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How One Engineering Team is Scaling AI Agents Using AI Observability
New Relic's SRE agent team dogfooded their AI Monitoring product to replace manual telemetry, automating token tracking and model comparisons between GPT-4 and Claude.
What: New Relic's SRE agent team adopted their own AI Monitoring (AIM) product, replacing manual telemetry code with automated tracking for token usage, response times, and error rates. The team uses AIM to test model changes (GPT-4 to Claude) in staging, monitor costs via token usage metrics, and debug agent interactions. IDC forecasts over 1 billion enterprise AI agents by 2029.
Why it matters: This shows AI agents transitioning from experimental projects to production infrastructure requiring the same observability tooling that became standard for web services—automated metrics replacing custom instrumentation.
Decoder
- SRE agent: Software agent that performs Site Reliability Engineering tasks (monitoring, incident response, system optimization) using AI/LLM capabilities rather than human operators
- Token usage: The number of input/output tokens consumed by LLM API calls—the primary cost metric for LLM-based systems
Original article
New Relic improved AI agent scalability by adopting AIM for integrated observability, replacing manual telemetry with automated metrics to enhance debugging, optimize costs, and accelerate development of production agents.
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