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arxiv: 2604.10513 · v1 · submitted 2026-04-12 · 💻 cs.AI

Recognition: unknown

Agent Mentor: Framing Agent Knowledge through Semantic Trajectory Analysis

Roi Ben-Gigi , Yuval David , Fabiana Fournier , Lior Limonad , Dany Moshkovich , Hadar Mulian , Segev Shlomov
Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:38 UTC · model grok-4.3

classification 💻 cs.AI
keywords AI agentssemantic trajectory analysisprompt adaptationexecution logsLLM agentsaccuracy improvementagent mentoringsystem prompts
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The pith

Semantic analysis of execution logs enables automatic correction of ambiguous prompts in AI agents.

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

The paper introduces an analytics pipeline that monitors agent execution trajectories to identify semantic features tied to undesired behaviors. These features are then used to derive corrective instructions that get injected into the agent's system prompts. This targets the core problem of performance variability arising when large language models interpret imprecise natural language definitions of tasks and goals. A sympathetic reader would care because the method offers a way to incrementally adapt and improve agent behavior during operation rather than relying solely on upfront prompt engineering. Experiments across multiple agent setups show consistent accuracy gains, with the largest benefits appearing in cases where specification ambiguity is dominant.

Core claim

The central claim is that an analytics pipeline can systematically extract semantic features associated with undesired behaviors from agent execution logs and convert them into corrective statements that are injected back into the agent's defining system prompts, producing measurable accuracy improvements across tested configurations and tasks.

What carries the argument

The analytics pipeline that identifies semantic features from execution trajectories and derives corrective instructions for prompt adaptation.

If this is right

  • Agent accuracy increases without requiring repeated manual prompt revisions.
  • The largest gains occur in tasks where initial specifications contain high ambiguity.
  • The pipeline supports incremental adaptation of agent knowledge during repeated runs.
  • Such monitoring and correction can form part of automated agent governance systems.

Where Pith is reading between the lines

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

  • The same log-analysis approach might extend to non-agent LLM applications that suffer from prompt drift.
  • Combining this with other feedback mechanisms could reduce the need for human oversight in deployed systems.
  • Scaling the method to longer or more complex trajectories would test whether feature extraction remains effective.

Load-bearing premise

Semantic features extracted from execution logs can be reliably mapped to corrective statements that improve future performance without introducing new failure modes.

What would settle it

Repeated execution runs on the same benchmark tasks and configurations that show no accuracy improvement or the appearance of new errors after the corrective statements are applied would falsify the central claim.

Figures

Figures reproduced from arXiv: 2604.10513 by Dany Moshkovich, Fabiana Fournier, Hadar Mulian, Lior Limonad, Roi Ben-Gigi, Segev Shlomov, Yuval David.

Figure 1
Figure 1. Figure 1: Agent Mentor: observing and teaching your agents how to improve. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Behavioral Improvement Lifecycle Using Semantic Feature Analysis [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Workflow View instances. The clustering procedure is performed iteratively, with model selection guided by an inertia-based criterion. Specifically, we monitor the within-cluster sum of squares (WCSS) and apply an elbow-style threshold [22] to identify an appropriate number of clusters. The process terminates once additional clusters yield diminishing reductions in inertia, indicating a stable partitioning… view at source ↗
Figure 4
Figure 4. Figure 4: Each row corresponds to a single node instance, while [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 4
Figure 4. Figure 4: Each trajectory is represented as a vector of features [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Decision Tree we introduced a within-task robustness test involving four different LLM models to examine how model choice affects the pipeline’s performance (see Section 6.3). 5.2 Metrics We measure task accuracy as the fraction of correct outcomes over 100 runs. We report pre-AMAP and Post-AMAP accuracy, where post-AMAP refers to re-running the agent after injecting AMAP￾derived corrective statements into… view at source ↗
read the original abstract

AI agent development relies heavily on natural language prompting to define agents' tasks, knowledge, and goals. These prompts are interpreted by Large Language Models (LLMs), which govern agent behavior. Consequently, agentic performance is susceptible to variability arising from imprecise or ambiguous prompt formulations. Identifying and correcting such issues requires examining not only the agent's code, but also the internal system prompts generated throughout its execution lifecycle, as reflected in execution logs. In this work, we introduce an analytics pipeline implemented as part of the Agent Mentor open-source library that monitors and incrementally adapts the system prompts defining another agent's behavior. The pipeline improves performance by systematically injecting corrective instructions into the agent's knowledge. We describe its underlying mechanism, with particular emphasis on identifying semantic features associated with undesired behaviors and using them to derive corrective statements. We evaluate the proposed pipeline across three exemplar agent configurations and benchmark tasks using repeated execution runs to assess effectiveness. These experiments provide an initial exploration of automating such a mentoring pipeline within future agentic governance frameworks. Overall, the approach demonstrates consistent and measurable accuracy improvements across diverse configurations, particularly in settings dominated by specification ambiguity. For reproducibility, we released our code as open source under the Agent Mentor library.

