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SwiggyRaj GohilExplainer
How We Made All Swiggy Apps Operations AI-Native
Summary
Swiggy’s mobile engineering teams faced operational overload as releases, production signals, and reviews required switching among Jira, Confluence, Bitrise, Play Console, Crashlytics, New Relic, Google Sheets, ImageKit, and Slack. The response was an operating layer built around Slack-native agent workflows, reusable AI skills, MCPs, and scheduled jobs connected to release, observability, QA, cost, and internal mobile systems. It can generate readiness updates, group Jira work, identify missing owners and blockers, summarize vitals, analyze alerts, follow up TRM actions, and recommend RAMP, HOLD, or ROLLBACK for staged rollouts by correlating Crashlytics, Play Vitals, and adoption. Humans still approve rollout actions, while recurring channel reports make observability and accountability more consistent and reduce clerical integration work. The stated model lets agents handle mechanical load while engineers retain judgement, ownership, product quality, architecture, and execution.
Context
Engineering across Swiggy Apps involved many systems, release trains, telemetry sources, and review rhythms. Engineers and release managers spent substantial time gathering metrics, checking tickets, formatting updates, mapping alerts to owners, and chasing TRM actions, even though the necessary data already existed across dashboards and tools.
Approach / What changed
The teams built a Slack-centered operating layer using AI agent skills, MCPs, scheduled jobs, and domain integrations for Jira, Confluence, New Relic, Firebase, Play Vitals, Bitrise, Google Sheets, Appium, Playwright, Databricks, ImageKit, and internal mobile tooling. Automations cover release readiness, rollout analysis, daily observability, alert triage, QA workflows, cost tracking, and follow-up tasks, while human approval remains required for rollout decisions.
Takeaways
Staged rollout analysis correlates Crashlytics, Play Vitals, and adoption to recommend RAMP, HOLD, or ROLLBACK; the agent does not click the rollout button, leaving final approval to a human.
Recurring AI-driven observability crons post metrics such as crash rate, ANR rate, startup time, API RTT, New Relic ingestion, ImageKit bandwidth, and scale-up health into team channels.
New Relic alert workflows can connect signals to specific widgets, banner IDs, assets, configuration owners, collection IDs, layouts, and asset URLs for investigation-ready triage.
Source and freshness
- Source
- medium.com
- Published
- Added to Yomu
Summaries are generated from the original by a language model. Check the original before relying on it.
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