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ঢাকাসহ দেশের বিভিন্ন স্থানে ভূমিকম্প অনুভূত


রাজধানী ঢাকাসহ দেশের বিভিন্ন স্থানে ভূমিকম্প অনুভূত হয়েছে। আজ বুধবার বেলা ১১টা ৩৬ মিনিটে এই কম্পন অনুভূত হয়।

মার্কিন ভূতাত্ত্বিক জরিপ সংস্থা (ইউএসজিএস) জানায়, ভূমিকম্পের উৎপত্তিস্থল ভারত ও মিয়ানমারের সীমান্তবর্তী এলাকা, যা ঢাকা থেকে প্রায় ৪৪৯ কিলোমিটার দূরে। রিখটার স্কেলে ভূমিকম্পের মাত্রা ছিল ৫ দশমিক ৬ এবং এটি ভূপৃষ্ঠের ১২৬ কিলোমিটার গভীরে সংঘটিত হয়।

আবহাওয়া অধিদপ্তরের ভূমিকম্প পর্যবেক্ষণ ও গবেষণাকেন্দ্রের ভারপ্রাপ্ত কর্মকর্তা মো. রুবাঈয়্যাৎ কবীর বিষয়টি নিশ্চিত করে জানান, এটি মাঝারি মাত্রার ভূমিকম্প হিসেবে বিবেচিত।

সিলেটসহ দেশের বিভিন্ন এলাকায়ও ভূমিকম্প অনুভূত হয়। সিলেট আবহাওয়া অধিদপ্তরের সহকারী আবহাওয়াবিদ শাহ মো. সজিব হোসাইন জানান, এক সপ্তাহের ব্যবধানে সিলেটে আবারও ভূমিকম্প অনুভূত হলো। এর আগে ২৬ ফেব্রুয়ারি রাত ২টা ৫৫ মিনিটে সেখানে ভূমিকম্প হয়েছিল। তবে এখনো কোনো ক্ষয়ক্ষতির তথ্য পাওয়া যায়নি।

বিশেষজ্ঞদের মতে, বাংলাদেশ ও আশপাশের এলাকায় ছোট ও মাঝারি ভূমিকম্পের সংখ্যা বেড়ে যাচ্ছে। আবহাওয়া অধিদপ্তরের হিসাব অনুযায়ী, ২০২৪ সালে দেশে এবং আশপাশের বিভিন্ন এলাকায় ৫৩টি ভূমিকম্প রেকর্ড করা হয়েছে, যা গত আট বছরে সর্বোচ্চ।

ভূমিকম্প বিশেষজ্ঞরা মনে করছেন, এভাবে ছোট ও মাঝারি ভূমিকম্পের সংখ্যা বৃদ্ধি পাওয়াটা একটি বড় ভূমিকম্পের পূর্বাভাস হতে পারে। ঐতিহাসিক ভূমিকম্পের পর্যালোচনা এবং সাম্প্রতিক কম্পনের সংখ্যা বৃদ্ধির ভিত্তিতে তারা এই আশঙ্কা করছেন।

During the parade of San Patricio in London, Catherine, Princess of Gales, lives with soldiers.


The Día de San Patricio in London served as the motivation for Catherine, Princess of Wales, to participate in the parade. The wife of Prince William lived with English soldiers and even shared a beer. The princess was receiving cancer treatment, therefore she was unable to participate in the celebration of the previous year.

New Hampshire Senator Jeanne Shaheen has announced she will not run for reelection in 2026.

Senator Jeanne Shaheen speaks at NHTI Concord Community College in Concord, New Hampshire, on October 22, 2024. (Mandel Ngan/AFP/Getty Images)


 Democratic Sen. Jeanne Shaheen of New Hampshire announced Wednesday that she will not run for reelection in 2026, setting the stage for a potentially competitive race to fill her seat.

“I ran for public office to make a difference for the people of New Hampshire. That purpose has never and will never change. But today, after careful consideration, I’m announcing that I have made the difficult decision not to seek reelection to the Senate in 2026. It’s just time,” Shaheen, 78, said in a video released by her congressional office.

A U.S. senator since 2009, Shaheen won reelection in 2014 and 2020 and currently serves as the top Democrat on the Senate Foreign Relations Committee. Before joining the Senate, she made history as New Hampshire’s first female governor, serving three terms from 1997 to 2003.

