Open Source Summit India 2026

AI is changing software delivery—but most teams don’t need more “chat with logs.” They need an engineering platform that turns delivery signals into reliable, auditable outcomes. In this session, we’ll walk through practical AI patterns for CI/CD and internal platforms using open-source building blocks: pipeline failure triage copilots grounded in CI logs and runbooks, flaky test detection, test prioritization/selection to shorten feedback loops, and change-risk scoring to drive progressive delivery.
You’ll get a reference architecture that treats the platform as a product: a unified “delivery intelligence” layer that connects Git, CI, CD, and observability (OpenTelemetry) into a closed loop—recommend → assist → automate—only within strict guardrails. We’ll cover what makes this production-safe and open-source friendly: evidence-first outputs (no claim without links), policy-as-code boundaries, secrets redaction, prompt-injection defenses, and evaluation harnesses so AI behavior is testable like any other dependency.
Attendees leave with a starter backlog, rollout plan, and metrics to prove impact (cycle time, false-red rate, MTTR, and change failure rate).