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New Relic's Head of AI and ML Innovation, Camden Swita discusses their four-cornered AI strategy and envisions a future of "agentic orchestration" with specialized agents.
Topics Include:
- Introduction of Camden Swita, Head of AI at New Relic.
- New Relic invented the observability space for monitoring applications.
- Started with Java workloads monitoring and APM.
- Evolved into full-stack observability with infrastructure and browser monitoring.
- Uses advanced query language (NRQL) with time series database.
- AI strategy focuses on AI ops for automation.
- First cornerstone: Intelligent detection capabilities with machine learning.
- Second cornerstone: Incident response with generative AI assistance.
- Third cornerstone: Problem management with root cause analysis.
- Fourth cornerstone: Knowledge management to improve future detection.
- Initially overwhelmed by "ocean of possibilities" with LLMs.
- Needed narrow scope and guardrails for measurable progress.
- Natural language to NRQL translation proved immensely complex.
- Selecting from thousands of possible events caused accuracy issues.
- Shifted from "one tool" approach to many specialized tools.
- Created routing layer to select right tool for each job.
- Evaluation of NRQL is challenging even when syntactically correct.
- Implemented multi-stage validation with user confirmation step.
- AWS partnership involves fine-tuning models for NRQL translation.
- Using Bedrock to select appropriate models for different tasks.
- Initially advised prototyping on biggest, best available models.
- Now recommends considering specialized, targeted models from start.
- Agent development platforms have improved significantly since beginning.
- Future focus: "Agentic orchestration" with specialized agents.
- Envisions agents communicating through APIs without human prompts.
- Integration with AWS tools like Amazon Q.
- Industry possibly plateauing in large language model improvements.
- Increasing focus on inference-time compute in newer models.
- Context and quality prompts remain crucial despite model advances.
- Potential pros and cons to inference-time compute approach.
Participants:
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