Although some New Hampshire Democrats had privately urged her to run for a fourth term, Shaheen kept her decision under wraps. In her announcement, she made it clear she is not stepping away from public service, stating, “Believe me, I am not retiring.” She will serve out her term, which ends in 2027.

Her departure opens a crucial Senate seat for Democrats, attracting early interest from prominent figures in both parties. Republican former Gov. Chris Sununu has not ruled out a run, while Democratic Rep. Chris Pappas is strongly considering entering the race. Former Democratic Rep. Ann Kuster has also expressed interest if Pappas declines to run.

Former Massachusetts Sen. Scott Brown, a Republican who challenged Shaheen in 2014 and narrowly lost, is also weighing another bid.

Despite the uncertainty, Democratic Senatorial Campaign Committee spokesman David Bergstein projected confidence in retaining the seat, emphasizing that no Republican has won a Senate race in New Hampshire in over a decade.

Total Lunar Eclipse March 2025 Livestreams: How to Watch the 'Blood Moon' Online for Free

 


Watch the Total Lunar Eclipse Live: How to Catch Every Moment of the Dramatic 'Blood Moon' Online on March 13.

A total lunar eclipse of March's Worm Moon is occurring this week, and you can watch it live online for free.

On the night of March 13-14, the full moon will undergo a total lunar eclipse, visible across the night side of Earth, with North America positioned perfectly for an incredible view. If you're unable to watch the eclipse in person, don't worry—there are several online livestreams (weather permitting) so you can still enjoy the Worm Moon lunar eclipse. We've gathered some of the best options below. For the latest updates and events, you can also check out our lunar eclipse live blog.

Lunar eclipses happen when Earth lies between the sun and the moon, casting a shadow on the moon's surface. During the "totality" phase, the moon moves into Earth's umbra—the dark center of its shadow—creating the red-hued "Blood Moon." The eclipse will reach its maximum phase, with the moon completely covered by Earth's deepest shadow, at 2:59 a.m. EDT (6:59 GMT) on March 14.


Greek and Cypriot Officials Applaud U.S. Renewed Commitment to Mediterranean Stability and Economic Cooperation


The potential return of Donald Trump to the U.S. presidency is sparking optimism among Greek and Cypriot officials, who anticipate new opportunities for regional stability and economic collaboration, particularly in the Eastern Mediterranean and the Middle East.

During a panel discussion at the Delphi Forum, hosted by the Hellenic American Leadership Council in Washington, D.C., senior officials from Greece and Cyprus emphasized the significance of sustained U.S. involvement in the region. The discussion, moderated by Breitbart News Washington Bureau Chief Matthew Boyle, highlighted prospects for expanding the Abraham Accords and strengthening geopolitical partnerships.


Enhancing Regional Alliances and Peace Efforts

Greek Deputy Minister of Foreign Affairs Alexandra Papadopoulou welcomed renewed U.S. engagement in the Eastern Mediterranean, stressing the strategic importance of American leadership.


“For years, there was concern that the U.S. would step back, leading to instability,” Papadopoulou said. “It is reassuring to see renewed American interest. However, regional nations must also take responsibility and collaborate with the new U.S. administration.”

She highlighted the strong ties between the U.S., Israel, Saudi Arabia, Jordan, and Egypt, emphasizing that this diplomatic network could facilitate the expansion of the Abraham Accords—historic agreements aimed at normalizing relations between Israel and Arab nations.


Cyprus’s Deputy Minister of Migration and International Protection, Nicholas Ioannides, reaffirmed his country’s commitment to regional stability through the U.S.-backed “3+1” framework, which includes Greece, Cyprus, Israel, and the United States.

“The Abraham Accords were a major milestone of the first Trump administration, and efforts to revitalize them are a positive step,” Ioannides said. “Cyprus, as the EU’s southeastern nation, is a vital strategic partner for both the U.S. and Israel. We are fully prepared to support renewed peace efforts.”


He further emphasized Cyprus’s role as a humanitarian hub and a bridge for diplomatic efforts, adding, “We can help expand what might be called ‘Abraham Accords 2.0.’”


Economic Cooperation and the India-Middle East-Europe Corridor


Beyond diplomacy and security, officials also pointed to economic initiatives that could transform regional trade and development. Papadopoulou discussed the potential of the proposed India-Middle East-Europe Corridor (IMEC), an ambitious trade route aimed at serving as an alternative to China’s Belt and Road Initiative.


