RohitBhardwaj

Claude Code is not just a coding assistant. Used casually, it can create fast prototypes. Used architecturally, it can become a powerful engineering accelerator for discovery, refactoring, test generation, documentation, architecture reviews, and modernization. This talk teaches architects, tech leads, and senior developers how to use Claude Code as part of a governed software delivery system. We will explore how to structure repositories, write effective CLAUDE.md guidance, create architecture guardrails, generate tests, review AI-produced code, and use Claude Code without turning your codebase into an ungoverned “vibe coding” experiment. The core message is simple: Claude Code should not replace architecture judgment. It should amplify it. Anthropic’s own Claude documentation emphasizes prompting clarity, examples, structured guidance, and agentic workflows, which makes architecture-level instructions especially important when using Claude in engineering systems.

Coding interviews and production systems share the same challenge: transforming vague problems into correct, efficient, and explainable solutions.
This talk introduces a 7-step algorithmic thinking framework that begins with a brute-force baseline and evolves toward an optimized, production-grade solution—using AI assistants like ChatGPT and GitHub Copilot to accelerate ideation, edge-case discovery, and documentation, without sacrificing rigor. Whether you’re solving array or graph problems, optimizing data pipelines, or refactoring legacy logic, this framework builds the discipline of clarity before optimization—and shows how to use AI responsibly as a thinking partner, not a shortcut.

Most enterprise LLM failures aren’t technical — they’re trust failures. Models hallucinate, drift from source truth, or produce outputs with no provenance. For regulated industries, that’s unacceptable.
This session introduces GraphRAG — a breakthrough approach combining knowledge graphs (Neo4j) with retrieval-augmented generation to deliver traceable, explainable, and auditable AI outputs.
You’ll learn how to design, evaluate, and deploy GraphRAG architectures aligned with the EU AI Act, NIST AI Risk Management Framework, and enterprise AI governance standards.

AI inference is no longer a simple model call—it is a multi-hop DAG of planners, retrievers, vector searches, large models, tools, and agent loops. With this complexity comes new failure modes: tail-latency blowups, silent retry storms, vector store cold partitions, GPU queue saturation, exponential cost curves, and unmeasured carbon impact.

Classic system design teaches you how to scale requests. AI-era architecture teaches you how to scale reasoning, retrieval, tokens, tools, trust, and cost. In the AI era, the best architects do not just draw boxes. They design authority, evidence, fallback, observability, and cost controls into every system. Modern system design has entered a new era. It’s no longer enough to optimize for uptime and latency — today’s systems must also be AI-ready, token-efficient, trustworthy, and resilient. Whether building global-scale apps, powering recommendation engines, or integrating GenAI agents, architects need new skills and playbooks to design for scale, speed, and reliability. This full-day workshop blends classic distributed systems knowledge with AI-native thinking. Through case studies, frameworks, and hands-on design sessions, you’ll learn to design systems that balance performance, cost, resilience, and truthfulness — and walk away with reusable templates you can apply to interviews and real-world architectures.

Autonomous LLM agents don’t just call APIs — they plan, retry, chain, and orchestrate across multiple services.
That fundamentally changes how we architect microservices, define boundaries, and operate distributed systems. This session delivers a practical architecture playbook for Agentic AI integration — showing how to evolve from simple request/response designs to resilient, event-driven systems.
You’ll learn how to handle retry storms, contain failures with circuit breakers and bulkheads, implement sagas and outbox patterns for correctness, and version APIs safely for long-lived agents. You’ll leave with reference patterns, guardrails, and operational KPIs to integrate agents confidently—without breaking production systems.

Enterprises are moving from single AI agents to networks of agents that trigger thousands of API calls, retries, and tool-chains per prompt. Without orchestration discipline and APIs built for AI-scale, systems buckle under bursty load, retry storms, cache-miss spikes, inconsistent decisions, and runaway costs. This talk shows how to combine MCP (Model Context Protocol) with proven inter-agent orchestration patterns — Supervisor, Pub/Sub, Blackboard, Capability Router — and how to harden APIs for autonomous traffic using rate limits, dedupe, backpressure, async workflows, resilient caching, and autoscaling without bill shock.

Certification-readiness talk with architecture scenarios, exam-domain mapping, practical examples, and production-design guidance. Claude is no longer just a chatbot for writing answers. It is becoming part of how developers design, build, review, and automate software. Claude Code can help developers work across repositories, Claude Code GitHub Actions can respond to issues and pull requests, MCP can connect Claude to external tools and systems, and the Claude Agent SDK enables developers to build custom agentic workflows. This creates a new skill requirement for architects: knowing how to design Claude-powered systems that are safe, measurable, governable, and production-ready. This talk provides a practical readiness roadmap for developers and architects preparing for Claude architecture work and Claude certification-style expectations. We will cover Claude platform fundamentals, Claude Code workflows, MCP/tool governance, Agent SDK patterns, API design, RAG, evals, observability, security, and enterprise deployment concerns. Participants will also work through certification-style scenarios that test architectural judgment, not memorization.

A live, end-to-end walkthrough of an AWS Well-Architected Review for a GenAI app. You’ll learn how to apply the AWS Generative AI Lens across the six pillars, then add Bedrock Guardrails and Knowledge Bases (RAG) to raise reliability, safety, and accuracy. You’ll leave with a reusable checklist and a prioritized remediation plan.

Large Language Models unlock new capabilities—and expose brand-new attack surfaces.
From prompt injection and data exfiltration to model denial-of-service and insecure plugin calls, adversaries are exploiting weaknesses traditional AppSec never anticipated. The new OWASP LLM Top-10 provides a shared vocabulary for AI risks; this session turns that list into actionable engineering practice.
You’ll learn how to threat-model LLM endpoints, design guardrails that actually block malicious behavior, sandbox tools and plug-ins with least privilege, and align your mitigations to the NIST AI Risk Management Framework for audit-ready governance.

LLM agents don’t just fetch data—they decide and act. To support planning and chaining, microservices must expose not only endpoints but also semantic context: what entities mean, which states are valid, which actions come next, and why decisions were made. This talk shows how to evolve from data-only APIs to MCP-aware, semantically rich services using JSON-LD/Schema.org, Hydra-style affordances, domain events, and OpenAPI metadata. You’ll learn retrofit vs greenfield paths, see cross-industry demos, and leave with a migration checklist that makes your services truly agent-ready.