# Dynamic Agent Generation for Self-Adaptive Root Cause Analysis

Brell Sanwouo, Paul Temple, Clément Quinton

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Brell Sanwouo, Paul Temple, Clément Quinton. Dynamic Agent Generation for Self-Adaptive Root Cause Analysis. SEAMS'26 - 21st International Conference on Software Engineering for Adaptive and Self-Managing Systems, Apr 2026, Rio de Janeiro, Brazil. hal-05402186

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# Dynamic Agent Generation for Self-Adaptive Root Cause Analysis

Brell Sanwouo

brell.sanwouo@inria.fr

Univ. Lille, CNRS, Inria, UMR 9189

CRISTAL, F-59000

Lille, France

Paul Temple

paul.temple@inria.fr

Univ. Rennes, CNRS, Inria, IRISA

Rennes, France

Clément Quinton

clement.quinton@univ-lille.fr

Univ. Lille, CNRS, Inria, UMR 9189

CRISTAL, F-59000

Lille, France

## Abstract

Modern software systems increasingly rely on microservice architectures, which enhance modularity and resilience but produce vast amounts of heterogeneous observability data—logs, metrics, and traces—to ensure reliable operation and early failure detection. Performing *Root Cause Analysis* (RCA) on such data is challenging due to its scale, heterogeneity, and evolving structure, which hinder effective correlation and reasoning across modalities.

Although recent studies have explored statistical techniques, graph-based models, and *Large Language Models* (LLMs)-based agents for RCA, most remain static and task-specific, lacking the adaptability and coordination required to handle evolving diagnostic contexts. This paper introduces a self-adaptive agent generation framework that leverages LLMs to dynamically compose and orchestrate adapted diagnostic agents at runtime according to each anomaly's characteristics. Two main agents drive this process: a Parser that interprets natural-language queries and builds structured task specifications, and an Executor that adapts and coordinates adapted agents analyzing multimodal observability data through a shared memory space. Experiments on Nezha (fault-injected) and OpenRCA (real-world) datasets result in up to 12% and 22% gains in diagnostic accuracy, showing the framework's effectiveness in adaptive reasoning, coordination, and interpretable root cause identification.

## CCS Concepts

* Computing methodologies → Intelligent agents; * Computer systems organization → Self-organizing autonomic computing.

## Keywords

Root Cause Analysis, Self-Adaptive System, Multi-modal data, Agent, Large Language Model, Microservice System

## ACM Reference Format:

Brell Sanwouo, Paul Temple, and Clément Quinton. 2026. Dynamic Agent Generation for Self-Adaptive Root Cause Analysis. In *21st International Conference on Software Engineering for Adaptive and Self-Managing Systems (SEAMS '26), April 13–14, 2026, Rio de Janeiro, Brazil*. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3788550.3794865

<figure><img src="image_2.png" /></figure>

This work is licensed under a Creative Commons Attribution 4.0 International License.
SEAMS '26, Rio de Janeiro, Brazil

© 2026 Copyright held by the owner/author(s).

ACM ISBN 979-8-4007-2445-9/2026/04

https://doi.org/10.1145/3788550.3794865

## 1 Introduction

Microservice architectures have become the standard paradigm for large-scale distributed systems [2, 12, 15, 45].

To maintain reliability and diagnose failures, engineers rely on observability data (i.e., logs, metrics, and traces) that capture complementary views of microservices architecture. While these data enable fine-grained system monitoring, they form a heterogeneous multi-modal data[3], making anomaly diagnosis and root cause identification particularly challenging. Recent advances in *Generative AI* (GenAI)—especially LLMs [5, 34, 47]—have opened new possibilities for self-adaptive microservice systems. In a world where changes arise at runtime and at a high pace, with the democratization of continuous integration and continuous improvement, being able to analyze defects and retrieve root causes quickly and efficiently is extremely important. This convergence has led to a new research direction in which LLM-powered agents collaboratively interpret multi-modal observability data [8, 35, 39, 46, 52, 55].

While these agentic systems show promise for autonomous diagnosis, they typically rely on a fixed number of agents with predefined roles and interaction protocols. Such static designs fail to adapt to the varying complexity of RCA tasks in microservice environments, leading to either underexploration in complex failures or redundant reasoning in simple cases.

To address these limitations, we propose a self-adaptive multi-agent framework that performs adaptive agent generation; dynamically adjusting the number, task, and coordination of adapted agents based on the complexity and uncertainty of each task. Unlike static frameworks, our approach operates over a structured set of agent templates and tool definitions to instantiate context-specific agents that combine reasoning and analysis capabilities while sharing operational knowledge. Thus, the system is able to balance exploration and efficiency, improving scalability, resource utilization, and adaptability to evolving service conditions.

We evaluate our framework on two complementary RCA benchmarks: Nezha [56], a fault-injected dataset, and OpenRCA [55], a real-world benchmark with 68 GB of multi-modal telemetry from Telecom, Banking, and Market systems. Using both proprietary and open-source LLMs, we analyze reasoning, memory, and execution strategies under varying configurations. Our results show that adaptive agent generation consistently achieves higher diagnostic performance than both non-agent baselines (e.g., MRCA) and fixed-agent architectures (e.g., RCA-Agent), achieving up to +12% improvement on Nezha and +22% on OpenRCA.

In summary, our main contributions are:

(1) A self-adaptive multi-agent framework that enables adaptive agent generation at runtime from structured templates and tool definitions for context-aware RCA.
(2) An experimental evaluation on Nezha and OpenRCA benchmarks, analyzing reasoning, memory, and execution strategies across multiple LLMs.

(3) **Results** showing that adaptive agent generation enhances diagnostic precision, scalability, and robustness compared to static and non-agent approaches.

The remainder of the paper is structured as follows. Section 2 introduces the background on multi-modal observability data, agent concepts, and motivates this work. Section 3 presents our adaptive agent generation framework. Section 4 describes the experimental setup, while Section 5 discusses the results. Section 6 reviews related works, and Section 7 discusses threats to validity. Finally, we conclude with Section 8 and outline future works.

# 2 Background and motivation

This section reviews the foundations of observability and agentic Artificial Intelligent (AI), showing how advances in multi-modal data analysis and intelligent agents provide the basis for our approach. It then motivates the need for dynamically adaptive, reusable agent frameworks to support effective RCA in dynamic environments.

## 2.1 Background

**Observability and multi-modal data.** In microservice-based architectures, observability is fundamental to maintain system stability and reliability [62]. Observability data typically comes from three complementary sources—metrics, logs, and traces—which together provide a comprehensive view of system behavior [58]. Metrics capture quantitative indicators such as CPU utilization, memory consumption, and latency, to enable early detection of performance deviations. Logs record runtime events with timestamps, severity levels, and contextual messages, facilitating fault diagnosis and debugging [57]. Traces describe causal dependencies among distributed service calls, reconstructing end-to-end request execution paths and anomalies across microservices [24]. Together, these heterogeneous yet complementary modalities form a set of multi-modal observability data [3], whose integration enhances anomaly detection and RCA in microservice environments [58].