“This corridor presents a unique opportunity,” she stated. “Looking at the map, the path is clear—India, the Gulf nations, Saudi Arabia, Israel, Cyprus, Greece, and then onto Europe and the United States. Greece and Cyprus can serve as the crucial link connecting these regions.”

She emphasized Greece’s strong economic ties with Israel and India, along with its expanding partnerships with Saudi Arabia and Egypt, as key factors in making the corridor a reality. “This initiative would not only boost economic openness but also reinforce our strategic alliance with the United States.”


Julie Rayman, Managing Director of Policy and Political Affairs at the American Jewish Committee, echoed the region’s vast economic potential but pointed out the impact of the October 7, 2023, Hamas terrorist attacks on Israel’s economy.


“The original Abraham Accords were built on economic prosperity, largely driven by Israel’s economic strength,” Rayman explained. “However, the tragic events of October 7 have affected Israel’s economy. While it is recovering, this must be factored into future agreements.”


A Vision for a Unified Eastern Mediterranean


Looking ahead, Greek and Cypriot officials underscored the need to align political and economic strategies to foster regional stability. Papadopoulou described the Eastern Mediterranean not as a standalone entity but as a crucial extension of Europe and the broader Middle East.

“This is about more than just the Middle East—it’s about the entire Eastern Mediterranean, spanning from the Gulf to the Balkans and into Europe,” she asserted. “The foundations are already in place; now we must work together to turn this vision into reality.”


With strong backing for renewed American leadership, Greece and Cyprus are positioning themselves as key players in shaping a more stable and economy integrated Mediterranean region.


Former NBA player and billionaire entrepreneur Junior Bridgeman passes away.

Former NBA sixth man Junior Bridgeman, who rose from humble beginnings to become one of the most successful post-playing business figures in sports, passed away on Tuesday at the age of 71. A billionaire philanthropist and, more recently, a minority owner of the [Milwaukee Bucks], Bridgeman suffered a medical emergency during a fundraising event in Louisville, Kentucky.



According to multiple Louisville television stations, Bridgeman clutched his chest during the luncheon, expressing concerns that he was experiencing a heart attack. Emergency medical personnel were called to the scene, as reported by WLKY and WAVE.


NBA Commissioner Adam Silver mourned Bridgeman’s passing, stating:


"I am devastated to learn of Junior Bridgeman’s sudden passing. He was the ultimate entrepreneur, building on his impactful 12-year NBA career to become a highly respected and successful business leader. Junior mentored generations of NBA players and athletes, guiding them on how to thrive in the business world. For 50 years, he was a dedicated member of the NBA family—most recently as a minority owner of the Milwaukee Bucks, an investor in NBA Africa, and a player who exemplified class and dignity."

Silver extended his condolences to Bridgeman’s wife, Doris, their children—Eden, Justin, and Ryan—the Bucks organization, and his many friends and admirers in the basketball community.

Hailing from East Chicago, Indiana, Bridgeman was a standout player on the 1971 Washington High School Senators' undefeated 29-0 state championship team. He went on to become an All-American at Louisville, leading his team to the 1975 Final Four. That same year, the Los Angeles Lakers selected the 6-foot-5 wing with the No. 8 overall pick in the NBA Draft, then traded him to the Milwaukee Bucks in a blockbuster deal for franchise star Kareem Abdul-Jabbar.

Bridgeman spent 12 seasons in the NBA, playing 10 with the Milwaukee Bucks and two with the LA Clippers. Over his career, he averaged 13.6 points, 3.5 rebounds, and 2.4 assists in 25 minutes per game, earning a reputation as one of the league's top sixth men. From 1985 to 1988, he also served as president of the National Basketball Players Association.

Bridgeman earned approximately $2.95 million over his NBA career, never making more than $350,000 in a single season. However, after retiring, he built a fast-food empire that grew to over 450 restaurants nationwide at its peak. He later became a Coca-Cola bottling distributor, with operations spanning three states and extending into Canada. In addition to his business ventures, he acquired Ebony and Jet magazines and invested in NBA Africa.

In September, Bridgeman purchased a 10% stake in the Milwaukee Bucks, and by February, Forbes reported that his net worth had surpassed $1.4 billion.

