Building an AI agent is fun. Fixing its production latency when it's juggling live data, RAG, and text-to-speech? Not so fun.
In the latest episode of the AI Agent Clinic, we sat down with developer Sami Maghnaoui to debug PlaybackIQ, a football / soccer agent he built to provide pre and post match analysis with text to voice, and minute-by-minute match insights with interactive UI.
The app was awesome, but under heavy "match day" data loads, the wait times were killing the UX.
Here’s how we fixed it:
The Bottleneck: We implemented OpenTelemetry on the Agent Platform to trace exactly where the LLM calls and data retrieval were hanging up.
The Scale: We shifted the deployment to Cloud Run to properly handle concurrent traffic.
The Result: We managed to slash the agent's latency by 80%.
If you're dealing with sluggish LLM response times in your own apps and want to see what a production-grade fix looks like, we recorded the whole teardown and rebuild.
🎥 Watch the teardown here:
(Let me know in the comments what your go-to stack is for tracing LLM latency!)
Top comments (2)
What observability stack are you currently using to trace your AI agents? Let us know below/
Great breakdown! The 80% latency reduction is especially impressive. It’s easy to focus on building the agent and forget that production performance can become a completely different challenge once real-time data and higher traffic are involved.
Using OpenTelemetry to identify the actual bottlenecks before scaling sounds like the right approach. Really useful example for anyone building real-time AI applications.