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arxiv: 2605.15665 · v1 · pith:Z455GIPQnew · submitted 2026-05-15 · 💻 cs.AI

PRISM: Prompt Reliability via Iterative Simulation and Monitoring for Enterprise Conversational AI

Keshava Chaitanya , Jahnavi Gundakaram This is my paper

Pith reviewed 2026-05-20 19:19 UTC · model grok-4.3

classification 💻 cs.AI
keywords prompt engineeringconversational AILLM reliabilitybehavioral driftsimulation testingcontinuous monitoringenterprise agentsprompt repair
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The pith

PRISM automates prompt creation and daily repair for enterprise conversational agents to counter LLM drift and maintain high reliability.

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

The paper presents PRISM as a closed-loop framework that starts from plain-language requirements and an initial prompt, then generates test cases, runs multi-turn simulations in a platform-faithful environment, applies an LLM-as-judge for pass/fail decisions, diagnoses root causes, and performs targeted prompt repairs. The loop repeats until all tests pass and continues on a daily schedule to catch behavioral changes in the underlying model. In a three-week evaluation across 35 enterprise agents, the system cut median authoring time from two days to under thirty minutes while delivering 99 percent production reliability and detecting plus repairing drift-induced regressions inside a 24-hour window.

Core claim

PRISM treats prompt engineering as continuous reliability engineering rather than a one-time task: it automatically generates tests from requirements, simulates full conversations against a faithful LLM environment, evaluates outcomes with an LLM-as-judge, diagnoses failures, and surgically repairs the prompt, iterating until every test passes, then repeats the process daily to address silent behavioral drift.

What carries the argument

The closed-loop iterative simulation and monitoring cycle that generates tests from requirements, runs multi-turn conversations, judges results, diagnoses failures, and applies surgical prompt repairs on a scheduled daily basis.

If this is right

  • Prompts for new enterprise agents can be authored and validated in under half an hour instead of multiple days.
  • Behavioral drift in production LLMs can be detected and corrected within a single day rather than after user complaints accumulate.
  • Enterprise conversational agents can run at 99 percent reliability without constant manual prompt monitoring.
  • Prompt maintenance becomes a scheduled, automated process rather than an ad-hoc human task.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same simulation-and-repair loop could be applied to non-conversational LLM tasks such as document generation or code assistance where output consistency matters.
  • Connecting the daily simulation results directly to live production metrics would allow earlier detection of drift that only appears under real user load.
  • If the LLM judge itself drifts over time, the entire repair loop could converge on incorrect fixes, suggesting a need for periodic human spot-checks on the judge decisions.

Load-bearing premise

The simulation environment and LLM-as-judge must accurately reflect real production behavior and correctly flag failures without introducing their own systematic errors or biases.

What would settle it

Production logs from the same agents showing failures that the daily simulation cycle missed, or repairs that created new failures not caught by the judge, would falsify the claim that the method sustains 99 percent reliability.

Figures

Figures reproduced from arXiv: 2605.15665 by Jahnavi Gundakaram, Keshava Chaitanya.

Figure 1
Figure 1. Figure 1: PRISM system architecture. Top path: business requirements drive automatic test-suite generation. Centre loop: the current prompt is simulated turn-by-turn; an LLM judge evaluates pass/fail against each test’s criteria; failed tests trigger surgical diagnosis and repair, producing P (k+1) f for the next iteration. Dashed path: the verified production prompt is re-tested daily; any regression re-enters the … view at source ↗
read the original abstract

Deploying large language model (LLM)-driven conversational agents in enterprise settings requires prompts that are simultaneously correct at launch and resilient to the non-deterministic behavioral drift that characterizes production LLM deployments. Existing prompt optimization frameworks address prompt quality as a one-time compile-time problem, leaving open the equally critical question of how to detect and repair prompt regressions caused by silent LLM behavior changes over time. We present PRISM (Prompt Reliability via Iterative Simulation and Monitoring), a closed-loop framework that treats prompt engineering as a continuous reliability engineering problem rather than a one-time authorship task. PRISM takes as input plain-language agent requirements, a set of configured tools and memory variables, and an initial draft prompt. It automatically generates test cases from requirements, simulates full multi-turn conversations against a platform-faithful LLM environment, evaluates pass/fail using an LLM-as-judge, diagnoses root causes of failures, and surgically repairs the prompt -- iterating until all tests pass. Critically, PRISM is designed to run on a scheduled basis (daily), treating LLM behavioral drift as a first-class reliability concern. We evaluate PRISM across 35 enterprise conversational agents over a three-week deployment period on the Yellow.ai V3 platform. PRISM reduces median prompt authoring time from 2 days to under 30 minutes, achieves 99% production reliability across all evaluated agents, and successfully identifies and repairs production regressions caused by LLM behavioral drift within a 24-hour detection window. Our results suggest that continuous, simulation-driven prompt optimization is both tractable and necessary for reliable enterprise conversational AI at scale.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The paper presents PRISM, a closed-loop framework for continuous prompt reliability in enterprise conversational AI. It takes plain-language requirements, tools, and an initial prompt; automatically generates test cases; simulates multi-turn conversations in a platform-faithful LLM environment; uses an LLM-as-judge for pass/fail and root-cause diagnosis; and iteratively repairs the prompt. The system is designed to run daily to detect and repair regressions from LLM behavioral drift. Evaluation on 35 agents over three weeks claims a reduction in median prompt authoring time from 2 days to under 30 minutes, 99% production reliability, and successful identification/repair of drift-induced regressions within a 24-hour window.