Millie Bobby Brown Says She Wants Babies: ‘I Want a Big Family’

 

Photo credit Netflix 

Stranger Things star Millie Bobby Brown has already accomplished much in her brief acting career, and from the looks of it, it seems that she is ready to embrace motherhood. Having married Jake Bongiovi in May 2024, Brown confirmed in a recent interview that she is looking forward to starting a family with her husband.


Here’s what the British thespian had to say about her plans regarding having children soon.


Millie Bobby Brown comments on starting a family

During her March 10 appearance on the Smartless podcast, Millie Bobby Brown expressed her excitement about becoming a mother one day. “Since I was a baby, I told my mom, like, baby dolls. I wanted to be a mom just like the way my mom was to me,” the actress noted, as per While acknowledging the fact that she is still “really young” and has a lot to look forward to on the professional front, Brown once again emphasized the importance of having a family. “Jake knows how important it is to me. Of course, I want to focus on really establishing myself as an actor and as a producer, but I also find it’s so important to start a family for me personally,” she remarked.


Millie Bobby Brown then explained that she and Jake Bongiovi are very much interested in having an extended clan, with both celebrities hailing from families with four kids. She relayed, “And my thing was, I really want a family. I really want a big family. I’m one of four. He’s one of four. So, it is definitely in our future, but, for me, I don’t see having your own child, you know, as really any different as in adopting.”


Brown is currently occupied with promoting her upcoming science-fiction feature, The Electric State. Helmed by Anthony and Joe Russo, the action-packed movie comes out on Netflix on March 14.


The post Millie Bobby Brown Says She Wants Babies: ‘I Want a Big Family’ appeared first on ComingSoon.net - Movie Trailers, TV & Streaming News, and More.

SoFi Technologies (NasdaqGS:SOFI) 13% Drop Follows Projected EPS For 2026 editorial-

 


SoFi Technologies experienced a share price decline of approximately 13% last week, potentially influenced by recent corporate guidance for 2026 earnings. The company's projected EPS ranged from 55 to 80 cents per share, a development hitting just as broader market trends showed a downturn. Despite major stock indexes closing higher last Friday after Federal Reserve Chair Jerome Powell expressed confidence in the economy, the S&P 500 and Nasdaq posted their third consecutive weekly declines, down 3% and 3.5% respectively. SoFi's performance contrasts with the tech-heavy Nasdaq Composite, where other companies like Broadcom saw gains due to positive AI demand, as well as the contrasting fortunes of Walgreens, which surged following a buyout deal. While intense market volatility led to mixed performances across sectors, the overall economic environment with subdued jobs data and mixed corporate earnings likely compounded SoFi’s stock pressure last week.


Over the last year, SoFi Technologies posted a total shareholder return of 63.29%, significantly outperforming both the US Consumer Finance industry, which gained 24%, and the broader US Market, with an increase of 11.9%. Integral to SoFi's strong performance was its transition to profitability, highlighted by the full year 2024 net income reaching US$498.67 million from the previous year's net loss of US$300.74 million. The company also experienced substantial revenue growth, with Q3 2024 revenues climbing to US$723.37 million from US$564.27 million the previous year.

The launch of a new robo-advisor platform in November 2024, offering access to alternative asset classes, added to SoFi's appeal. The company's raised earnings expectations throughout 2024 further bolstered investor confidence, with guidance adjustments reflecting improved EPS projections. Another factor enhancing SoFi's market position was its impressive profit acceleration, marked by significant growth in net income across successive quarters of 2024.


This article by Simply Wall St is general in nature. We provide commentary based on historical data and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned.

How does AI enhance digital marketing strategies today?

 

AI enhances digital marketing by enabling personalization at scale, providing advanced customer insights, automating tasks, and optimizing campaigns through tools like chatbots and AI-powered advertising platforms.

Here's a breakdown of how AI is transforming digital marketing strategies:

1. Enhanced Personalization and Customer Experience:

Personalized Content & Recommendations:AI algorithms can analyze customer data to understand preferences and deliver highly relevant content and recommendations, improving engagement and conversion rates.

Targeted Advertising:AI-powered advertising platforms allow marketers to precisely target ads to specific audiences, increasing campaign effectiveness and ROI.

Chatbots and Customer Support:AI-powered chatbots can handle basic customer inquiries and provide 24/7 support, improving customer satisfaction and freeing up human agents for more complex issues.