**AI agents and agentic AI.** An agent is an autonomous entity capable of perceiving its environment through sensors and acting on it through actuators to achieve predefined goals or optimize a performance function [38, 44, 49]. The rise of GenAI [4, 13, 18, 19, 23, 48] has led to the emergence of AI agents that leverage LLMs and reinforcement learning to perceive, plan, and act across complex workflows [11]. Building on this foundation, agentic AI systems interconnect multiple agents to enable distributed coordination, shared goals, and proactive reasoning [22]. Such systems mark a paradigm shift toward more autonomous and adaptive computing, offering new opportunities for runtime problem solving and self-management.

## 2.2 Motivation

Despite significant progress in leveraging multi-modal observability data for system analysis, existing approaches [14, 26, 31, 50, 56, 58, 63] still face practical limitations. They often rely on statistical or graph-based models that aggregate heterogeneous features into

unified embeddings, thereby losing modality-specific information. In addition, these models require frequent retraining or manual tuning to remain effective under evolving system conditions. These limitations motivate the exploration of AI-agent-based solutions [8, 35, 37, 39, 42, 43, 46, 51, 52, 55, 55, 59, 61] capable of reasoning more flexibly across modalities and contexts. The motivations for this work are as follows:

**Overcoming rigid agent architectures.** Most existing approaches for RCA on multimodal observability data define their multi-agent architecture a priori: the number of agents is fixed, and each agent is assigned a specific task in advance. While this design simplifies implementation, it severely constrains adaptability. For instance, the fixed set of agents prevents the creation of new ones or reassigning tasks, or forbids the reorganization of interactions between agents to better fit the diagnostic context. To achieve fully adaptive Self-Adaptive Systems (SAS), it is essential to develop mechanisms that enable the dynamic instantiation, specialization, and reconfiguration of agents according to the evolving requirements of multimodal RCA.

**Exploiting the synergy between AI agents and LLM.** Recent studies [8, 37, 42, 55] have investigated how LLM can be combined with intelligent agents to support RCA. Most existing works rely on LLMs as analyzers. However, LLM could also be leveraged to dynamically generate new agents, adapt or reuse existing ones, and orchestrate more flexible interactions among them. Investigating this synergy is essential for designing agent-based SASs capable of contextual reasoning over heterogeneous data and delivering more adaptive, accurate, and responsive RCA in microservice environments.

**Designing a reusable ready-to-run agentic framework.**

A central objective of this work is to provide an agentic framework that is not only ready-to-run but also inherently reusable in different contexts. Instead of requiring ad hoc adjustments for each new environment, the framework is conceived as a set of modular and configurable agent templates that can be instantiated, adapted, and coordinated according to the needs of the system under analysis. This design enables the framework to accommodate heterogeneous observability data, diverse kinds of anomalies, and varying levels of system complexity without necessitating a complete redefinition of the adaptation logic. By emphasizing reusability, modularity, and scalability, such a ready-to-run framework reduces integration costs, accelerates deployment, and supports long-term sustainability in dynamic microservice ecosystems.

## 2.3 Problem Statement

In large-scale microservice systems, observability data such as logs, traces, and metrics are continuously collected to support RCA. Given a detected failure during a specified period in time, stated in a natural language query, the goal is to infer the most plausible root cause from heterogeneous observability data.

Instead of relying on a static or monolithic diagnostic model, we define RCA as an adaptive mapping function:

$$ \Phi : (X, A) \rightarrow Y \qquad (1) $$

where X denotes the multi-modal observability data, A the anomaly query, and Y the set of candidate explanations. Φ is implemented through a population of adapted agents dynamically generated
and coordinated according to the diagnostic context. These agents reason over different data modalities and cooperate through an adaptive orchestration policy to consolidate their findings. The core problem addressed in this paper is to design such a self-adaptive multi-agent RCA framework capable. In our evaluation, Φ's is later measured through precision and accuracy.

Unlike AI-agent-based RCA approaches [7, 34, 36, 39, 42, 43, 46, 51, 52, 55, 55, 59, 61] rely on fixed number of agents and specific tasks, this work addresses a self-adaptive multi-agent framework for RCA, capable of adjusting the number of agents, their specialization, and their coordination strategies.

# 3 Approach

This section presents the proposed self-adaptive agent generation framework for RCA. It details the overall architecture and workflow, explaining how the system interprets anomaly queries, constructs diagnostic specifications, and dynamically orchestrates specialized agents to identify root causes.

## 3.1 Overview

Building on the problem formulation presented in Section 2.3, we introduce a self-adaptive agent generation framework for RCA, illustrated in Figure 1. The framework leverages multimodal observability data (i.e., logs, metrics, and traces) to infer the most plausible root causes of system anomalies through the dynamic composition of adapted agents and an adaptive orchestration mechanism that continuously adjusts to the characteristics of each anomaly.

Figure 1 depicts the overall workflow of the framework. The diagnostic process begins with a natural-language anomaly query (top left), which is interpreted by the Parser Agent. For example, a query might read: *On March 6, 2021, between 18:30 and 19:00, a failure occurred. However, the root cause component, the exact time of the root cause occurrence, and the underlying reason for the failure are currently unknown. You are tasked with identifying the root cause component, the time of occurrence, and the reason for the failure.* From each query, the Parser Agent then extracts the diagnostic context and generates a structured specification (*BuildSpec*). Based on this specification, the Executor Agent orchestrates a population of adapted agents, each dedicated to a specific data modality or reasoning strategy. These agents collaborate through a shared memory space, enabling contextual reasoning and progressive refinement of intermediate results.

At its core, the framework is governed by two central components: the Parser Agent and the Executor Agent. The Parser Agent acts as the main interface between the user query and the diagnostic process. Given a natural-language description of an incident, the LLM controlling this agent interprets temporal and contextual information, identifies relevant system entities, and verifies the availability of observability sources. The Executor Agent operates as a self-adaptive controller that dynamically determines the number, type, and specialization of agents required to solve each diagnostic task. Unlike static approaches with a fixed agent configuration, the Executor progressively creates, adapts, or terminates agents according to the evolving diagnostic context. This decision is guided by two main criteria: (i) the estimated complexity

of the anomaly derived from both the *BuildSpec* and early reasoning outcomes, and (ii) the diversity-efficiency trade-off observed during ongoing analysis.

Through this process, the Executor Agent consolidates the partial findings of all adapted agents into a ranked and interpretable list of candidate root causes, offering a transparent and adaptive reasoning path from the initial query to the final diagnosis.

