We've developed an AI agent called CoSupport to help manage our support queue and reduce those pesky late-night alerts about system failures. This Go service analyzes our Zendesk data, mimics our response tone, and accurately provides answers roughly 99% of the time. We store prompts in S3, utilize GitHub Actions with Terraform for nightly updates, and maintain a latency around 200 ms—it's impressive to see tickets resolved instantly! We plan to launch in mid-August, but I want to identify potential issues now. If you were in my position, what precautions or integrations would you implement before going live with an AI agent? Would you suggest shadow mode, strict hand-off rules, or something else entirely? I'm open to sharing data and insights on how we manage errors too. Let's hear your thoughts!
3 Answers
Shadow mode is definitely a smart approach for the initial launch. It allows you to test the AI's real-world performance without impacting user experience. By monitoring the interactions, you can gather valuable feedback and make necessary adjustments. Also, detailed logging of these interactions can help you learn and improve based on actual user behavior, helping to avoid any surprises later on.
You might want to think about hiring a consultant for a thorough review before you launch. While I can share what worked for us, having someone with experience could give you tailored advice. It’s essential to assess the infrastructure and patterns you have in place. Deploying AI is somewhat similar to other app releases, just with those unique AI challenges. Best of luck!
When it comes to releasing AI agents, it’s crucial to prioritize reliability and availability to meet your SLAs. Make sure you have a plan for handling latency and downtime, and consider using deployment strategies like blue/green or canary releases to minimize risks with new updates. Since you've mentioned being aware of model hallucinations, ensuring secure access is also key—your AI shouldn’t have permissions beyond what it needs for its job.
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