Predictive Analytics:AI tools can analyze past customer behavior and predict future trends, enabling marketers to anticipate needs and proactively respond to them.

2. Automation and Efficiency:

Automated Email Campaigns:AI can automate email sequences and personalize email content based on customer data, leading to higher open and click-through rates.

Content Optimization and Creation:AI tools can assist with keyword research, content creation, and optimization, improving search engine rankings and organic reach.

Real-time Campaign Management:AI allows for real-time monitoring and optimization of marketing campaigns, enabling marketers to make data-driven decisions and adjust strategies as needed.

3. Gaining Deeper Customer Insights:

Sentiment Analysis:

A.udience Segmentation:AI can help marketers identify and segment their audience based on various factors, enabling more targeted and effective campaigns.

Predictive Analytics:By analyzing past customer data, AI can predict future purchasing behavior and customer churn, allowing marketers to take proactive measures.

Read More.....


Greenland Has Said: Things Trump Is Ignoring About Us

 


The Greenlanders cast their ballots. Anders Visited, a Danish member of the European Parliament, issued a message last week that was nearly as explicit as theirs: "Mr. Trump, f*** off."

Qulleq, the only party whose leader claims to believe in Donald Trump, with roughly 1% of the vote after the bulk of votes have been tabulated. On the other extreme, with almost 30% of the vote, the Demokraatit Party is seen as the election victor.

Before the election, Democratic leader Jens-Frederik Nielsen expressed his hope that

 Greenlanders would make it clear to Trump that Greenland is not for sale, calling Trump's remarks "a danger to our political independence."

"We would rather not be Americans. No, being Danes is not what we desire. Our goal is to become Greenlanders. Additionally, we hope to become independent in the future. And we do not want his hope; we want to construct our own nation on our own.

However, what is it about the Greenlanders' insistence that "Greenland is not for sale" that Trump finds incomprehensible? Why is not he fulfilling his pledges to protect Greenlanders and make them wealthy?

Greenland Is The Answer To Very Different Questions

When Trump looks at Greenland, he clearly sees something different than what the Greenlanders see. While Trump sees an opportunity, the Greenlanders see the reality of their everyday lives. For Trump, Greenland is the answer to “What do I want?” and “How do I get it?” For the Greenlanders, it is the answer to “Who are we?” and “Why are we not something else (for example Danish or American)?”

For Trump, Greenland is a question of doing – making deals, buying, strategyzing, innovating – for the Greenlanders, it is a question of being: Living in accordance with the nature in and around them. And respecting the history and culture that made them who they are. By approaching and talking about Greenland as a commercial product, Trump makes not only Greenland, but also the identity of Greenlanders, look like something that can be traded and negotiated.

And that’s why it doesn’t matter how many billions of dollars, he says he will invest in making Greenlanders rich. That’s why not even the protection from “the Greatest Nation anywhere in the World” seems to do the trick:

Because if Trump doesn’t get that the question of Greenland is a matter of identity to the Greenlanders – not products, dollars, minerals, or military – he will protect the wrong things. What Trump is offering is worth nothing to Greenlanders if they have to give up who they are and what they care about to get it.

To Be Or Not To Be Greenlander, That Is The Question

Being Danish, I know as little about being a Greenlander as Trump does. And yet there is a difference. Not because of the historical relationship between Denmark and Greenland. But because I know what it’s like to have your identity shaped by a history, culture, and language that is not American.

Like Greenlanders, people of Denmark and most other European countries don’t have English as their native language. We learned about the world through Greenlandic, Danish, Swedish, German, Ukrainian, Spanish and a host of other languages. And our cultures are shaped, and continue to be shaped, by the similarities and differences we encounter when we speak to each other in languages that are foreign to all of us.

For example, Greenlandic children are of course taught Greenlandic in school. But they are also taught Danish and English. Likewise, Danish schoolchildren are taught Danish, English and German or French. With each language comes not only a different way of speaking, but also a different way of thinking. And having to translate between different ways of speaking and thinking is a constant reminder of the differences in how we understand ourselves and our surroundings.

Or put differently, having your identity shaped by a history, culture, and language that is not American makes the question of identity an actual question. The translations challenge your perception of who you and others are, and why you and they are not something else. So you have to ask. To make room for your fundamental differences and the fact that they may care about and value something different than you do.