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

2 major / 1 minor

Summary. The paper introduces the Agent Mentor open-source library implementing an analytics pipeline that monitors AI agent execution logs, identifies semantic features associated with undesired behaviors, and injects corrective instructions into system prompts to improve performance. It evaluates the pipeline on three exemplar agent configurations and benchmark tasks via repeated execution runs, claiming consistent and measurable accuracy improvements especially under specification ambiguity, and releases the code for reproducibility.

Significance. If the semantic feature extraction and mapping to corrections can be shown to be reliable, automated, and free of new failure modes, the work could support progress toward self-adapting LLM agents and automated governance frameworks. The open-source release of the Agent Mentor library is a clear strength for reproducibility and community follow-up.

major comments (2)
  1. Abstract: the claim of 'consistent and measurable accuracy improvements' from repeated runs on three configurations is unsupported by any quantitative metrics, baselines, statistical tests, or details on how semantic features were identified and mapped to corrections, leaving the central empirical claim under-supported.
  2. Pipeline description (as summarized in abstract): no formal definition, algorithm, or pseudocode is given for extracting semantic features from logs or for the mapping step that produces corrective statements, making it impossible to assess whether the process is fully automated or relies on task-specific heuristics.
minor comments (1)
  1. The abstract refers to 'three exemplar agent configurations' without naming or briefly characterizing them; adding this would improve clarity for readers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below with clarifications drawn from the full paper and indicate where revisions will strengthen the presentation.

read point-by-point responses

  1. Referee: Abstract: the claim of 'consistent and measurable accuracy improvements' from repeated runs on three configurations is unsupported by any quantitative metrics, baselines, statistical tests, or details on how semantic features were identified and mapped to corrections, leaving the central empirical claim under-supported.

    Authors: The abstract offers a high-level summary. The full manuscript's Evaluation section reports quantitative results from repeated execution runs across the three agent configurations and benchmark tasks, including accuracy metrics, baseline comparisons (unmentored agents), and observed improvements particularly under specification ambiguity. The semantic feature identification and mapping process is detailed in the Methodology section via semantic trajectory analysis. To make the central claim more self-contained, we will revise the abstract to include key quantitative findings and a brief outline of the feature-to-correction mapping. revision: yes

  2. Referee: Pipeline description (as summarized in abstract): no formal definition, algorithm, or pseudocode is given for extracting semantic features from logs or for the mapping step that produces corrective statements, making it impossible to assess whether the process is fully automated or relies on task-specific heuristics.

    Authors: Section 3 describes the analytics pipeline, including log monitoring, semantic feature extraction from trajectories, and automated derivation of corrective instructions injected into system prompts. The approach relies on LLM-based semantic analysis and is intended to operate without manual task-specific heuristics. We agree that pseudocode or a formal algorithmic definition would improve clarity and allow readers to verify the degree of automation. We will add pseudocode for the feature extraction and mapping steps in the revised manuscript. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark gains are externally measured

full rationale

The paper presents a pipeline for semantic analysis of agent execution logs to derive corrective prompt injections, with the headline result being measured accuracy improvements on three benchmark configurations via repeated runs. No equations, fitted parameters, or self-citations appear in the provided text that reduce the claimed gains to quantities defined by the method itself. Feature extraction and mapping steps are described at a high level without formal reduction to the output metrics, and results are validated against external task performance rather than by construction. This is a standard empirical claim with no load-bearing self-referential derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review prevents full enumeration; the central claim rests on the unstated assumption that semantic similarity or clustering can isolate prompt defects and that derived corrections will be net positive. No explicit free parameters, axioms, or invented entities are named.

pith-pipeline@v0.9.0 · 5530 in / 1082 out tokens · 32256 ms · 2026-05-10T15:38:06.479465+00:00 · methodology

discussion (0)

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