Trust-Aware Multi-Agent Traceability: Confidence-Calibrated Knowledge Graphs for Consistent Software Artifact Management
arXiv:2606.17203v1 Announce Type: cross Abstract: Multi-agent AI systems are increasingly used to automate software engineering tasks including requirements analysis, architecture design, test generation, and traceability linking. When these agents operate as a sequential pipeline over shared software artifacts, errors and low-confidence decisions made by upstream agents propagate to downstream stages, producing orphaned requirements, contradictory links, and compliance gaps that pose significant risks in safety-critical domains. We propose a trust-aware coordination framework where a shared k
As multi-agent AI systems become more prevalent in critical software engineering, the need for robust error propagation and trust management mechanisms is escalating.
This research addresses a fundamental challenge for deploying AI agents in safety-critical domains, where unchecked errors can lead to significant risks and failures.
The proposed framework introduces confidence-calibrated knowledge graphs to improve the consistency and reliability of AI agent pipelines in complex software development.
- · Software developers
- · High-assurance software sectors
- · AI agent developers
- · Safety-critical industries
- · Companies relying on unvalidated AI agent pipelines
- · Sectors with low fault tolerance
Increased reliability and adoption of AI agents in software engineering workflows.
Faster development cycles for complex and critical software, reducing time-to-market.
Potential for AI agents to autonomously manage and develop entire software systems with minimal human oversight.
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Read at arXiv cs.AI