llmops

The article explains the key differences between DevOps, MLOps, and LLMOps, highlighting how each addresses distinct challenges in software development, machine learning, and LLM applications, with a focus on unique monitoring and optimization in LLMOps.

The article discusses the importance of system design for AI agents, covering concepts like Agent Harness, LLMOps, and Evals, and provides a proof-of-concept implementation with plans for future parts.

This arXiv paper presents a unified LLMOps architecture for real-time, enterprise-ready LLM deployments, integrating data ingestion, continual learning, RAG, and feedback loops. It introduces components like AIPO, STAR+FAR, and SAGE to address knowledge staleness, hallucination, and latency-cost trade-offs in regulated sectors.

Schneider Electric uses LangChain's LangSmith to run over 60 production AI agents across 100+ countries, serving 160,000 employees with their AI Assistant, demonstrating enterprise-scale LLMOps.

TensorZero, an open-source LLMOps platform that raised $7.3 million in seed funding, has archived its GitHub repository. The platform provides a unified gateway, observability, evaluation, optimization, and experimentation for LLMs.

RiskKernel is a self-hosted, single Go binary that enforces hard per-run budgets (cost, loop count, wall-clock), kill switches, and human approval gates for AI agents, supporting Anthropic and OpenAI providers with no telemetry.

An open-source interactive playbook for building an Agentic DevOps pipeline, covering observability, test-driven prompt evaluations, guardrails, and cost control for multi-agent systems.