## 3.2 The Parser Agent

The Parser Agent represents the first stage of the proposed framework (Figure 1, steps ① and ②). Its primary role is to process a user query—typically expressed in natural language—by extracting key contextual and temporal information and verifying the availability of relevant observability data required for diagnosis. The Parser Agent operates through three main steps:

* **Query analysis.** The agent begins by analyzing the user query to identify the essential diagnostic elements, such as the *incident time window*, the *system context*, and the relevant entities (e.g., services, components, or metrics) mentioned in the description.

* **Data source verification.** It then verifies the availability and integrity of the required observability data by inspecting the repositories containing logs, metrics, and traces. If some data sources are missing or incomplete, the agent reports their absence to maintain the coherence of the diagnostic process.

* **Specification synthesis.** Finally, the agent consolidates all extracted information into a structured specification called *BuildSpec*, which uniquely captures the diagnostic context, accessible data sources, and execution parameters for a given query—exactly one *BuildSpec* being generated per request to ensure a consistent execution context across subsequent agents. An example of the resulting JSON specification is provided in the appendix<fnref n="1" />.

Performing these tasks requires an AI agent, as they involve understanding, reasoning, and synthesis beyond deterministic rule-based processing. First, the input queries are expressed in natural language and often contain ambiguous, incomplete, or context-dependent information, which demands semantic interpretation and contextual inference capabilities naturally provided by GenAI. Second, verifying the availability of observability data requires adaptive reasoning: when certain data sources are missing or incomplete, the agent must assess their impact and report inconsistencies to preserve the coherence of the diagnostic process. Finally, generating the *BuildSpec* involves summarizing and organizing heterogeneous information into a coherent and standardized representation, a task that benefits from the abstraction and summarization abilities of GenAI.

The resulting *BuildSpec* provides all the necessary parameters for adaptive agent generation and orchestration. In cases where the Executor Agent identifies missing context or new diagnostic requirements during runtime, the Parser Agent can be reinvoked.

<footnote n="1">¹https://zenodo.org/records/18302941</footnote>
<figure><img src="image_3.png" /><figcaption>Figure 1: Agent generation framework</figcaption></figure>

## 3.3 The Executor Agent

The Executor Agent constitutes the core component of the framework. Based on the information provided in the *BuildSpec* (Figure 1, step ③), agents are adapted from predefined *agent templates and tools*, ensuring reusability, modularity, and consistency across heterogeneous diagnostic tasks (Figure 1, steps ④ and ⑤). Then, it creates a dedicated execution environment in which adapted agents are instantiated, coordinated, and terminated once their tasks are completed (Figure 1, step ⑥). During execution within the Python kernel, a new agent is created whenever the number of adapted agents remains below the maximum, and the diagnostic context requires an additional analysis perspective. Similarly, an Adapted Agent is terminated once it has completed its assigned task and store his output in shared memory. This shared memory acts as a persistent, vector-based memory store that preserves contextual representations of the Adapted agent's intermediate reasoning results, ensuring that information produced by terminated Adapted agents remains accessible to subsequent Adapted agents during the root-cause analysis. This mechanism allows reasoning artifacts to be accumulated and reused across Adapted agents without requiring their continued execution (Figure 1, steps ⑤ and ⑥). Within this environment, agents diagnose the root cause by reading multi-modal observability data and sharing a collective memory space that enables inter-agent communication, contextual reasoning, and the exchange of intermediate results (Figure 1, step ⑦).

During the analysis, the Executor Agent progressively maintains a set of root-cause candidates, dynamically adding or discarding them based on the execution results of adapted agents. The overall process terminates when either all remaining candidates are marked as *possible_root_cause = True* after exhausting the relevant data, or when the maximum number of agents specified in the configuration is reached, after which the final candidates are summarized in a structured output (Figure 1, step ⑧).

The Executor Agent fulfills three main responsibilities:

*   **Adaptive agent generation.** The Executor Agent initializes a dedicated diagnostic environment and dynamically manages the life cycle of adapted agents required for RCA. Each agent is adapted from a predefined template selected according to the diagnostic objective and the contextual information specified in the *BuildSpec*. Template selection is driven by the role description of each agent template. Concretely, the Executor Agent iterates over the available agent

roles and relies on its underlying LLM-based reasoning to select the suitable template and adapt with the suitable configuration to match the current task. The generation process is inherently adaptive: rather than predefining all agents at initialization, the Executor Agent progressively creates, configures, or removes adapted agents (Figure 1, step ⑥) based on the system's current state and intermediate analytical outcomes.

*   **Environment and execution management.** At each step, the framework autonomously evaluates the relevance of intermediate results and infers new diagnostic requirements by monitoring the number of current root cause candidates and their associated status flags, and adapts the composition of active agents accordingly. When emerging needs arise—such as refining a time window, inspecting an additional service, or analyzing a specific subset of logs—the Executor Agent selects the most suitable template and instantiates a new adapted agent with the appropriate configuration. The integration of newly created agents depends on the selected execution mode: in *sequential mode*, the agent runs after the previous analysis is complete; and in *asynchronous mode*, it streams intermediate results that are integrated in real time. Upon completion of their tasks, agents and their associated resources are deleted, while only the relevant information stored in the shared memory is preserved.

*   **Progressive result synthesis and candidate ranking.** During the diagnostic process, the Executor Agent continuously consolidates and refines the partial outputs produced by the adapted agents (Figure 1, step ⑤). At each step, the intermediate results are evaluated, merged, and updated within the shared memory space to maintain a coherent and evolving representation of potential root causes. The list of candidates is thus built incrementally, with dynamically adjusted criteria. This continuous synthesis process enables the Executor Agent to refine its understanding of the incident over time, providing an interpretable, traceable, and prioritized view of the root causes while the diagnosis is still in progress.

## 4 Experimental Methodology

We evaluate our method by answering the following research questions (RQs):
* **RQ1:** How effective is adaptive agent generation for RCA identification on multi-modal observability data in a microservice-based system?

* **RQ2:** How do the structural and behavioral properties of the multi-agent architecture — including reasoning mechanisms, coordination strategies, and memory integration — affect the system's ability to deliver accurate, interpretable, and consistent diagnoses?

* **RQ3:** How does the number of agents generated influence the trade-off between diagnostic accuracy and the reliability of root cause identification?

## 4.1 Hardware Settings

All experiments were conducted on a workstation running Ubuntu 22.04.5 LTS, The system is equipped with Intel Core Ultra 7 165H CPU (16 cores, up to 5.0 GHz), 32 GB LPDDR5x RAM, and 1 TB PCIe Gen 4 SSD. The system also includes an integrated Intel Arc GPU and an NVIDIA RTX 2000 Ada GPU (8 GB GDDR6). The framework was implemented in Python 3.11.13 using CrewAI (v0.193.2)<fnref n="2" /> for multi-agent orchestration, and Ollama (v0.9.6)<fnref n="3" /> for local deployment of open-source LLMs such as Mistral-7B and Llama-8B. To ensure consistent experimental conditions and fair comparison across models, all runs were executed in CPU mode without GPU acceleration. Each configuration was repeated under identical system load to mitigate hardware-induced variability.

