AI Essentials for CX Teams

Getting an AI agent to work once is easy. The real challenge is ensuring it behaves reliably across edge cases, channels, and teams every single day. 

Reliability starts with prompt design. Fragile prompts may break when wording changes, while robust prompts generalize across variations and maintain consistent behavior. To make this repeatable, AI systems need three core capabilities: 

1. Versioning to track prompt changes.
2. Observability to monitor performance.
3. A/B testing to compare outcomes. 

Together, these practices turn AI from a black box into a transparent, configurable system.

Edge cases are another critical design point. A high-stakes message like “My payment went through, but nothing shipped, and support just hung up” demands sensitivity. Poor handling risks trust and churn. That’s why fallback strategies matter: prompting the AI to escalate gracefully or respond with a neutral holding message preserves credibility. With Decagon’s Agent Operating Procedures (AOPs), these behaviors can be built directly into workflows.

As systems scale, so does the complexity. A library of reusable AOPs acts like prompt recipes for common scenarios, ensuring consistency across use cases. Debugging tools help pinpoint when behavior changes, whether from new data, policies, or prompt updates. And because AI touches all teams, collaboration across CX, ops, and engineering teams is essential.

Finally, reliable support means adapting across channels. Chat and SMS are suitable for concise updates, while voice requires empathy and pacing. Well-designed workflows orchestrate these differences seamlessly, meeting customers where they are.

In short, designing reliable AI workflows means planning for consistency, edge cases, and channel nuance. Done well, these systems turn AI into a trusted extension of your support operation — scalable, resilient, and always aligned with customer expectations.