---
title: "Building AI-Powered Observability with Heroku Managed Inference and Agents"
description: "Production incident debugging often requires switching among an IDE, Splunk, Sentry, and PagerDuty while translating natural-language questions into platform-specific queries. The described system connects AI coding assistants to observability platforms through the Model Context Protocol (MCP), exposing typed tools for Splunk searches, Honeycomb queries, and Sentry event lookups. Heroku Managed Inference and Agents provides an MCP gateway for SSE transport, allowing servers to run as STDIO processes and be deployed with a Heroku app, an inference add-on, credentials, and an inference token configured in tools such as Cursor or Claude. The post reports ranked production error results, processing more than 175,000 events in minutes for a suspicious-account review, and using natural-language questions to reduce syntax barriers and keep telemetry in the IDE."
---

# Building AI-Powered Observability with Heroku Managed Inference and Agents

[Heroku](https://yomu.fyi/company/heroku) · Karunasri (Karuna) Garigipati · Feb 13, 2026

**Type:** Tutorial

## Summary

Production incident debugging often requires switching among an IDE, Splunk, Sentry, and PagerDuty while translating natural-language questions into platform-specific queries. The described system connects AI coding assistants to observability platforms through the Model Context Protocol (MCP), exposing typed tools for Splunk searches, Honeycomb queries, and Sentry event lookups. Heroku Managed Inference and Agents provides an MCP gateway for SSE transport, allowing servers to run as STDIO processes and be deployed with a Heroku app, an inference add-on, credentials, and an inference token configured in tools such as Cursor or Claude. The post reports ranked production error results, processing more than 175,000 events in minutes for a suspicious-account review, and using natural-language questions to reduce syntax barriers and keep telemetry in the IDE.

## Context

Production incident investigation involves switching between development and observability tools, recalling platform-specific query syntax, and manually correlating telemetry with code. The post frames this as a workflow that slows debugging and can delay incident analysis.

## Approach / What changed

The implementation exposes observability capabilities as MCP tools, including typed Splunk search parameters, Honeycomb filters and breakdowns, and Sentry event lookup by URL or ID. Heroku Managed Inference and Agents supplies the MCP gateway and SSE transport; deployment uses a Heroku app, an inference add-on, platform credentials, and an inference token configured in an AI client.

## Takeaways

- MCP tools provide a consistent interface for Splunk searches, Honeycomb queries, and Sentry event retrieval, with typed parameters supplied to the AI assistant.
- Heroku Managed Inference and Agents lets MCP servers run as simple STDIO processes while its gateway handles SSE transport, authentication, and hosting.
- The post reports processing more than 175,000 events in minutes for a suspicious-account review and returning ranked production error types through natural-language questions.

**Tags:** [Heroku](https://yomu.fyi/topic/heroku), [MCP](https://yomu.fyi/topic/mcp), [Observability](https://yomu.fyi/topic/observability)

- Source: [Heroku](https://www.heroku.com/blog/building-ai-powered-observability-with-managed-inference-and-agents)
- Source URL: https://www.heroku.com/blog/building-ai-powered-observability-with-managed-inference-and-agents
- Ingested by Yomu: 2026-09-10T04:05:20.311Z

[Read original post](https://www.heroku.com/blog/building-ai-powered-observability-with-managed-inference-and-agents)