elan President Nicolas Maduro.

If true, it would mark one of the most public uses of U.S. cyber power against
another nation in recent memory. These operations are typically highly
classified, and the U.S. is considered one of the most advanced nations in
cyberspace operations globally.

** *** ***** ******* *********** *************

The Wegman's Supermarket Chain Is Probably Using Facial Recognition

[2026.01.07] The New York City Wegman's is collecting biometric information
about customers.

** *** ***** ******* *********** *************

AI & Humans: Making the Relationship Work

[2026.01.08] Leaders of many organizations are urging their teams to adopt
agentic AI to improve efficiency, but are finding it hard to achieve any
benefit. Managers attempting to add AI agents to existing human teams may find
that bots fail to faithfully follow their instructions, return pointless or
obvious results or burn precious time and resources spinning on tasks that
older, simpler systems could have accomplished just as well.

The technical innovators getting the most out of AI are finding that the
technology can be remarkably human in its behavior. And the more groups of AI
agents are given tasks that require cooperation and collaboration, the more
those human-like dynamics emerge.

Our research suggests that, because of how directly they seem to apply to hybrid
teams of human and digital workers, the most effective leaders in the coming
years may still be those who excel at understanding the timeworn principles of
human management.

We have spent years studying the risks and opportunities for organizations
adopting AI. Our 2025 book, Rewiring Democracy, examines lessons from AI
adoption in government institutions and civil society worldwide. In it, we
identify where the technology has made the biggest impact and where it fails to
make a difference. Today, we see many of the organizations we've studied taking
another shot at AI adoption -- this time, with agentic tools. While generative
AI generates, agentic AI acts and achieves goals such as automating supply chain
processes, making data-driven investment decisions or managing complex project
workflows. The cutting edge of AI development research is starting to reveal
what works best in this new paradigm.

Understanding Agentic AI

There are four key areas where AI should reliably boast superhuman performance:
in speed, scale, scope and sophistication. Again and again, the most impactful
AI applications leverage their capabilities in one or more of these areas. Think
of content-moderation AI that can scan thousands of posts in an instant,
legislative policy tools that can scale deliberations to millions of
constituents, and protein-folding AI that can model molecular interactions with
greater sophistication than any biophysicist.

Equally, AI applications that don't leverage these core capabilities typically
fail to impress. For example, Google's AI Overviews irritate many of its users
when the overviews obscure information that could be more efficiently consumed
straight from the web results that the AI attempted to synthesize.

Agentic AI extends these core advantages of AI to new tasks and scenarios. The
most familiar AI tools are chatbots, image generators and other models that take
a single action: ask one question, get one answer. Agentic systems solve more
complex problems by using many such AI models and giving each one the capability
to use tools like retrieving information from databases and perform tasks like
sending emails or executing financial transactions.

Because agentic systems are so new and their potential configurations so vast,
we are still learning which business processes they will fit well with and which
they will not. Gartner has estimated that 40 per cent of agentic AI projects
will be cancelled within two years, largely because they are targeted where they
can't achieve meaningful business impact.

Understanding Agentic AI behavior

To understand the collective behaviors of agentic AI systems, we need to examine
the individual AIs that comprise them. When AIs make mistakes or make things up,
they can behave in ways that are truly bizarre. But when they work well, the
reasons why are sometimes surprisingly relatable.

Tools like ChatGPT drew attention by sounding human. Moreover, individual AIs
often behave like individual people, responding to incentives and organizing
their own work in much the same ways that humans do. Recall the counterintuitive
findings of many early users of ChatGPT and similar large language models (LLMs)
in 2022: They seemed to perform better when offered a cash tip, told the answer
was really important or were threatened with hypothetical punishments.

One of the most effective and enduring techniques discovered in those early days
of LLM testing was 'chain-of-thought prompting,' which instructed AIs to think
through and explain each step of their analysis -- much like a teacher forcing a
student to show their work. Individual AIs can also react to new information
similar to individual people. Researchers have found that LLMs can be effective
at simulating the opinions of individual people or demographic groups on diverse
topics, including consumer preferences and politics.

As agentic AI develops, we are finding that groups of AIs also exhibit
human-like behaviors collectively. A 2025 paper found that communities of
thousands of AI agents set to chat with each other developed familiar human
social behaviors like settling into echo chambers. Other researchers have
observed the emergence of cooperative and competitive strategies and the
development of distinct behavioral roles when setting groups of AIs to play a
game together.

The fact that groups of agentic AIs are working more like human teams doesn't
necessarily indicate that machines have inherently human-like characteristics.
It may be more nurture than nature: AIs are being designed with inspiration from
humans. The breakthrough triumph of ChatGPT was widely attributed to using human
feedback during training. Since then, AI developers have gotten better at
aligning AI models to human expectations. It stands to reason, then, that we may
find similarities between the management techniques that work for human workers
and for agentic AI.

Lessons From the Frontier

So, how best to manage hybrid teams of humans and agentic AIs? Lessons can be
gleaned from leading AI labs. In a recent research report, Anthropic shared the
practical roadmap and published lessons learned while building its Claude
Research feature, which uses teams of multiple AI agents to accomplish complex
reasoning tasks. For example, using agents to search the web for information and
calling external tools to access information from sources like emails and
documents.

Advancements in agentic AI enabling new offerings like Claude Research and
Amazon Q are causing a stir among AI practitioners because they reveal insights
from the frontlines of AI research about how to make agentic AI and the hybrid
organizations that leverage it more effective. What is striking about
Anthropic's report is how transparent it is about all t
--- FMail-lnx 2.3.2.6-B20251227
 * Origin: TCOB1 A Mail Only System (618:500/1)