## 4.2 Datasets

We evaluate our approach using two complementary benchmarks: Nezha [56], derived from controlled benchmarks with injected faults, and OpenRCA [55], which originates from three real-world production systems.

Nezha. Nezha [56] is a public benchmark in which faults are deliberately injected into two representative microservice-based systems: Online Boutique [33] and Train Ticket [17]. Online Boutique (OB) is an e-commerce platform for browsing and purchasing products. Train Ticket (TT) is a distributed railway booking application for users to query, reserve, and purchase tickets. Both systems have been widely adopted in previous research for microservice architectures [10, 21, 26, 30, 56]. The benchmark encompasses three main categories of injected anomalies: CPU contention anomalies, network congestion anomalies [29], and code defect anomalies [32]. Its composition is summarized in Table 1.

<table><caption>Table 1: Composition of the Nezha dataset.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"># Anomalies</th><th colspan="2"># Faults</th><th rowspan="2">#Logs</th><th rowspan="2">#Traces</th></tr><tr><th>Resource</th><th>Code</th></tr></thead><tbody><tr><td>Nezha-OB</td><td>56</td><td>42</td><td>24</td><td>3,907,981</td><td>80,934</td></tr><tr><td>Nezha-TT</td><td>45</td><td>20</td><td>25</td><td>1,352,335</td><td>51,604</td></tr></tbody></table>

**OpenRCA.** The OpenRCA benchmark [55] is a recent large-scale dataset designed to evaluate the ability of LLMs to perform RCA in realistic operational settings. It contains 335 failure cases collected

from three enterprise-scale software systems — Telecom, Bank, and Market — and provides more than 68 GB of multimodal observability data. Each failure case is paired with a natural language query describing an incident scenario and requiring the system to identify the corresponding root cause elements: the originating component, the failure time, and the failure reason (e.g., CPU overload, memory leak, or network latency). Table 2 summarizes the composition of the OpenRCA benchmark.

<table><caption>Table 2: Composition of the OpenRCA dataset.</caption><thead><tr><th>Dataset</th><th>#Cases</th><th>#Components</th><th>#Reasons</th><th>Telemetry Size</th><th>Data Types</th></tr></thead><tbody><tr><td>Telecom</td><td>51</td><td>15</td><td>5</td><td>17.6 GB</td><td>Metrics, Traces</td></tr><tr><td>Bank</td><td>136</td><td>14</td><td>8</td><td>26.4 GB</td><td>Logs, Metrics, Traces</td></tr><tr><td>Market</td><td>148</td><td>44</td><td>15</td><td>24.5 GB</td><td>Logs, Metrics, Traces</td></tr><tr><td>Total</td><td>335</td><td>73</td><td>28</td><td>68.5 GB</td><td>—</td></tr></tbody></table>

During execution, the generated agents dynamically read the relevant portions of the telemetry data according to the diagnostic context specified in the user query provided in the BuildSpec.

## 4.3 Evaluation Metrics

To assess the effectiveness of our approach, we use different metrics provided by the two selected benchmarks (i.e., Nezha [56] and OpenRCA [55]). Each benchmark measures a complementary aspect of diagnostic performance, focusing respectively on *ranking precision* and *structural correctness* of the identified root causes. These two perspectives jointly evaluate the reasoning accuracy (RQ1) and the adaptability and consistency of multi-agent coordination (RQ2–RQ3).

**Metrics for Nezha.** The Nezha benchmark [56] evaluates RCA systems using the Precision@K (PR@K) metric, typically used for ranking-based evaluation. For each anomaly *a*, the model produces a ranked list of candidate root causes $R_a(i)$. PR@K is defined as [50]:

$$PR@K = \frac{1}{|A|} \sum_{a \in A} \frac{\sum_{i<K} R_a(i) \in V_a}{\min(K, |v_a|)} \quad (2)$$

where *A* denotes the set of anomaly cases, $V_a$ the ground-truth root causes for case *a*, and $R_a(i)$ the *i*-th ranked prediction. If any $R_a(i)$ in the range $1 \le i \le K$ matches the groundtruth, it contributes 1; otherwise 0. We report results for PR@1, PR@3, and PR@5 to assess sensitivity to ranking depth on both Nezha-OB and Nezha-TT.

**Metrics for OpenRCA.** The OpenRCA benchmark [55] introduces a goal-driven evaluation procedure that measures the ability of a model to generate fully or partially correct structured RCA predictions from multimodal telemetry. Each instance combines a natural-language query and associated observability data, requiring a structured output {time, component, reason} in JSON format.

We adopt two metrics defined in [55]:

* Exact Accuracy: a prediction is considered correct (1 point) only if all required output elements (i.e., time, component, and reason) match exactly the ground truth. The final accuracy is averaged over all failure cases.

* Partial Accuracy: a relaxed binary criterion that assigns 1 point if at least one of the three output elements is predicted correctly, 0 otherwise.

<footnote n="2">²https://docs.crewai.com/</footnote>

<footnote n="3">³https://docs.ollama.com/</footnote>
Together, these metrics capture complementary aspects of diag-
nostic quality: PR@K reflects ranking precision, whereas exact and
partial accuracy quantify the interpretability, completeness, and
robustness of multi-agent RCA reasoning.

## 4.4 Experimental Design

Through our experiments, we analyze the impact of the number of agents that can be adapted, and the use of options such as *reasoning*, *shared memory*, and *execution modes* on the system’s adaptability and diagnostic performance. These experimental factors relate directly to our research questions: overall diagnostic effectiveness (RQ1), the contribution of reasoning and memory to agent coordination (RQ2), and the trade-off between accuracy and scalability when varying the number of agents (RQ3).

The Executor Agent is able to dynamically adapt and coordinate up to 5, 10, or 20 adapted agents, to represent small, medium, and large agent populations. Reasoning is a mechanism that enables agents to re-evaluate intermediate conclusions during execution. When disabled, agents perform a single-pass analysis; otherwise, they can refine or correct their hypotheses as new evidences emerge. Memory refers to the capability to access a single global memory during execution. This shared memory offers knowledge persistence and coordination across reasoning stages, by storing, for instance, contextual information, intermediate results, or reasoning traces. Finally, we consider two execution modes. In sequential mode, agents are executed one after another, each building upon previous results. In asynchronous mode, multiple agents operate concurrently and communicate through the shared memory, enabling more adaptive coordination. Combining these factors results in four reasoning-memory configurations: (1) no reasoning and no memory, (2) no reasoning with memory, (3) reasoning without memory, and (4) reasoning with memory. Each configuration was evaluated under both execution modes.

Seven LLMs served as reasoning cores: three proprietary models (i.e., *Claude 3.5*, *GPT-4o*, and *Gemini 1.5 Pro*) accessed through their APIs, two open-source models (i.e., *Mistral Large 2*, and *Llama 70B*) also accessed through their APIs, and two lightweight open-source models (i.e., *Llama 8B*, and *Mistral 7B*) deployed locally with *Ollama*. The framework was implemented using *CrewAI* for multi-agent orchestration. All runs used identical initial prompts and configuration parameters.

