AI Spend and Consumption Management in 1Password SaaS Manager gives IT and Finance a real-time view of AI consumption and spend across vendors, helping teams understand what's driving costs before they become budget surprises. See it in action and learn more: #AI# #SaaSManagement# #FinOps# #ITLeadership# #tokenmaxxing# #AIROI# #AIGovernance# #AgenticWorkflows# #1Password#

AI is priced by consumption. Every prompt and model call compounds the bill. This is the problem we are solving with AI Spend and Consumption Management in 1Password SaaS Manager. As our CFO Greg Henry puts it, “Organizations need better data and alerts to understand where model usage is creating value to keep budgets well managed as AI adoption grows.” Read more to get ahead of AI spend before procurement gets the call: #AI# #SaaSManagement# #FinOps# #ITLeadership# #tokenmaxxing# #AIROI# #AIGovernance# #AgenticWorkflows#

Build agentic workflows completely offline. The Antigravity SDK now supports local execution with Gemma 4 and LiteRT. Run agents entirely on your local machine with: 💵 Zero token costs 🔒 Total data privacy 🔌 Offline reliability Bonus feature: Support for OpenAI-compatible endpoints. Use Ollama, llama.cpp, vLLM and more to serve Gemma 💪 Get started: pip install google-antigravity litert-lm Read the details:

Grok is absolutely crushing agentic SaaS workflows Grok 4.6 ranked #2#, On the updated AutomationBench-AA outperforming Claude Fable 5.1, GPT-5.6, Kimi K3 and more This benchmark tests real agentic workflows across SaaS tools, not just static Q&A Grok is getting ridiculously strong at actually doing the work

The most important agentic workflows are the ones where you collaborate with the agent, verify its results, and encode them as a skill or verifier for reuse. This works for things like writing, researching, coding, and other tasks. Domain expertise, in the form of human verification, is a crazy moat. Don't let anyone or any company tell you otherwise. And you can build incredibly valuable services and products with that. Protect it and don't give it away for free. Keep upskilling yourself and leverage the AI agents along the way. But don't forget how crucial it is to develop and hone taste, judge quality, and critical thinking. In simpler terms, don't offload understanding to your AI systems. Offload all the rest (boring and repetitive tasks). Careful automation goes a very long way. A lot of the narrative today is around eliminating the need/replacing domain expertise. But if you work on hard problems, which you should be doing with AI, you realize quickly how primitive AI models are in their "intelligence", capabilities, and adapting to extremely hard and important domains. There is a reason why math problems continue to be solved only by folks with deep math backgrounds. Learn from that. I am not saying the models won't get better. They will get better, but so will humans (it's important to be an optimist in human intelligence for this to be crystal clear), and so the agent-to-human relationship and interactions are the real moat and where all the value and discovery will come from.

This is one of the most effective ways to improve your agentic workflows. If you are building computer-use agents, this one is worth your time. Task Model Induction takes a raw recording of someone working, just screenshots and mouse and keyboard events, and turns it into a symbolic model of how the work was actually done. The hard part is that real recordings are multi-threaded. People switch between goals mid-task. TMI first discovers the latent tasks inside an unconstrained trace and separates them, hitting 0.974 agreement against ground-truth groupings. Each recovered task then gets two things. A hierarchical objective model of how the goal decomposes, and a procedure model of the control flow that organized execution. It reconstructs 74.9% of observed execution steps, and skills derived from these task models lift held-out task accuracy by 30.0% over the strongest workflow induction baseline. Passive traces are sitting on most work laptops already. This work just shows how to mine them into auditable, reusable skills. Paper: Track more trending AI papers in our academy:

There’s no one-size-fits-all SKU for agentic AI. “The customer environments are diverse, the workloads are diverse, the constraints are diverse.” AMD’s @MadhuR_PDX explains why end-to-end agentic workflows will continue to require different compute configurations: