When you build agentic AI solutions, you face unique operational challenges. Agents make unpredictable decisions, costs spiral unexpectedly, and debugging non-deterministic failures seems impossible. Agentic AI applications don't just execute predetermined workflows. They reason, adapt, and make autonomous decisions, and DevOps practices need to be adapted. That's where AgentOps comes in, the operational discipline for deploying, managing, and continuously improving AI agents in production.

This post explores how combining Amazon FSx for Lustre, NVIDIA GPUDirect Storage, and sharded parallel loading reduces cold-start time-to-first-token for large language models from minutes to seconds, and how TurboQuant KV cache significantly increases context window size.

Rippling uses LangChain Deep Agents and LangSmith to run cross-domain AI across HR, IT, finance, payroll, and global operations.

This post walks through a practical implementation using KDB-X MCP server integration with Amazon Quick, demonstrating how traders and analysts can ask questions using conversational language and receive actionable insights from datasets. The integration pattern applies to various domains, from financial market analysis to IoT sensor monitoring to DevOps performance dashboards.

Learn how Googlers used AI to produce Google I/O 2026, from the jellyfish pre-show to the “TPU Training Day” film, see how Gemini helped make I/O happen this year.

JetBrains releases Mellum2, a 12B-parameter MoE model activating only 2.5B parameters per token, optimized for low-latency text-and-code workloads. It excels in routing, RAG, sub-agents, and private deployments, achieving 2x faster inference than comparable models. Released under Apache 2.0.

IBM Research introduces 'agent logic'—software primitives like knowledge graphs and program analysis that guide LLMs to focus on enterprise workflow cores, dramatically reducing token consumption and improving performance. Validated across legacy code understanding, test generation, incident response, and compliance automation, agent logic enables AI agents to outperform pure LLM approaches with up to 30x token reduction in app understanding and 20-45% coverage improvement in test generation. Case studies include a configurable generalist agent (CUGA) in healthcare and condition-based maintenance for IBM Global Real Estate.