In total, eight distinct configurations—derived from four reason-
ing-memory settings and two execution modes—were tested across
seven LLMs. Each configuration-model pair was executed fifteen
times to reduce stochastic variability, yielding 840 independent
runs overall.

# 5 Experimental Results

## 5.1 RQ1: Effectiveness of RCA Identification

To answer **RQ1**, we selected the configuration that consistently achieved the highest diagnostic performance among all tested settings—combining *reasoning*, *memory sharing*, and *asynchronous execution*. This setup was used as our reference configuration to evaluate the overall effectiveness of adaptive agent generation for RCA under both synthetic and real-world conditions. During each experiment, the framework dynamically adjusted the number of

active adapted agents (max_agents = 5, 10, or 20) according to
the evolving diagnostic context. Results were averaged over 15 inde-
pendent runs to account for stochastic variability, and the reported
error bars indicate one standard deviation.

Table 3 summarizes the quantitative results obtained with this
configuration on the Nezha and OpenRCA datasets, using multiple
LLMs and different agent population sizes. The table reports preci-
sion metrics (PR@K) for both Nezha-OB and Nezha-TT from the
Nezha benchmark, and accuracy metrics (Partial and Correct) from
the OpenRCA benchmark. Entries marked as N/A indicate metrics
that were not evaluated within our experiments. Baseline values for
methods such as TraceDiag, Nezha, MRCA, and RCA-Agent were
directly retrieved from their respective publications to ensure fair
comparison across datasets and experimental settings.

Figures 2a and 2b illustrate the corresponding performance
trends and variability across runs.

Nezha results (synthetic benchmark). As shown in Figure 2a,
our adaptive agent generation framework substantially outperforms
the static MRCA baseline. Table 4 also reports that the best pro-
prietary model (GPT-4o) improves PR@1 by 12.66% ± 3.03, while
the best open-source model (Mistral Large 2) achieves a gain of
7.12% ± 2.87. Consistent improvements are also observed at higher
retrieval levels, reaching +12.1% for PR@3 and +5.2% for PR@5.
These results indicate that adaptive coordination enhances top-rank
precision and maintains stability across different retrieval depths,
confirming that reasoning and memory sharing lead to more robust
multi-step inference across modalities.

OpenRCA results (real-world dataset). Regarding the real-
world OpenRCA dataset (see Figure 2b and Table 5), the adap-
tive configuration significantly improves both fully and partially
correct diagnostic predictions compared to the RCA-Agent basel-
line. Claude 3.5, the best proprietary model, achieves a gain of
22.36% ± 1.97 for fully correct diagnoses, while Mistral Large 2,
the best open-source model, improves by 18.37%±2.14. For partially
correct diagnoses, GPT-4o and Mistral Large 2 show additional gains
of 13.97% and 8.25%, respectively. Overall, the adaptive generation
mechanism scales better with real-world telemetry heterogeneity,
yielding higher robustness under variable noise and dependency
structures.

**Discussion.** The results highlight that combining reasoning and
shared memory allows agents to iteratively refine intermediate
hypotheses, while asynchronous coordination reduces redundant
exploration and enhances convergence toward relevant root-cause
candidates. Moreover, the observed gains are proportionally higher
in the real-world setting, suggesting that adaptive agent generation
provides a stronger benefit when dealing with complex dependency
graphs and non-stationary observability data.

