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title: Blueberry: Force Multiplier For The On-Call Engineer
description: Instacart built Blueberry, a Slack-native AI reasoning harness for on-call engineers that auto-triages alerts and supports multi-turn investigations. The...
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> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
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# Blueberry: Force Multiplier For The On-Call Engineer

**[Instacart](https://daily.dev/sources/instacart)** · 15 min read · 0 upvotes · 0 comments

## Summary

Instacart built Blueberry, a Slack-native AI reasoning harness for on-call engineers that auto-triages alerts and supports multi-turn investigations. The system uses a Postgres-backed durable job queue, three composable Model Context Protocol surfaces (in-process, shared, and team-hosted), and parallel sub-agents to gather historical and live evidence simultaneously. In April 2026, it ran ~25k diagnostic passes across 270+ Slack channels, achieving ~3-minute average time to first insight and ~3-minute theory-testing turnaround with a 99.9% success rate. Key design lessons: harness quality (grounding, specialization, continuity, learning loops) matters more than model quality alone. Teams can contribute custom profiles, runbooks, and MCP endpoints to make tribal knowledge reusable infrastructure.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://tech.instacart.com/blueberry-force-multiplier-for-the-on-call-engineer-98c446dfcc12>

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---

Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#mcp](https://daily.dev/tags/mcp), [#slack](https://daily.dev/tags/slack)

[View this post on daily.dev](https://daily.dev/posts/blueberry-force-multiplier-for-the-on-call-engineer-jwfisqj6u)

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