mlsanityData to 14 Oct 2026

Observability and Fault Injection for LLM-Based Multi-Agent Systems in Software Engineering

arXiv 2608.24271 ↗First posted 25 Aug 2026cs.SEcited by 3

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

Large Language Model-based multi-agent systems are increasingly explored for software engineering tasks, but they remain difficult to inspect, debug, and evaluate under controlled failures. We present llmmas-otel, a lightweight and framework-agnostic tool that combines OpenTelemetry-based distributed tracing with fault injection for LLM-based multi-agent systems in software engineering workflows. The tool instruments agent executions with trace-aligned telemetry across workflow phases, agent steps, inter-agent communication, tool calls, and LLM invocations, and supports targeted fault injection at selected interaction points. This makes it possible to compare baseline and faulty executions in a reproducible way and inspect the effects through aligned traces and run artifacts. We describe the motivation, architecture, implementation, current capabilities, and initial validation of the tool on a minimal demo workflow and a real LLM-based multi-agent system for software development.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 9 of its 14 references that are in the map. First posted on arXiv 25 Aug 2026.

Builds on

9 of 14 references.

Closest ancestors · 9

Same direction, newest first

  1. 2025Aegis: Automated Error Generation and Attribution for Multi-Agent Systems17 Sep
  2. 2025AgenTracer: Who Is Inducing Failure in the LLM Agentic Systems?3 Sep
  3. 2025LumiMAS: A Comprehensive Framework for Real-Time Monitoring and Enhanced Observability in Multi-Agent Systems17 Aug
  4. 2025TRAIL: Trace Reasoning and Agentic Issue Localization13 May
  5. 2025Which Agent Causes Task Failures and When? On Automated Failure Attribution of LLM Multi-Agent Systems30 Apr
  6. 2025Why Do Multi-Agent LLM Systems Fail?17 Mar
  7. 2024AgentOps: Enabling Observability of LLM Agents8 Nov
  8. 2024HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scale9 Sep
  9. 2023ChatDev: Communicative Agents for Software Development16 Jul

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026When Agentic Executions Fail: Detecting and Localizing Runtime Faults from Telemetry
  2. 2025Towards Engineering Multi-Agent LLMs: A Protocol-Driven Approach
  3. 2026AgentChaos: Chaos Engineering for Agent Systems via Programmatic Fault Injection
  4. 2026LLM-Based Agentic Systems for Software Engineering: Challenges and Opportunities
  5. 2026Automated structural testing of LLM-based agents: methods, framework, and case studies
  6. 2026Understanding Agent-Reactive Bugs at the Model-Harness Boundary: An Empirical Study of LLM Agent Issue Reports
  7. 2026MAS-FIRE: Fault Injection and Reliability Evaluation for LLM-Based Multi-Agent Systems
  8. 2026When Agents Fail to Act: A Diagnostic Framework for Tool Invocation Reliability in Multi-Agent LLM Systems