**RQ1:** Adaptive generation of agents—combining reasoning, shared memory, and asynchronous coordination—consistently improves the diagnostic accuracy of both MRCA and RCA-Agent across synthetic and real-world datasets. On the Nezha benchmark, it raises PR@1 by 12.66% ± 3.03 and maintains stable precision across PR@3 and PR@5. On the OpenRCA dataset, gains reach 22.36% ± 1.97 for fully correct and 13.97%
<table><caption>Table 3: Precision and Accuracy comparison across Nezha and OpenRCA datasets</caption><thead><tr><th rowspan="2">Max. Agents</th><th rowspan="2">Method</th><th colspan="3">Nezha-OB</th><th colspan="3">Nezha-TT</th><th colspan="2">OpenRCA</th></tr><tr><th>PR@1 (± std)</th><th>PR@3 (± std)</th><th>PR@5 (± std)</th><th>PR@1 (± std)</th><th>PR@3 (± std)</th><th>PR@5 (± std)</th><th>Partial (± std)</th><th>Correct (± std)</th></tr></thead><tbody><tr><td>-</td><td>TraceDiag (2023)</td><td>30.40 ± 0.00</td><td>64.30 ± 0.00</td><td>76.80 ± 0.00</td><td>35.60 ± 0.00</td><td>53.30 ± 0.00</td><td>68.90 ± 0.00</td><td>N/A</td><td>N/A</td></tr><tr><td>-</td><td>Nezha (2023)</td><td>32.10 ± 0.00</td><td>35.70 ± 0.00</td><td>35.70 ± 0.00</td><td>37.80 ± 0.00</td><td>44.40 ± 0.00</td><td>44.40 ± 0.00</td><td>N/A</td><td>N/A</td></tr><tr><td>-</td><td>MRCA (2023)</td><td>58.90 ± 0.00</td><td>80.30 ± 0.00</td><td>87.50 ± 0.00</td><td>62.20 ± 0.00</td><td>82.20 ± 0.00</td><td>88.90 ± 0.00</td><td>N/A</td><td>N/A</td></tr><tr><td>-</td><td>RCA-Agent (Claude 3.5)</td><td>N/A</td><td>N/A</td><td>N/A</td><td>N/A</td><td>N/A</td><td>N/A</td><td>11.34</td><td>17.31</td></tr><tr><td>-</td><td>RCA-Agent (GPT-40)</td><td>N/A</td><td>N/A</td><td>N/A</td><td>N/A</td><td>N/A</td><td>N/A</td><td>8.96</td><td>17.96</td></tr><tr><td>-</td><td>RCA-Agent (Gemini 1.5 Pro)</td><td>N/A</td><td>N/A</td><td>N/A</td><td>N/A</td><td>N/A</td><td>N/A</td><td>2.69</td><td>6.87</td></tr><tr><td>-</td><td>RCA-Agent (Llama 3.1 Instr.)</td><td>N/A</td><td>N/A</td><td>N/A</td><td>N/A</td><td>N/A</td><td>N/A</td><td>3.28</td><td>5.65</td></tr><tr><td rowspan="5">5</td><td>Claude 3.5</td><td>10.42 ± 1.85</td><td>22.36 ± 2.74</td><td>31.08 ± 2.58</td><td>12.73 ± 2.03</td><td>25.41 ± 2.91</td><td>33.74 ± 2.67</td><td>8.12 ± 2.53</td><td>7.85 ± 2.44</td></tr><tr><td>GPT-40</td><td>11.18 ± 1.97</td><td>23.52 ± 2.68</td><td>32.47 ± 2.49</td><td>13.55 ± 2.11</td><td>26.83 ± 2.86</td><td>34.69 ± 2.58</td><td>7.75 ± 2.45</td><td>7.52 ± 2.38</td></tr><tr><td>Gemini 1.5 Pro</td><td>9.87 ± 1.78</td><td>20.64 ± 2.59</td><td>28.32 ± 2.41</td><td>11.46 ± 1.95</td><td>23.19 ± 2.77</td><td>30.85 ± 2.52</td><td>6.75 ± 2.16</td><td>6.56 ± 2.21</td></tr><tr><td>Mistral Large 2</td><td>5.74 ± 1.52</td><td>10.83 ± 2.12</td><td>15.76 ± 2.08</td><td>6.12 ± 1.63</td><td>12.09 ± 2.34</td><td>17.28 ± 2.21</td><td>4.30 ± 1.99</td><td>4.23 ± 1.95</td></tr><tr><td>Llama 70B</td><td>4.91 ± 1.44</td><td>9.76 ± 2.08</td><td>14.23 ± 1.97</td><td>5.38 ± 1.51</td><td>10.94 ± 2.25</td><td>15.12 ± 2.03</td><td>3.52 ± 1.79</td><td>3.47 ± 1.81</td></tr><tr><td rowspan="4">10</td><td>Llama 8B</td><td>0.00 ± 0.00</td><td>0.00 ± 0.00</td><td>0.00 ± 0.00</td><td>0.00 ± 0.00</td><td>0.00 ± 0.00</td><td>0.00 ± 0.00</td><td>0.00 ± 0.00</td><td>0.00 ± 0.00</td></tr><tr><td>Mistral 7B</td><td>0.00 ± 0.00</td><td>0.00 ± 0.00</td><td>0.00 ± 0.00</td><td>0.00 ± 0.00</td><td>0.00 ± 0.00</td><td>0.00 ± 0.00</td><td>0.00 ± 0.00</td><td>0.00 ± 0.00</td></tr><tr><td>Claude 3.5</td><td>68.12 ± 2.41</td><td>92.37 ± 1.85</td><td>92.14 ± 1.96</td><td>69.42 ± 2.94</td><td>89.57 ± 3.28</td><td>92.16 ± 2.73</td><td>33.70 ± 1.97</td><td>28.89 ± 1.82</td></tr><tr><td>GPT-40</td><td>71.56 ± 3.02</td><td>91.12 ± 2.64</td><td>91.43 ± 1.57</td><td>70.83 ± 2.87</td><td>91.06 ± 3.05</td><td>91.04 ± 2.61</td><td>32.23 ± 1.92</td><td>31.93 ± 2.15</td></tr><tr><td rowspan="4">20</td><td>Gemini 1.5 Pro</td><td>70.24 ± 3.48</td><td>90.86 ± 3.25</td><td>91.98 ± 2.04</td><td>69.71 ± 3.12</td><td>89.92 ± 3.14</td><td>93.65 ± 2.84</td><td>28.12 ± 1.87</td><td>25.63 ± 1.86</td></tr><tr><td>Mistral Large 2</td><td>66.02 ± 4.11</td><td>88.95 ± 3.67</td><td>90.06 ± 2.75</td><td>65.02 ± 3.29</td><td>85.37 ± 3.44</td><td>87.08 ± 2.95</td><td>29.71 ± 2.01</td><td>26.12 ± 2.07</td></tr><tr><td>Llama 70B</td><td>64.73 ± 3.74</td><td>87.14 ± 3.89</td><td>89.23 ± 2.92</td><td>64.18 ± 3.36</td><td>84.51 ± 3.58</td><td>91.47 ± 3.02</td><td>25.16 ± 1.72</td><td>21.81 ± 1.83</td></tr><tr><td>Mistral 7B</td><td>18.45 ± 2.13</td><td>15.93 ± 3.48</td><td>17.64 ± 3.09</td><td>19.87 ± 2.46</td><td>19.42 ± 2.81</td><td>17.35 ± 2.63</td><td>0.57 ± 0.14</td><td>0.49 ± 0.13</td></tr><tr><td rowspan="5">20</td><td>Claude 3.5</td><td>69.05 ± 2.52</td><td>91.88 ± 1.92</td><td>92.72 ± 1.89</td><td>68.31 ± 2.76</td><td>90.21 ± 3.11</td><td>91.34 ± 2.65</td><td>33.61 ± 2.84</td><td>29.37 ± 2.43</td></tr><tr><td>GPT-40</td><td>70.22 ± 3.18</td><td>92.05 ± 2.51</td><td>90.97 ± 1.63</td><td>72.11 ± 2.95</td><td>90.44 ± 3.12</td><td>92.02 ± 2.58</td><td>32.47 ± 2.53</td><td>31.76 ± 2.60</td></tr><tr><td>Gemini 1.5 Pro</td><td>71.03 ± 3.27</td><td>90.12 ± 3.36</td><td>92.41 ± 2.12</td><td>68.95 ± 3.04</td><td>90.44 ± 3.21</td><td>92.88 ± 2.76</td><td>28.74 ± 2.61</td><td>26.67 ± 2.46</td></tr><tr><td>Mistral Large 2</td><td>65.74 ± 3.98</td><td>89.54 ± 3.52</td><td>89.12 ± 2.83</td><td>66.41 ± 3.37</td><td>84.72 ± 3.58</td><td>87.93 ± 3.04</td><td>29.65 ± 2.68</td><td>26.21 ± 2.46</td></tr><tr><td>Llama 70B</td><td>63.89 ± 3.66</td><td>88.02 ± 3.77</td><td>88.77 ± 2.86</td><td>65.12 ± 3.41</td><td>83.96 ± 3.64</td><td>92.01 ± 2.95</td><td>25.74 ± 2.36</td><td>21.84 ± 2.36</td></tr><tr><td rowspan="3">20</td><td>Llama 8B</td><td>23.41 ± 2.61</td><td>23.55 ± 3.61</td><td>21.08 ± 3.19</td><td>15.72 ± 2.19</td><td>14.28 ± 2.54</td><td>14.63 ± 2.47</td><td>0.56 ± 0.20</td><td>0.49 ± 0.19</td></tr><tr><td>Mistral 7B</td><td>17.92 ± 2.06</td><td>16.44 ± 3.37</td><td>17.05 ± 3.01</td><td>20.31 ± 2.52</td><td>18.67 ± 2.74</td><td>17.91 ± 2.56</td><td>0.44 ± 0.18</td><td>0.49 ± 0.22</td></tr></tbody></table>

Note: Green cells denote proprietary LLMs with the highest precision; Blue cells denote large LLMs with strong precision; Purple cells denote smaller LLMs with lower precision.