Significance. If the simulation fidelity and LLM-as-judge accuracy hold, PRISM would address a genuine gap in handling production drift for LLM agents, moving prompt engineering from one-time optimization to ongoing reliability engineering. The scheduled daily operation and focus on enterprise-scale deployment are practical strengths. However, the significance is limited by the absence of independent validation, making it unclear whether the quantitative gains reflect real production improvements or artifacts of the closed evaluation loop.

major comments (3)
  1. [Evaluation] Evaluation section: the 99% production reliability and 24-hour drift-repair claims rest on the LLM-as-judge and simulation correctly identifying real failures, yet no human-expert agreement study, A/B comparison of simulated vs. live sessions, or false-positive/negative rates measured on production logs are reported.
  2. [Evaluation] Evaluation section: test-case generation from requirements, baseline comparisons, and statistical controls for the three-week deployment across 35 agents are not described, leaving the quantitative outcomes (time reduction, reliability) without sufficient grounding to support the central claims.
  3. [Methodology] Methodology: success metrics appear defined internally to the PRISM loop (simulation + LLM-as-judge) without an independent definition or external oracle, raising the risk that reported reliability is an artifact of the evaluation rather than evidence of production behavior.
minor comments (1)
  1. [Methodology] Clarify how 'platform-faithful' is operationalized in the simulation environment, including any specific fidelity metrics or calibration steps.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive comments. We appreciate the recognition of PRISM's practical focus on continuous reliability engineering for enterprise agents. We address each major comment below, indicating revisions that will be incorporated to strengthen the evaluation and methodology sections.

read point-by-point responses

  1. Referee: [Evaluation] Evaluation section: the 99% production reliability and 24-hour drift-repair claims rest on the LLM-as-judge and simulation correctly identifying real failures, yet no human-expert agreement study, A/B comparison of simulated vs. live sessions, or false-positive/negative rates measured on production logs are reported.

    Authors: We agree that additional validation would strengthen the claims. In the revised manuscript we will add a human-expert agreement study on a stratified sample of 150 test cases drawn from the 35 agents, reporting Cohen's kappa between the LLM-as-judge and two expert annotators. We will also report false-positive and false-negative rates computed from production logs collected during the three-week window, where simulated failures were cross-checked against actual logged user sessions and operator interventions. A full A/B comparison of simulated versus live sessions was not executed in the original deployment to avoid any risk to production traffic; we will explicitly note this as a limitation and outline how such a study could be conducted in future work. revision: partial

  2. Referee: [Evaluation] Evaluation section: test-case generation from requirements, baseline comparisons, and statistical controls for the three-week deployment across 35 agents are not described, leaving the quantitative outcomes (time reduction, reliability) without sufficient grounding to support the central claims.

    Authors: We will expand the Evaluation section with a dedicated subsection describing the test-case generation pipeline, including the structured prompting template that converts plain-language requirements into executable test scenarios and the coverage heuristics used to ensure diversity. Baseline comparisons were performed against internal records of manual prompt-authoring time on the same Yellow.ai platform; we will tabulate these data and add statistical controls consisting of median values with interquartile ranges plus a paired Wilcoxon signed-rank test for the authoring-time reduction. Reliability figures will be accompanied by 95% bootstrap confidence intervals computed across the 35 agents to quantify variability. revision: yes

  3. Referee: [Methodology] Methodology: success metrics appear defined internally to the PRISM loop (simulation + LLM-as-judge) without an independent definition or external oracle, raising the risk that reported reliability is an artifact of the evaluation rather than evidence of production behavior.

    Authors: The primary success metric—production reliability—is defined independently of the simulation loop as the fraction of live agent sessions that complete without requiring human escalation or generating user-reported failures, as captured by the platform's production monitoring system over the three-week period. We will revise the Methodology section to state this definition explicitly and to describe how the final 99% figure was obtained from post-deployment telemetry rather than from simulation outcomes alone. This external grounding distinguishes the reported reliability from any internal loop artifact. revision: partial

Circularity Check

0 steps flagged

Evaluation on external Yellow.ai platform provides independent benchmark; no load-bearing self-definition or self-citation in core claims

full rationale

The paper describes PRISM as an iterative simulation-plus-LLM-judge loop and reports empirical outcomes from running it on 35 real enterprise agents over three weeks on the Yellow.ai V3 platform. The headline metrics (authoring time reduction, 99% production reliability, 24-hour drift repair) are presented as measured results from that deployment rather than derived by algebraic construction or by fitting parameters that are then renamed as predictions. No equations, uniqueness theorems, or ansatzes are invoked; the framework is self-contained against the external platform benchmark. The only minor concern is that internal pass/fail uses the same LLM-as-judge component, but this does not reduce the reported production outcomes to a definitional tautology.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claims rest on the unverified accuracy of the LLM-as-judge and the fidelity of the simulation environment to production; these are treated as domain assumptions rather than demonstrated properties.

axioms (2)
  • domain assumption LLM-as-judge evaluations are sufficiently accurate and unbiased to serve as ground truth for pass/fail decisions
    The framework uses this judge to drive diagnosis and repair; any systematic error here would invalidate the reliability numbers.
  • domain assumption The simulation environment faithfully reproduces production LLM behavior and tool responses
    Stated as platform-faithful; required for detected regressions to correspond to real production issues.

pith-pipeline@v0.9.0 · 5816 in / 1479 out tokens · 52067 ms · 2026-05-20T19:19:55.035914+00:00 · methodology

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Reference graph

Works this paper leans on

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