Sachin Gupta
Technical Leader at eBay
San Jose, California, United States
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I am a senior backend and platform engineer at eBay with over 14 years of experience building and modernizing enterprise systems. I work on large scale billing and financial platforms that demand high reliability, strong security, and precise data correctness. I spend most of my time modernizing legacy architectures, designing cloud ready services, and building APIs and pipelines that hold up under heavy traffic and real world failure modes.
Lately, I have been integrating AI into engineering workflows, focusing on diagnostics, observability, testing, and automation that help teams move faster without sacrificing safety.
On stage, I share field tested patterns and mistakes to avoid, from migrations and cutovers to incident driven design and operational readiness. My goal is to give engineers practical takeaways they can apply immediately when building and modernizing systems at scale.
Area of Expertise
Topics
From OpenAPI Spec to MCP Server
Most teams already have hundreds of internal APIs described in OpenAPI, but very few have the time or budget to hand build MCP servers for all of them. In this session, I will show how to take a real OpenAPI 3.1 specification and generate a working MCP server using open source tooling. The generated server includes Streamable HTTP transport, tool discovery, and typed input validation, giving agents a discoverable, invocable interface without writing MCP specific code.
The demo, however, is only the starting point. The core of the talk explains how OpenAPI operations map to MCP tools, how request and response schemas become typed tool inputs and outputs, and how HTTP failures can be translated into structured MCP errors that agents can handle programmatically. From there, we will examine the practical issues that make generated servers fail in production, including pagination, file uploads, polymorphic responses, auth propagation, and tool name collisions.
I will also show the tuning needed to make these servers dependable in real environments, including naming cleanup, schema refinement, auth handling, and guardrails for agent usability. The session concludes with a Spring Boot Gradle plugin that fits into an existing CI pipeline and a decision matrix for choosing which APIs are the best candidates for conversion first. Attendees will leave with a repeatable pipeline, a working example, and a clear framework for scaling MCP adoption across large API portfolios.
From Cloudflare Tunnel to Tailscale Funnel
Many self hosted environments start with the same pattern: expose services through a public tunnel, then gradually realize that most of those services never needed to be public in the first place. This talk walks through a migration from Cloudflare Tunnel to a private first design built around a Tailscale tailnet for internal access, with Tailscale Funnel used only for the small number of services that truly need public reachability. Tailscale describes a tailnet as private space that is not publicly accessible, while Funnel is designed to expose selected services from that private network to the internet.
I will compare the two architectures, explain what changed operationally, and show how to decide which services should remain private and which should be exposed publicly. Cloudflare Tunnel routes traffic through Cloudflare’s network using outbound cloudflared connections, while Tailscale Funnel exposes a local service through Tailscale’s Funnel relay servers and TCP proxy. The tradeoff is not simply “one is better,” but whether your environment benefits more from a public edge first model or a private network first model.
The session covers migration planning, service classification, internal naming with MagicDNS, policy design with grants, and the practical limits of the approach. I will also share what worked, what broke, and where the architecture became simpler or more secure after moving most services off the public internet. Tailscale supports MagicDNS, automatic NAT traversal, and both direct and relayed connectivity inside the tailnet, but public Funnel traffic has its own routing model and should be evaluated on its own terms.
Microservice Cognitive Index for Deploy Diagnosis and Change Impact
Modern cloud native systems often span hundreds of microservices, thousands of endpoints, and fragmented telemetry across logs, traces, metrics, deployments, and service catalogs. Even with strong observability, engineers still struggle to answer two high impact questions fast: why did this deployment fail, and if I change this service or API, what breaks.
This industry session presents an AI powered Microservice Cognitive Index, an intelligence layer on top of existing observability. It builds a canonical evidence graph by ingesting telemetry, deriving runtime topology from traces, clustering incident signatures from normalized logs, correlating regressions with deployments, and propagating change impact through dependency and contract signals. It combines graph based reasoning with machine learning and large language models to summarize evidence, rank likely causes, and explain blast radius with confidence. Unlike chat with logs approaches, it enforces tool grounded answers with evidence references, confidence scoring, and refusal policies when data is incomplete or confounded, making results auditable and safer for operations.
WeAreDevelopers World Congress 2026 - North America Sessionize Event Upcoming
Explore DDD 2026 Sessionize Event Upcoming
TailscaleUp 2026 Sessionize Event Upcoming
PlatformCon 2026 Sessionize Event Upcoming
DeveloperWeek New York 2026 Sessionize Event Upcoming
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