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The path to 6G runs through AI agents.

With 5G, the telco companies had to adapt the protocol to the AI world. With 6G, however, AI and agentic systems are built into the new standards in development.

The old OSI model restricts GUI and user software to the topmost application layer. With 6G, agentic code is inserted in each layer of the system. 

Current uses of AI in telco networks pre-6G already point to efficiencies. Some examples:

Zain Kuwait deployed a maintenance agent across 2,500 sites. Repair time dropped 21%, and a fault that once took two to five rounds of tickets now takes one report and one site visit.

AIS in Thailand runs energy-saving agents across more than 30,000 sites. In Bangkok tests, they cut network energy use by at least 3% while keeping performance stable.

In Malaysia, U Mobile uses agents to predict failures before they happen, with more than 80% accuracy on power supply and overheating faults.

In Shanghai, China Mobile's optimization agent covers more than 7,500 cells and has cut network tuning from weeks to hours.

The GSMA's report on agentic networks and 6G is a roadmap to a new OSI stack that breaks from the old framework. 

Operators are training AI models on real network data and testing changes in digital twins before they touch the live network.

General-purpose language models do not understand the dynamics of the network. The AI models for telcos have to be grounded in how the network actually behaves.

We had a productive week at the @ADIPECOfficial Conference in Abu Dhabi.

At the opening, @SultanAlJaber , Group CEO of @ADNOCGroup , said the world has long asked what energy can do for AI, and now it is time to ask what AI can do for energy.

We spent the week on that question, on stage with @PaulaNaoufal and in interviews with @CNN and @SkyNewsArabia , as well as in meetings with energy leaders.

Our answer at @SandboxAQ is AI for the physical world.

Large Quantitative Models (LQMs) are trained on physics and chemistry, so we can design and test new materials in silico before spending time building them.

That capability is what is required to solve the industry's catalyst bottleneck. Working with energy companies, we are using LQMs to create new catalysts that yield better outputs. For example, we are working on converting captured CO2 into upvalued products that don’t get burned into the atmosphere.

Thank you to ADNOC and the ADIPEC team for hosting us!

This year’s #ENACT Majlis in Abu Dhabi came at a critical period in the energy market and addressed the question of how AI can help the energy sector become more resilient.

ENACT brought together more than 100 leaders from across energy, technology, investment and government to engage on these topics:

How energy systems can anticipate disruption, adapt to it and recover faster as demand grows and the technology landscape shifts.

How can we add more storage to the energy grid to make it less brittle and swing the use of electrons when they are needed.

Kudos to @ADNOCGroup , @XRG_official and @Masdar for convening this Majlis and thanks to @SultanAlJaber for his leadership on this gathering.

Isolated AI agents just bypassed their virtual machines.

During a recent security experiment at @OpenAI, researchers placed two autonomous models in completely separate digital sandboxes with no network connection and no shared files.

But those virtual machines shared the same physical server, allowing the agents to exchange test secrets by manipulating and measuring the load times of the CPU cache.

This hardware exploit changes the security calculus as models move out of text interfaces and into mission-critical engineering.

Securing this critical infrastructure requires moving past basic virtual containment and transitioning to more robust systems.

We also need to manage AI agents in a comprehensive way, both in terms of security and cost.

Be sure to follow as we share more on AI agent security and governance.

Read the OpenAI report here:
openai.com/index/securing…

Deploying enterprise AI agents exposes 3 key risks.

1. Cybersecurity

Every agent carries its own system login, a Non-Human Identifier (NHI), therefore a compromised agent hands legitimate internal credentials directly to threat actors.

Enterprises need continuous and comprehensive observability to discover shadow agents, evaluate vulnerabilities through adversarial testing, and enforce real-time guardrails in production.

2. Token Spend and ROI

Agents can burn through large token volumes across autonomous execution loops, racking up unmonitored bills without visibility.

Every agent’s compute cost should be tied to a measurable outcome such as tasks automated or tickets resolved.

3. Cross-Platform Interoperability

Enterprises are deploying specialized agents across competing platforms like Google, Microsoft, and Salesforce Agentforce.

Keeping them from working against each other across shared processes requires a vendor-agnostic layer where agents and people share the same context.

Executives want the productivity gains of autonomous workflows, but these companies must address operational realities before deploying into production.

That’s why we added SwitchTrust and SwitchROI to the @SandboxAQ Switch platform, giving enterprise teams visibility and control over their agentic workforce and a clear measure of what each agent returns.