Sohail Shaikh
Data Scientist
Atlanta, Georgia, United States
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Sohail Shaikh is a Data Scientist at with 9+ years of overall professional experience across AI, NLP, analytics, marketing, and software development. He holds an MS in Information Systems Management from Carnegie Mellon University, an MBA in Marketing, and a Bachelor’s degree in Computer Engineering. His work focuses on building practical AI systems for conversational intelligence, including LLM workflows, RAG pipelines, semantic search, topic modeling, and knowledge extraction from large-scale unstructured data. He has also received professional recognition, including a Flipkart Innovation Award awarded to the top 1% of employees.
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From Stack Trace to Fix Path: GitHub Troubleshooting Graphs with Neo4j
Developers often search across GitHub issues, pull requests, release notes, documentation, and source files to understand why an error happened and how it was fixed. Traditional code search can find matching text, but it usually does not connect the full troubleshooting trail: the original error report, related issue discussion, fixing pull request, changed files, release version, and updated documentation.
In this session, the speakers will show how to build a GitHub troubleshooting graph with Neo4j. They will demonstrate how public GitHub repository data can be modeled as a graph of Issues, Pull Requests, Files, Releases, Comments, Docs, Error Signatures, and Fix Paths. The session will show how Neo4j can combine semantic search with Cypher traversal to move from an error message or stack trace to the most likely issue, fix PR, affected files, and supporting evidence.
You will learn how to design the graph schema, ingest public GitHub data, create embeddings for issues and docs, use hybrid retrieval, and write Cypher queries that explain the path from problem to fix. The session will also cover how to evaluate the system using closed issues and linked pull requests as ground truth.
By the end, you will understand how graph-based retrieval can go beyond code search and help developers debug faster, trace fixes, analyze upgrade issues, and build more useful AI coding assistants.
AI Agents Are Becoming Distributed Systems Problems
As AI agents become more autonomous and interconnected, many of the hardest engineering problems are starting to resemble classic distributed systems challenges rather than traditional application development problems.
In production environments, multi-agent systems introduce orchestration complexity, retry amplification, context propagation issues, non-deterministic execution paths, observability gaps, and cascading failure patterns that closely mirror the behavior of large-scale distributed systems.
This session explores how real-world agentic AI workflows begin to inherit distributed systems failure modes as they scale, and what engineering teams can learn from decades of distributed systems design principles. We will examine practical production lessons around orchestration reliability, tracing, concurrency control, retry handling, state management, and operational debugging for AI agent architectures.
Attendees will leave with a practical framework for thinking about AI agents as operational systems rather than isolated AI components, along with concrete strategies for building more reliable, observable, and scalable agent-based applications.
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