user inputs and API responses for the three SOPs.
    The paper states the synthetic inputs mimic real-world variations (Section 4); no coverage analysis is provided.
  • domain assumption Manual evaluation by the authors is accurate and unbiased.
    The accuracy numbers come from the authors' manual labeling of expected steps (Section 4); no inter-annotator agreement is reported.

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Cite this review

Pith. "Pith review of Agent-S: LLM Agentic workflow to automate Standard Operating Procedures." pith.science (2026). https://pith.science/paper/2CNCLREM

@misc{pith2026250315520,
  author       = {Pith},
  title        = {Pith review of: Agent-S: LLM Agentic workflow to automate Standard Operating Procedures},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2CNCLREM}},
  note         = {Machine review of arXiv:2503.15520}
}
read the original abstract

AI agents using Large Language Models (LLMs) as foundations have shown promise in solving complex real-world tasks. In this paper, we propose an LLM-based agentic workflow for automating Standard Operating Procedures (SOP). For customer care operations, an SOP defines a logical step-by-step process for human agents to resolve customer issues. We observe that any step in the SOP can be categorized as user interaction or API call, while the logical flow in the SOP defines the navigation. We use LLMs augmented with memory and environments (API tools, user interface, external knowledge source) for SOP automation. Our agentic architecture consists of three task-specific LLMs, a Global Action Repository (GAR), execution memory, and multiple environments. SOP workflow is written as a simple logical block of text. Based on the current execution memory and the SOP, the agent chooses the action to execute; it interacts with an appropriate environment (user/API) to collect observations and feedback, which are, in turn, inputted to memory to decide the next action. The agent is designed to be fault-tolerant, where it dynamically decides to repeat an action or seek input from an external knowledge source. We demonstrate the efficacy of the proposed agent on the three SOPs from the e-commerce seller domain. The experimental results validate the agent's performance under complex real-world scenarios.

Figures

Figures reproduced from arXiv: 2503.15520 by the authors.

Figure 1
Figure 1. Proposed architecture of the SOP Agentic workflow. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

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Forward citations

Cited by 4 Pith papers

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    A graph-guided multi-agent framework plus asymmetric episodic distillation lets fine-tuned 32B models match larger teachers on warehouse SOP execution and hit 94% expert agreement on ticket processing.

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    IFScale measures instruction-following at densities from 10 to 500 constraints and finds that even top frontier models satisfy only about two-thirds of 500 simultaneous keyword instructions.

  3. Email as the Interface to Generative AI Models: Seamless Administrative Automation

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  4. Entity Resolution in Practice: Lessons from a Self-Serve Pipeline

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    On six public benchmarks, the paper shows no single entity-matching algorithm dominates and recommends an automatic matcher tournament, separate precision/recall mechanisms, and verified-merge clustering to stop casca...

Reference graph

Works this paper leans on

21 extracted references · 21 canonical work pages · cited by 4 Pith papers

  1. [1]

    Action in the workflow

    work page

  2. [2]

    thought,

    Execution Memory Workflow consists of a logical sequence of actions. Execution Memory consists of the history of actions, observations and feedback. Your task is to decide the next action based on the workflow and execution memory. *** If the execution memory is empty, output the first action from the workflow. *** If the feedback for the current entry in...

    work page

  3. [3]

    Action context Your task is to generate data to execute an action as per the action, action type and action context

    work page

  4. [4]

    Output the question as user_interaction

    If action type includes ask_user_input, your task is to generate a polite question to the user using the action. Output the question as user_interaction

    work page

  5. [5]

    Output the required params and its values

    If action type includes api_call, your task is to extract and assign a correct value to each of the required param using the action context. Output the required params and its values

    work page

  6. [6]

    Output the search query as search_query

    If action type includes external_knowledge, your task is formulate a short search like query from the user’s question/query provided in the action context. Output the search query as search_query

    work page

  7. [7]

    thought,

    If action type includes message_to_user, your task is to generate the response to the user as shown in the action context. For failure case, inform user that you are retrying the <action>. Output the response as user_interaction. ### Action: <action> ### Action type: <action_type> ### Action context: <action_context> Think step by step and output your thi...

    work page

  8. [8]

    Question asked to the user

    work page

Show all 21 references
  1. [9]

    Condition Your tasks are as follows

  2. [10]

    If yes, set input_validation field as success

    Verify if the user’s reply satisfies the condition. If yes, set input_validation field as success. Otherwise set it as fail

  3. [11]

    Assign a distinctive name to the key as per the question for easy identification

    Extract all the entities from user’s reply and output the slots with key and value per entity. Assign a distinctive name to the key as per the question for easy identification

  4. [12]

    thought,

    Generate a response to the user as follows. If input_validation is success, provide a one-line acknowledgment message. If input_validation field is fail: ** If User’s reply clearly shows a question or a query, output the message that you are working on it and politely ask user...

  5. [13]

    action:check user status, observation:active, feedback:success 2. action:ask user to provide listing id, observation: LSTFYDF12G, feedback:success check listing id status listing_id: ’LST- FYDF12G’ The status of your listing ID (LSTFYDF12G) could not be retrieved due to an err...

  6. [14]

    action:ask user to provide listing id, observation: LSTFYDF12G, feedback:success 3

    action:check user status, observation:active, feedback:success 2. action:ask user to provide listing id, observation: LSTFYDF12G, feedback:success 3. action:check listing id status, observation: api call failed, feedback:fail check listing id status listing_id: ’LST- FYDF12G’ ...

  7. [15]

    action:ask user to provide listing id, observation: LST1234, feedback:success check listing id status listing_id: ’LST1234’ The status of the listing ID ’LST1234’ is invalid

    action:check user status, observation:active, feedback:success 2. action:ask user to provide listing id, observation: LST1234, feedback:success check listing id status listing_id: ’LST1234’ The status of the listing ID ’LST1234’ is invalid. I am retrying the check listing id s...

  8. [16]

    action:ask user to provide listing id, observation: LST1234, feedback:success 3

    action:check user status, observation:active, feedback:success 2. action:ask user to provide listing id, observation: LST1234, feedback:success 3. action:check listing id status, observation: invalid listing id, feedback:fail ask user to provide listing id Could you please pro...

  9. [17]

    action: check user status, observation:active, feedback: success ask user to provide listing id Could you please provide the listing ID? how to find it observation: how to find it feedback: fail

  10. [18]

    action: check user status, observation:active, feedback: success

  11. [19]

    Log into your Seller Portal 2

    action: ask user to provide listing id, observation:how to find it, feedback: fail seek external knowledge search query: How to find my listing ID? To find your Listing ID, follow these steps: 1. Log into your Seller Portal 2. Under the ’Listings’ tab, select ’My Listings’ 3. ...

  12. [20]

    Under the ’Status Details’, check the ’Listing Status’ observation: done feedback: success

    On the right-hand side, click on ’Listing Information’ 6. Under the ’Status Details’, check the ’Listing Status’ observation: done feedback: success

  13. [21]

    action: check user status, observation:active, feedback: success 2.action: ask user to provide listing id, observation:how to find it, feedback: fail 3. action: seek external knowledge , observation:done, feedback: success ask user to provide listing id Could you please provid...

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