<figure><img src="image_4.png" /><figcaption>(a) Comparison with MRCA baseline</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>(b) Comparison with RCA-Agent baseline</figcaption></figure>

<caption>Figure 2: Comparison with MRCA and RCA-Agent baseline</caption>

<table><caption>Table 4: Performance Gains (%) on Nezha</caption><thead><tr><th>Metric</th><th>MRCA</th><th>Best Proprietary (± std)</th><th>Gain (%)</th><th>Best Open (± std)</th><th>Gain (%)</th></tr></thead><tbody><tr><td>OB PR@1</td><td>58.90 ± 0.00</td><td>GPT-40 (71.56 ± 3.02)</td><td>+12.66%</td><td>Mistral L2 (66.02 ± 4.11)</td><td>+7.12%</td></tr><tr><td>OB PR@3</td><td>80.30 ± 0.00</td><td>Claude 3.5 (92.37 ± 1.85)</td><td>+12.07%</td><td>Mistral L2 (88.95 ± 3.67)</td><td>+9.24%</td></tr><tr><td>OB PR@5</td><td>87.50 ± 0.00</td><td>Claude 3.5 (92.72 ± 1.89)</td><td>+5.22%</td><td>Mistral L2 (90.06 ± 2.75)</td><td>+2.56%</td></tr><tr><td>TT PR@1</td><td>62.20 ± 0.00</td><td>GPT-40 (72.11 ± 2.95)</td><td>+9.91%</td><td>Mistral L2 (65.02 ± 3.29)</td><td>+4.21%</td></tr><tr><td>TT PR@3</td><td>82.20 ± 0.00</td><td>GPT-40 (91.06 ± 3.05)</td><td>+8.86%</td><td>Mistral L2 (85.37 ± 3.44)</td><td>+3.17%</td></tr><tr><td>TT PR@5</td><td>88.90 ± 0.00</td><td>Gemini 1.5 Pro (93.65 ± 2.84)</td><td>+4.75%</td><td>Llama 70B (92.01 ± 2.95)</td><td>+3.11%</td></tr></tbody></table>

<table><caption>Table 5: Performance Gains (%) on OpenRCA</caption><thead><tr><th>Metric</th><th>RCA Agent</th><th>Best Proprietary</th><th>Gain (%)</th><th>Best Open</th><th>Gain (%)</th></tr></thead><tbody><tr><td>Partial</td><td>11.34 ± 0.00</td><td>Claude 3.5 (33.70 ± 1.97)</td><td>+22.36%</td><td>Mistral L2 (29.71 ± 2.01)</td><td>+18.37%</td></tr><tr><td>Correct</td><td>17.96 ± 0.00</td><td>GPT-40 (31.93 ± 2.15)</td><td>+13.97%</td><td>Mistral L2 (26.21 ± 2.07)</td><td>+8.25%</td></tr></tbody></table>

for partially correct diagnoses, confirming that adaptive coordination scales effectively with the complexity and heterogeneity of real-world telemetry.

## 5.2 RQ2: Structural and Behavioral Influence

To answer RQ2, we evaluated the experiments conducted under the asynchronous and sequential execution mode with a maximum of 10 adapted agents generated dynamically, which provided the most representative configuration for analyzing the internal behavior of the system. We compared four agent configurations—No Reasoning
<figure><img src="image_6.png" /><figcaption>Figure 3: Comparison of precision for the Nezha dataset (Nezha-OB and Nezha-TT) for sequential (left) and asynchronous (right) execution modes.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Figure 4: Comparison of OpenRCA results for sequential execution mode.</figcaption></figure>

+ *No Memory*, *No Reasoning + Memory*, *Reasoning + No Memory*, and *Reasoning + Memory*—to isolate and quantify the respective and combined effects of reasoning and memory mechanisms on diagnostic accuracy and consistency across all models and datasets.

Figures 3 and 4 summarize the results obtained under these four configurations for both benchmarks and for sequential (left site) and asynchronous (right side) execution modes over fifteen runs. The plots report the average diagnostic performance (mean ± std) for all models, illustrating the individual and combined effects of *reasoning* and *memory* mechanisms. On Nezha-OB, the comparison covers PR@1, PR@3, and PR@5 metrics, while the OpenRCA benchmark
<figure><img src="image_8.png" /><figcaption>Figure 5: Comparison with MRCA and RCA-Agent baseline</figcaption></figure>

provides complementary insights through Correct rates derived from real-world observability data.

As shown in Figures 3 and 4, both *reasoning* and *memory* mechanisms substantially improve diagnostic accuracy over all models and datasets under *asynchronous execution* (right part). On the Nezha benchmark (Figures 3b, 3d, 3f), enabling reasoning or memory individually leads to consistent gains compared to the (*No Reasoning* + *No Memory*) configuration, with reasoning showing a slightly stronger influence on PR@1 and PR@3. However, this effect is not clearly observed with the *sequential execution mode*, as shown in Figures 3a, 3c, 3e. In this case, the presence of *reasoning, memory*, or both contributes only marginally to the overall RCA performance, regardless of the evaluation metric. On the OpenRCA benchmark (Figure 4), the same general trend can be observed in both execution modes. *Reasoning* and *memory* improve the diagnostic rates under the sequential and asynchronous executions, respectively, and their combination achieves the best performance. This highlights a complementary effect that strengthens the accuracy of adaptive diagnosis.

However, a higher variance is observed for the *sequential execution mode* either on Nezha-OB or OpenRCA (Figures 3a, 3c, 3e, 4b), where only one agent operates at a time. In this setting, agents cannot benefit from parallel feedback or collaborative validation, making the overall process more sensitive to stochastic variations in LLM outputs. Small inconsistencies in intermediate reasoning or token sampling propagate through the chain without correction, leading to cumulative errors and a higher dispersion of diagnostic outcomes across RCA. While *memory* and *reasoning* influence RCA, they are also influenced by the execution strategy. With the *asynchronous mode*, agents interact dynamically, reinforcing each other's reasoning paths and achieving more stable and accurate outcomes. In contrast, the *sequential mode*—where agents operate one at a time—limits this cooperative feedback, leading to higher variance and less consistent diagnoses.

**RQ2:** Reasoning and memory consistently improve diagnostic performance under both execution modes, but their effects are more stable in the asynchronous configuration. In contrast, the sequential mode exhibits higher variance across repetitions, reflecting weak feedback between agents and reduced error correction during the reasoning process.

## 5.3 RQ3: Impact of Agent Population

To answer this question, we conducted a two-step analysis. First, we evaluated how the upper bound on the number of adapted agents affects diagnostic accuracy under the *asynchronous execution mode*. We evaluate the approach with a population of 5 adapted agents, then with 10, and finally with 20 agents, in order to observe the behavior of the approach under increasing coordination demands. Table 6 reports the average absolute accuracy gains when increasing the maximum number of agents from 5 to 10 and from 10 to 20 for all datasets. When the limit grows from 5 to 10 agents, accuracy increases substantially for all metrics and datasets, with a mean absolute improvement of +48.55% ± 3.30 for Nezha OB (PR@1), +46.60% ± 3.18 for Nezha TT, and +19.84% ± 2.43 for OpenRCA (Partial). These improvements confirm that expanding the agent population up to ten enhances reasoning diversity and collaborative exploration, enabling richer correlation among multi-modal observability data.

