Satya’s point about AI ecosystems is directionally right, but for enterprises the missing piece is the harness. The real challenge isn’t just connecting a model to a workflow. It’s giving that workflow safe access to infrastructure, policy, telemetry, and enforcement so it can actually run in production and improve over time. That’s how I think about @Cisco Cloud Control: a secure harness for infrastructure that helps enterprises build loops safely. Not just agents that can act, but governed systems that can observe, decide, execute, and learn without losing control.

Ok - this is super cool! @Ghatikesh has built something that I use everyday! It’s the “2nd brain” setup that doesn’t require a Ph.D. to setup! If you are like me and like to organize your thoughts, your interactions and optimize your mental framing on topics, and synthesize them regularly - you will love Keel.

The PocketOS incident is a reminder: prompts are not permissions. “Don’t delete production data” is not a control plane. Agents need hard boundaries: - scoped credentials, - confirmations, - blast-radius limits, - audit trails, and - reversible workflows. Agent UX is the safety layer.

Enterprise agents are not just a model problem. They are a UX problem. Not chat UX. Delegation UX. Supervision UX. Approval UX. Interruption UX. The hard part is not getting an agent to do something impressive once. It is making the work legible enough that a company can trust it repeatedly. Enterprise adoption will depend on whether agents feel controllable, reviewable, and safe inside real workflows.

Most ‘AI agent security’ debates are still happening at the sandbox layer. That’s not the real production problem. The real problem is giving agents access to real data real systems without giving them unlimited authority. The winning stack will look like: • explicit permissions • action-level policy • audit trails • rollback • human escalation Smarter models help.

The is is super cool.

We’re moving from “AI that can do things” to “AI that can be trusted to do things.” The next wave of agent products won’t be ones that are powered by the single smartest model. It’ll be the stack that combines: - specialized models - trusted runtimes - scoped identity - action-level permissions - audit by default

The contrarian take is that AI won’t just automate labor. It will massively increase the value of **coordination infrastructure**. When agents can act, the bottleneck shifts from generation to governance. Everyone talks about AI replacing workers. The more interesting effect may be AI increasing demand for management systems!! Details on who approved it, what it touched, how it failed, when a human steps in, etc. Better agents don’t remove the need for oversight. They make good oversight a moat.

Everyone talks about models. Fewer people talk about the worlds those models learn inside. But environments, evals, and harnesses may end up being where a lot of the real leverage sits. The frontier isn't just intelligence. It's the systems that shape it.

the most underrated hire right now is a great product person. when i say product person i'm def not talking about a product manager. perhaps i think there has to be somewhat of a new role. i don't have a good name for it yet but maybe something like "product thinker".. someone with an intuitive grasp of the product as it exists, where it's soft, where it sings, & how to iterate it toward something even sharper. in some sense, this person has to cohesively hold in their head where this product should be 2 years from now & work backwards from that. i say this cuz when building was hard, engineering was the bottleneck & the status hierarchy often reflected that. building is no longer hard. which means the variance in outcomes has shifted almost entirely to judgment on what to build, how to sequence it, & how to talk about it. & the story matters as much as the thing. internally, it organizes the team around a shared model of why. externally, it shapes the interpretive frame users bring to their first experience. you can't retrofit narrative onto a product & expect it to land, it has to be load bearing from the start. the rarest version of this person sits at the intersection of culture & deep technology. someone genuinely bilingual. they know what's technically possible & they know which cultural currents are real vs. ephemeral. that combo is what separates products that feel inevitable from products that feel assembled. before ppl clap back with this person has always been valuable, i know.. i am just saying now they might be the most *important* person in the room. their value compounds like never before.

Such an awesome video. ❤️

❤️ Please let us know how we can make the skills scanner even better! Teams @Cisco AI are hard at work ensuring we help the community secure the use of AI.

With all the craze on @openclaw and mac-minis, I found this benchmark really interesting. This is from @digitalix on how 8-bit quantized model running on vLLM and @nvidia's DGX spark, scales to almost 1158 tps. This gets even better when you go down to FP4!

This. There needs to be a good design pattern to review prompts alongside code generated in PRs.

I almost believe it’s time to reinvent resumes. Every job applicant needs to submit a vibe coded project. This is not just for engineering but for product, design, sales, marketing, and mandatory for any leadership positions.

#AI agents are moving fast. Trust needs to move faster. Securing MCP servers, reducing risk in AI-generated code, and understanding what agents are doing are key as agents take on more work. Learn more from Cisco SVP, AI Software and Platform, @djsampath and @davidbombal. ➡️ cs.co/6012CKOu2