Building effective agent automations
Anthropic has released a reference implementation and configuration guide for building scheduled agent automations using Claude Managed Agents. The guide details how to read custom sources like Slack and GitHub, manage credentials securely via vaults, handle source failures, and maintain consistent state across runs.
The Core Development
As AI accelerates our work, it's getting harder to keep up. At Anthropic, simple agent automations are frequently used to help. They often run on a schedule, gather context in the background, and proactively tell us what we need to know. But it’s difficult to build effective agent automations: they can lose access to a source without anyone noticing or fail to follow our preferences.
Using Claude Managed Agents (beta), we built a reference implementation that reads custom sources (e.g., Slack and GitHub repos) on a schedule, tracks what changed since the last run, and posts what you need to know (e.g., to Slack). In this article, we walk through each step, share a reference implementation, and provide a command to run in Claude Code that configures the agent for you.
The reference implementation is here . For an interactive walkthrough, run the command below in Claude Code. The claude-api skill can help set up the agent following the guidance in this article:
Strategic Perspective & Ecosystem Impact
For this reference implementation, you need a Slack app ( create it from the manifest ) and a GitHub token . The provided files (shown below) are configuration for Claude API resources, including the agent, its environment, memory stores, vault, and deployment.
ant apply , a command in the ant CLI, reads these files, creates the resources in your Claude API workspace (where the platform stores and runs them), and records the IDs in claude-lock.json .
We'll use this command in the sections below. Once configured, the automation runs on a schedule on Anthropic's infrastructure, so nothing has to stay running on your machine.
Why it matters
This implementation provides practical engineering patterns for deploying reliable, autonomous background agents in production environments. Addressing common failure modes like broken source connections and state drift is crucial for scaling enterprise AI workflows.