Intelligent Bug Triage and Root Cause Analysis Agent

Semiconductor GenAI case study to accelerate bug diagnosis and reduce fix-cycle delays.

About Customer

The customer is a semiconductor engineering team handling high-volume simulation and verification issues during active chip development. Debug pipelines involved logs, reports, and code context spread across multiple systems.

The organization needed faster root-cause workflows to reduce time-to-market risk and improve release predictability.

Semiconductor Verification Operations Managing large-scale RTL debugging and simulation triage across engineering teams Verification Engineers Engineers investigate simulation failures under tight tape-out cycles Debug Evidence Stack Error Logs RTL Failing RTL Historical Bug Reports DATA Faster Root-Cause Triage Teams need rapid ownership signals to reduce debug turnaround time

Problem Statement

Engineers manually correlated simulation logs, historical bug reports, and design sources to identify probable causes. This process was repetitive and slow, especially when issue patterns overlapped across modules and versions.

The triage bottleneck delayed severity classification and ownership assignment, extending bug-fix cycles and release timelines.

  • Manual evidence correlation across logs, reports, and code.
  • Slow root-cause identification for complex bug chains.
  • Increased time-to-market due to prolonged fix cycles.

Solution Architecture

Zettabolt deployed an Intelligent Bug Triage Agent that combines RAG (Retrieval-Augmented Generation) over historical fix reports with ZettaLens-built secure connectors that pull the failing RTL (Register-Transfer Level - the hardware code that describes the chip) straight from the design database. The agent synthesizes logs, source, and history to instantly generate an accurate bug summary, classify severity, and recommend a probable fix and owner - eliminating 100% of the manual log-vs-history-vs-code correlation that used to stretch bug-fix cycles. The result: 60X+ faster root-cause identification with up to 45% bug-fix cycle reduction. Here is how we integrated the pipeline:

Intelligent Bug Triage & Root-Cause AgentSim FailureLogs · RTL · HistoryTriage AgentRAG: Past FixesTool: Design DBLLM: SynthesisZettaLens ToolsAuto Bug ReportSEVERITY: CriticalCAUSE: Race in FIFO ptrFIX: Sync 2-flop CDCOWNER: @rtl-teamSimilar fix: BUG-2417RTL: fifo.sv:14260Xfaster RCA

Implementation Highlights

  • Built a bug triage agent that automates initial correlation and classification.
  • Used RAG over historical bug and fix data to improve relevance and speed.
  • Applied secure ZettaLens tool wrappers to query design context when needed.

Implementation context: The deployment prioritized high-frequency issue classes and integrated seamlessly into existing bug workflows. Engineers received structured summaries, likely root causes, and ownership hints early in the cycle, reducing manual effort and improving triage consistency across teams.

LLM RAG AI Agent Tool Wrappers Bug Triage

Business Impact

60X+ faster root-cause identification
Up to 45% bug-fix cycle reduction
100% elimination of manual first-pass effort
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