A few days ago, I wrote about building an AI agent with Claude Code that automatically posts my daily ClickUp standup updates to Slack. The workflow was surprisingly simple. Every morning at 10 AM, the agent fetches my tasks from ClickUp, generates a standup summary using AI, and posts it directly to Slack. No manual work. No copy-pasting updates. Just automation doing its job. And honestly, it worked great. But while building it, I started thinking beyond the demo. What happens when this workflow runs every day for the next six months? What happens when the ClickUp token expires? What if Slack rate limits the request? What if the network drops halfway through execution? What if the AI generates an invalid payload? What happens when one API succeeds but another fails? That’s when I realized something important. **The hardest part of AI agents isn’t the AI. It’s the APIs.** The Demo Worked. Production Is a Different Story. Most AI agent demos follow a familiar pattern. You connect an LLM to a few tools, run a workflow, watch everything work perfectly, take a screenshot, and publish a post about it. The problem is that production systems don’t operate on happy paths. Sooner or later, something breaks. Imagine an agent that reads support tickets from Zendesk, creates Jira issues, and posts updates to Slack. On paper, it looks straightforward. Until the first failure happens. Maybe the API token expired overnight. Maybe Slack returns a rate-limit error. Maybe Jira introduces a new required…
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