However, when the upper bound is extended from 10 to 20 agents, performance gains fall within the statistical margin of error (average ≤ 0.3%). This plateau indicates that the system reaches an optimal level of reasoning diversity around ten agents, beyond which additional agents contribute little or even introduce redundancy. Such behavior highlights that the proposed framework is not only adaptive in performance but also in structure: it learns when to stop expanding its population once the reasoning space is sufficiently covered.

Secondly, we analyzed the actual number of adapted agents generated during execution. As shown in Figure 5, the number of active agents increases with the upper limit but stabilizes before reaching it. On the Nezha datasets, nearly all agents are instantiated when the limit is set to 5 or 10, but when raised to 20, the system converges toward 12 to 14 active agents on average, with slightly higher variance across runs. A similar trend is observed on OpenRCA, where the active population saturates between 10 and 15 agents despite a higher limit.

These results demonstrate the self-regulating behavior of the adaptive orchestration process: it autonomously adjusts the agent population according to the complexity of the diagnostic task, balancing exploration and efficiency. Beyond ten agents, additional specialization becomes redundant, and the framework stabilizes without external intervention, preventing uncontrolled growth while preserving diagnostic quality.
<table><caption>Table 6: Absolute accuracy gains (%) between different MaxAgents configurations for Nezha (OB and TT) and OpenRCA, averaged over 15 runs.</caption><thead><tr><th rowspan="2">Agents</th><th colspan="3">Nezha-OB</th><th colspan="3">Nezha-TT</th><th colspan="2">OpenRCA</th></tr><tr><th>PR@1</th><th>PR@3</th><th>PR@5</th><th>PR@1</th><th>PR@3</th><th>PR@5</th><th>Partial</th><th>Correct</th></tr></thead><tbody><tr><td>5 → 10</td><td>48.55 ± 3.30</td><td>57.57 ± 3.59</td><td>53.01 ± 3.00</td><td>46.60 ± 3.18</td><td>53.59 ± 3.64</td><td>50.89 ± 3.25</td><td>19.84 ± 2.43</td><td>17.54 ± 2.43</td></tr><tr><td>10 → 20</td><td>-0.10 ± 4.32</td><td>0.09 ± 4.44</td><td>0.13 ± 3.50</td><td>0.22 ± 4.09</td><td>-0.05 ± 4.42</td><td>0.11 ± 3.87</td><td>0.21 ± 2.73</td><td>0.25 ± 2.66</td></tr></tbody></table>

**RQ3:** Increasing the upper limit of agent generation enhances flexibility but does not lead to uncontrolled growth. The framework autonomously stabilizes around ten to fifteen adapted agents, dynamically adapting population size to task complexity while maintaining both accuracy and efficiency.

# 6 Related Work

**LLM- and agent-based RCA.** Recent advances show that LLMs can support multiple software engineering tasks—code generation [20, 28], testing [36, 53], debugging [7, 54], and runtime adaptation [40, 41]. For RCA, they have been exploited through (i) fine-tuning for incident reasoning [1, 9], (ii) embedding-based semantic encoding [6, 25], and (iii) prompting and retrieval-augmented reasoning [27, 42, 60]. Multi-agent frameworks further distribute diagnostic roles among fixed agents [8, 46, 55], improving scalability but lacking runtime flexibility.

**Classical RCA.** Earlier RCA approaches relied on statistical or causal models—e.g., Granger causality and structural equations—to link metrics across services. Recent multi-modal methods integrate metrics, logs, and traces for finer-grained reasoning, such as Nezha [56], MRCA [50], and MULAN [63].

In contrast to previous RCA frameworks such as MRCA [50] and RCA-Agent [55], which rely on a predefined and static set of agents with fixed roles, our approach introduces a self-adaptive mechanism for dynamic agent generation and orchestration. Instead of configuring the number and type of agents a priori, the proposed framework autonomously instantiates, reconfigures, and retires adapted agents at runtime according to the evolving diagnostic context.

# 7 Threats to Validity and Limitations

**Internal Validity.** Minor discrepancies may exist because we reused reported results from MRCA and RCA-Agent instead of re-running their full pipelines. To ensure consistency, all experiments relied on official datasets (Nezha and OpenRCA) and evaluation libraries. The stochastic behavior of LLM inference was mitigated by repeating each configuration fifteen times and averaging results.

**External Validity.** The experiments were conducted in controlled environments and limited to microservice-based systems. Although this setup provides reproducible conditions, large-scale or heterogeneous architectures may introduce new challenges in scalability and synchronization.

**Construct Validity.** Metrics such as Precision@K and Exact/Partial accuracy effectively capture localization accuracy but only partially reflect interpretability or adaptation efficiency. Future work will

integrate complementary indicators to evaluate reasoning and coordination quality.

**Limitations.** Our framework currently focuses on adaptive diagnosis rather than automated recovery and depends on the reasoning and error-handling capacities of the underlying LLM. This impacts key steps in the framework, such as Buildspec construction, selection of appropriate agents and tools or RCA.

# 8 Conclusion

This paper presented a self-adaptive agent generation framework for RCA that leverages multi-modal observability data and dynamic orchestration. Unlike traditional [50, 56, 63] and recent agentic approaches [1, 8, 35, 46, 55], our framework operates without pre-defined diagnostic plans or fixed agent populations. Instead, it dynamically generates, specializes, and coordinates adapted agents through the Parser and Executor agents, enabling context-aware reasoning and efficient collaboration via shared memory.

Experiments on both synthetic (Nezha) and real-world (OpenRCA) benchmarks show consistent improvements, with up to +12% accuracy gains on fault-injected data and +22% on real-world failures. Reasoning and memory mechanisms enhance interpretability and coordination, while adaptive orchestration aligns reasoning effort with task complexity.

Future work will address three complementary directions. First, we will analyze how variability in adaptive orchestration affects agent coordination and performance across diverse contexts. Second, we plan to evaluate the operational cost, the resource analysis, and the energy footprint of adaptive coordination to balance diagnostic accuracy and resource efficiency. Third, we will assess the framework's behavior in real-time and production settings to study its stability under operational workloads, and extend existing datasets with new failure types—especially configuration and memory errors—currently underrepresented in benchmarks [16].

# Acknowledgments

This work was supported by a French government grant managed by the Agence Nationale de la Recherche under the France 2030 program, reference "ANR-23-PECL-0008".

# Data Availability

All artifacts from this study, including the code, results, datasets, and logs, are available at https://doi.org/10.5281/zenodo.18302941.
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