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Traceability and Accountability in Role-Specialized Multi-Agent LLM Pipelines

Amine Barrak

TL;DR

The article tackles trust in sequential multi-agent LLM pipelines by introducing a traceable blame-attribution framework in a Planner→Executor→Critic configuration. It conducts a large-scale empirical study across eight configurations and three benchmarks using three frontier LLMs to quantify how accountability affects accuracy, cost, and latency, and to reveal error origination and propagation dynamics. Key findings show that structured, accountable handoffs markedly improve performance, that planner quality dominates pipeline failure risk, and that the optimal accuracy-cost-latency balance is task-dependent, often favoring heterogeneous pipelines. The work provides a practical, data-driven methodology for designing, tracing, and debugging robust, predictable multi-agent systems, and contributes dataset resources for replication and extension.

Abstract

Sequential multi-agent systems built with large language models (LLMs) can automate complex software tasks, but they are hard to trust because errors quietly pass from one stage to the next. We study a traceable and accountable pipeline, meaning a system with clear roles, structured handoffs, and saved records that let us trace who did what at each step and assign blame when things go wrong. Our setting is a Planner -> Executor -> Critic pipeline. We evaluate eight configurations of three state-of-the-art LLMs on three benchmarks and analyze where errors start, how they spread, and how they can be fixed. Our results show: (1) adding a structured, accountable handoff between agents markedly improves accuracy and prevents the failures common in simple pipelines; (2) models have clear role-specific strengths and risks (e.g., steady planning vs. high-variance critiquing), which we quantify with repair and harm rates; and (3) accuracy-cost-latency trade-offs are task-dependent, with heterogeneous pipelines often the most efficient. Overall, we provide a practical, data-driven method for designing, tracing, and debugging reliable, predictable, and accountable multi-agent systems.

Traceability and Accountability in Role-Specialized Multi-Agent LLM Pipelines

TL;DR

The article tackles trust in sequential multi-agent LLM pipelines by introducing a traceable blame-attribution framework in a Planner→Executor→Critic configuration. It conducts a large-scale empirical study across eight configurations and three benchmarks using three frontier LLMs to quantify how accountability affects accuracy, cost, and latency, and to reveal error origination and propagation dynamics. Key findings show that structured, accountable handoffs markedly improve performance, that planner quality dominates pipeline failure risk, and that the optimal accuracy-cost-latency balance is task-dependent, often favoring heterogeneous pipelines. The work provides a practical, data-driven methodology for designing, tracing, and debugging robust, predictable multi-agent systems, and contributes dataset resources for replication and extension.

Abstract

Sequential multi-agent systems built with large language models (LLMs) can automate complex software tasks, but they are hard to trust because errors quietly pass from one stage to the next. We study a traceable and accountable pipeline, meaning a system with clear roles, structured handoffs, and saved records that let us trace who did what at each step and assign blame when things go wrong. Our setting is a Planner -> Executor -> Critic pipeline. We evaluate eight configurations of three state-of-the-art LLMs on three benchmarks and analyze where errors start, how they spread, and how they can be fixed. Our results show: (1) adding a structured, accountable handoff between agents markedly improves accuracy and prevents the failures common in simple pipelines; (2) models have clear role-specific strengths and risks (e.g., steady planning vs. high-variance critiquing), which we quantify with repair and harm rates; and (3) accuracy-cost-latency trade-offs are task-dependent, with heterogeneous pipelines often the most efficient. Overall, we provide a practical, data-driven method for designing, tracing, and debugging reliable, predictable, and accountable multi-agent systems.

Paper Structure

This paper contains 21 sections, 1 equation, 3 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Multi-Agent Collaboration in Autonomous Software Engineering
  4. Traceability and Correction in Agentic Pipelines
  5. Design Study
  6. Prompts Datasets Collection
  7. Model Selection Rationale
  8. Experimental Conditions
  9. Evaluation Metrics
  10. Blame Assignment in Multi-Agent Pipelines
  11. Results
  12. RQ1: How do sequential multi-agent pipelines compare to the performance of monolithic LLMs?
  13. Baseline Performance of Monolithic Models
  14. Impact of Accountable Protocol on Pipeline Performance
  15. RQ2: How do individual LLMs perform in specialized roles, and what are the dynamics of error propagation and correction within the pipeline?
  16. ...and 6 more sections

Figures (3)

  • Figure 1: A sequential multi-agent pipeline. A failure in the final output makes debugging and assigning responsibility a critical challenge.
  • Figure 2: Impact of pipeline architecture on accuracy.
  • Figure 3: Accuracy vs. cost with latency color-coded; Pareto frontier shown (dashed).