If I had to help 4 SaaS teams build AI agents all over again, here’s what I’d change

Pain Points:

• Outdated or messy APIs cause AI agents to fail
• Bad or outdated documentation leads to errors
• Agents retrying excessively frustrates users
• Lack of confirmation steps leads to accidental destructive actions
• Power users feel out of control without visibility into agent actions
• Infrequent large updates delay improvements
• Edge cases (like null dates) cause unexpected failures

Problems to Solve:

• Making SaaS products more human and effortless to use
• Reducing errors in AI agent operations
• Preventing accidental destructive actions
• Building user trust in AI agents
• Improving responsiveness to user issues
• Handling edge cases gracefully

Potential Solutions:

• Clean up and standardize API function names (e.g., updateInvoice, cancelSubscription)
• Maintain proper OpenAPI specs for documentation
• Limit retries to two attempts before offering an alternate path
• Implement confirmation steps and undo functionality for critical actions
• Add a 'shadow UI' to show agent actions in real-time
• Make small weekly tweaks based on user feedback instead of large quarterly updates
• Add validation layers to handle edge cases (e.g., returning 'NOT_READY' instead of retrying)
• Log events for auditing and rollback capabilities
• Expose event trails to power users (even as raw JSON)
• Teach agents to admit uncertainty early ('I don't know')
• Instrument everything for monitoring and logging
• Use tools like PostHog for event logging and pattern detection
• Use synthetic testing of top user flows to catch regressions