Graph Thinking with AI: Algorithms that Power Real Systems
Graphs aren’t just academic—they power the backbone of real systems: workflows (Airflow DAGs), build pipelines (Bazel), data processing (Spark DAGs), and microservice dependencies (Jaeger).
This session demystifies classic graph algorithms—BFS, DFS, topological sort, shortest paths, and cycle detection—and shows how to connect them to real-world systems.
You’ll also see how AI tools like ChatGPT and graph libraries (Graphviz, NetworkX, D3) can accelerate your workflow: generating adjacency lists, visualizing dependencies, and producing test cases in seconds.
You’ll leave with reusable patterns for interviews, architecture reviews, and production systems.
You’ll leave with reusable patterns for interviews, architecture reviews, and production systems.
Why Now
- Graphs underpin the modern stack: Airflow DAGs, Spark, Kubernetes, Jaeger, and CI/CD pipelines.
- AI tools can rapidly generate, visualize, and validate graph structures—bridging algorithmic theory and practical engineering.
- Graph literacy now distinguishes great developers from average system designers.
Problems Solved
- Translating algorithmic concepts into production patterns
- Lack of intuition about DAGs, dependency graphs, or routing systems
- Difficulty visualizing large graph structures quickly
- Limited practice applying BFS/DFS/topo sort outside interview prep
Learning Outcomes
- Apply BFS/DFS, topological sort, and shortest path algorithms in production systems
- Translate graph theory into schedulers, dependency analyzers, and routing services
- Use AI (ChatGPT/Copilot) for quick generation of adjacency lists, test data, and graph visualization
- Map graphs to system design conversations: latency, scaling, dependencies
- Build your own reusable Graph Thinking toolkit for architecture and interviews
Agenda
Opening: From Whiteboard to Production
Why every large-scale system is a graph in disguise.
How workflows, microservices, and dependency managers rely on graph structures.
Pattern 1: Graphs in the Real World
Examples:
- Workflows: Airflow, Dagster
- Builds: Bazel
- Data pipelines: Spark
- Services: Jaeger tracing DAGs Show how each maps to graph nodes, edges, and cycles.
Pattern 2: Core Algorithms Refresher
- BFS/DFS: Reachability, search, and crawl use cases
- Dijkstra / A*: Routing, latency, and cost optimization
- Topological Sort: Scheduling builds, DAG execution order
- Cycle Detection: Fail-fast prevention in workflows and dependency graphs
Pattern 3: AI-Assisted Graph Engineering How to use AI tools to accelerate graph work:
- Generate adjacency lists from plain-text prompts
- Auto-create test cases for reachability and cycle detection
- Use Graphviz / NetworkX / D3 to visualize graphs instantly
- Validate algorithm correctness interactively
Pattern 4: Graph Patterns in Architecture Mapping algorithms to system design:
- BFS → discovery & dependency mapping
- DFS → deep audits & lineage analysis
- Dijkstra → route optimization & latency modeling
- Topo sort → job orchestration & CI/CD scheduling How architects can embed graph thinking into design reviews.
Pattern 5: AI Demo Prompt → adjacency list → Graphviz/NetworkX render → algorithmic validation. Demonstrate quick prototyping workflow with AI assistance.
Wrap-Up: From Algorithms to Architectural Intuition How graph literacy improves system reliability and scalability. Checklist and reusable templates for ongoing graph-based reasoning.
Key Framework References
- NetworkX / Graphviz / D3.js: Visualization and validation libraries
- Apache Airflow / Spark / Bazel / Jaeger: Real-world DAG examples
- AI Tools (ChatGPT, Copilot): Adjacency generation, testing, and explanation
- Big-O Foundations: BFS/DFS/Dijkstra complexity reminders for performance analysis
Takeaways
- Graph Thinking Checklist: Nodes, edges, cycles, and DAG validation
- AI Prompt Pack: Templates for adjacency generation, test creation, and visualization
- Algorithm Snippet Starter Kit: BFS, DFS, Dijkstra, Topo Sort in Python/JS
- Architecture Mapping Guide: Graph patterns → system use cases
- Mindset: Move from memorizing algorithms → to engineering with them
About Rohit Bhardwaj
Rohit Bhardwaj is a Director of Architecture working at Salesforce. Rohit has extensive experience architecting multi-tenant cloud-native solutions in Resilient Microservices Service-Oriented architectures using AWS Stack. In addition, Rohit has a proven ability in designing solutions and executing and delivering transformational programs that reduce costs and increase efficiencies.
As a trusted advisor, leader, and collaborator, Rohit applies problem resolution, analytical, and operational skills to all initiatives and develops strategic requirements and solution analysis through all stages of the project life cycle and product readiness to execution.
Rohit excels in designing scalable cloud microservice architectures using Spring Boot and Netflix OSS technologies using AWS and Google clouds. As a Security Ninja, Rohit looks for ways to resolve application security vulnerabilities using ethical hacking and threat modeling. Rohit is excited about architecting cloud technologies using Dockers, REDIS, NGINX, RightScale, RabbitMQ, Apigee, Azul Zing, Actuate BIRT reporting, Chef, Splunk, Rest-Assured, SoapUI, Dynatrace, and EnterpriseDB. In addition, Rohit has developed lambda architecture solutions using Apache Spark, Cassandra, and Camel for real-time analytics and integration projects.
Rohit has done MBA from Babson College in Corporate Entrepreneurship, Masters in Computer Science from Boston University and Harvard University. Rohit is a regular speaker at No Fluff Just Stuff, UberConf, RichWeb, GIDS, and other international conferences.
Rohit loves to connect on http://www.productivecloudinnovation.com.
http://linkedin.com/in/rohit-bhardwaj-cloud or using Twitter at rbhardwaj1.