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Heroku is introducing significant updates to Managed Inference and Agents. These changes focus on reducing developer friction, expanding model catalogue, and streamlining deployment workflows.

Large language models are good at writing code. Data from Anthropic shows that allowing Claude to execute scripts, rather than relying on sequential tool calls, reduces token consumption by an average of 37%, with some use cases seeing reductions as high as 98%.

Untrusted code needs a secure and isolated place to execute. We solved this with code execution sandboxes (powered by one-off dynos), launched alongside Heroku Managed Inference and Agents in May 2025.

If you’ve ever debugged a production incident, you know the drill: IDE on one screen, Splunk on another, Sentry open in a third tab, frantically copying error messages between windows while your PagerDuty keeps buzzing.

You ask “What errors spiked in the last hour?” but instead of an answer, you have to context-switch, recall complex query syntax, and mentally correlate log timestamps with your code. By the time you find the relevant log, you’ve lost your flow. Meanwhile the incident clock keeps ticking away.

The workflow below fixes that broken loop. We’ll show you how to use the Model Context Protocol (MCP) and Heroku Managed Inference and Agents to pipe those observability queries directly into your IDE, turning manual hunting into instant answers.

Heroku is introducing significant updates to Managed Inference and Agents. These changes focus on reducing developer friction, expanding model catalogue, and streamlining deployment workflows.

Large language models are good at writing code. Data from Anthropic shows that allowing Claude to execute scripts, rather than relying on sequential tool calls, reduces token consumption by an average of 37%, with some use cases seeing reductions as high as 98%.

Untrusted code needs a secure and isolated place to execute. We solved this with code execution sandboxes (powered by one-off dynos), launched alongside Heroku Managed Inference and Agents in May 2025.

If you’ve ever debugged a production incident, you know the drill: IDE on one screen, Splunk on another, Sentry open in a third tab, frantically copying error messages between windows while your PagerDuty keeps buzzing.

You ask “What errors spiked in the last hour?” but instead of an answer, you have to context-switch, recall complex query syntax, and mentally correlate log timestamps with your code. By the time you find the relevant log, you’ve lost your flow. Meanwhile the incident clock keeps ticking away.

The workflow below fixes that broken loop. We’ll show you how to use the Model Context Protocol (MCP) and Heroku Managed Inference and Agents to pipe those observability queries directly into your IDE, turning manual hunting into instant answers.

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