Operating LLM-Centric AI Systems
As generative AI systems evolve from simple prompts to complex applications, LLMOps and AgentOps emerge as key disciplines for ensuring production-readiness, reliability, and observability.
This includes systems built on LLMs with tools, memory, retrieval (RAG), and multi-step reasoning – commonly referred to as agents.
Why it matters
The transition from prototype to production-ready application with LLM is not trivial:
- Prompt and workflow versioning
- Orchestration of tools and memory
- Monitoring quality, cost, and drift
- Guardrails and safety enforcement
LLMOps & AgentOps define the patterns and toolchains to manage this complexity.
Conclusion and Details
LLMOps & AgentOps provide the operational backbone for scaling generative AI. Teams adopting LLMs should treat operational workflows as first-class citizens – just like code.
A rapidly growing ecosystem supports these workflows and patterns.
More details can be found in the AOE AI Radar.