The Fifth Elephant
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The Fifth Elephant Pune edition

Call for Proposals

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Pingjin Wen

Agentic Triage Engine

Submitted Jan 9, 2026

Indicate the track in which your submission fits: Track 1 AI in Software Development Life Cycle (SDLC) Type of submission: 30 mins talk

Challenge

Complex software products span a wide range of capabilities - hardware and software - hypervisors, containers, storage, servers, networking, systems, control plane, management plane, security - for on-prem and public cloud systems.

Software Development lifecycle includes the qualification of product features, as they are developed, using tests in isolated and integrated environments - to test individual functionality, the overall system, in happy path and negative scenarios, as well as cross-functional workflows involving multiple layers of software.

Such tests are executed frequently (daily, weekly etc) to detect breakages early in the software development lifecycle. Test failures need to be triaged efficiently, for maximum benefit.

Agentic Triage Engine uses Agentic and AI tools & techniques to efficiently triage test failures, by integrating into the Quality Assurance (QA) infrastructure, solicit and incorporate QA engineers’ review and feedback to reduce overall time to triage failures.

Key Takeaways

  • Agentic and AI approaches for Efficiency in Triage.
  • Determine duplicate vs fresh failures quickly.
  • Integrate Domain Expert (QA Engineer) feedback seamlessly.

Target Audience

  • Software Engineers and AI Developers directly involved in software development lifecycle.
  • Verification and Validation Engineers responsible for Qualification of Software products.
  • Engineering Managers and Tech Leads responsible for bringing efficiency in software development lifecycle

Speakers Bio

Pingjin Wen: Pingjin Wen is a Senior Staff Engineer at Nutanix with extensive experience in testing large-scale distributed systems and building high-impact test automation platforms. His current work centers on bringing AI into the QA lifecycle, enabling data-driven quality signals, automated insights from test failures, and more efficient end-to-end SDLC workflows.

Geetha Srikantan Geetha Srikantan contributed to early AI/ML projects for the USPS, IRS and other federal agencies, and has worked on several products spanning Streaming Media, Hyper-Converged Infrastructure, Data Protection. More recently, she has been involved in Root Cause Analysis projects at Nutanix.

Tingting Li: Tingting Li is a Lead Engineer in the System Test Core Data Path team. She is responsible for ensuring the quality of the Cloud Native AOS product. One of the team’s founding engineers initiated the Triage Genie AI project, leveraging AI technologies such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI-driven workflows to automate the QA triage and test process. A patent is currently pending for the LogRAG project.

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