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AI Agents in Enterprise Production Environments
AI agents in enterprise production environments present unique challenges. To function effectively, they require carefully designed architecture patterns and robust infrastructure components.
As AI engineers, we must define what a successful deployment of a multi-agent platform looks like.
Defining Success with Service Level Objectives (SLOs)
Service Level Objectives (SLOs) are essential in measuring service performance and reliability. They set the acceptable ratio of “successful” to “total” measurements, impacting user journeys. SLOs help us determine the expected service levels from AI agents and the workflows they support.
Key SLOs for AI Agents:
- Availability: This measures the percentage of requests that receive a successful response (e.g., HTTP 200 status code). Traditional systems relied on server uptime and ping success as indicators. However, with microservices, the focus shifts to the ratio of successful versus unsuccessful responses as a more accurate measure of availability. Related metrics include Latency and Throughput.
- Accuracy: This focuses on the correctness of…