Greenland Doesn’t Care About Trump’s Billions

When Trump talks about the billions of dollars he will invest to create new jobs and make Greenlanders rich, he is doing the opposite. He is not making room for the Greenlanders’ identity and values. Rather, he is sharing his own. While Europe is shaped by different languages and cultures, the U.S. is shaped by the promise of a blank slate: Whoever you are and wherever you come from, you can start over.

The American dream is not just about creating a better future, it is also about putting the past behind you. The U.S. was founded by people who left Europe to escape tyranny, religious and political persecution, or poverty. They captured their values in the Declaration of Independence, saying: “We hold these truths to be self-evident, that all men are created equal, that they are endowed by their Creator with certain unalienable Rights, that among these are Life, Liberty and the pursuit of Happiness.”

While most European will probably agree with these self-evident truths, they will struggle with the idea that we can put our past behind us. Throughout millennia of European history, the experiences and memories of not being able to start over have left their mark. Thousands of years of making mistakes, repeating mistakes, and working together to avoid repeating them again have made history what Europeans have in common: You may even say that respect for history has become part of the common identity of Europeans – just as the pursuit of a better future is part of the identity of Americans. Not recognizing and respecting this fundamental difference can be fatal.

What “Greenland Is Not For Sale” Really Means

Assuming that billions of dollars are the answer to the Greenlanders’ questions seems to have had the opposite effect of what Trump wanted. Instead of pushing Greenland into the arms of the United States, it has shed light on what Greenland and Denmark have in common. The politicians and people of Denmark have treated the politicians and people of Greenland horribly. But unlike Trump, the Danish politicians and people understand that the question of Greenland is not a matter of dollars, minerals, innovation, or even international security. It is a matter of recognizing and respecting that being Greenlandic is different from being anything else. And that Greenland therefore should not be bought, owned or controlled by anyone but the Greenlanders.

Trump's promise to rule the island dominates Greenland's election vote.

 


In an election that was heavily influenced by Trump's pledge to seize control of the vital Arctic island, voting was prolonged at many polling places due to high voter turnout.

In a referendum that will decide which leaders challenge US President Donald Trump's promise to seize control of the strategically located Arctic nation, unofficial results are anticipated to be announced soon after polling ended.
Due to significant voter turnout at several of the 72 polling places located throughout the mineral-rich island, where 40,500 individuals were eligible to vote, voting was extended by 30 minutes past the 22:00 GMT deadline on Tuesday.

There were no exit polls, and a final tally of the vote could take between three and five hours to complete, Greenland’s election authority said.

Official results will not be certified for weeks as ballot papers make their way to the capital, Nuuk, from remote settlements by boat, plane and helicopter.

Images and video clips shared on social media showed people queueing in the ice and snow outside polling stations in Nuuk up to 45 minutes before voting closed. Earlier in the day, long queues were also reported at voting centers.

Since taking office in January, Trump has promised to make Greenland – a semi-autonomous territory of Denmark – part of the US, saying it is vital to US security interests.

The vast island, with a population of just 57,000, has been caught up in a geopolitical race for dominance in the Arctic, where melting ice caps are making its rich resources of rare earth metals more accessible and opening new shipping routes.

Greenland’s prime minister, Mute Bourup Egede, called the election last month, saying the country needed to be united during a “serious time” that is unlike anything Greenland has ever experienced.

While Trump has been outspoken about his desire to control Greenland, both Russia and China have also intensified military activity in the Arctic region.

Greenland is a former Danish colony and a territory since 1953. It gained some autonomy in 1979 when its first parliament was formed, but Copenhagen still controls foreign affairs, defense and monetary policy and provides just under $1bn a year to the economy.

In 2009, Greenland won the right to declare full independence through a referendum, even though it has not done so out of concern that living standards would drop without Denmark’s economic support.



Julie Rademacher, a consultant and former adviser to Greenland’s government, said that early on, the election campaign focused on the anger and frustration aimed at historical wrongdoings by former colonial ruler Denmark.

“But I think the fear of the US imperialist approach has lately become bigger than the anger towards Denmark,” Rademacher said.

The Reuters news agency spoke to more than a dozen Greenlanders in Nuuk, all of whom said they favoured independence, although many expressed concern that a swift transition could damage the economy and eliminate Nordic welfare services like universal healthcare and free schooling.

“We don’t want to be part of the US for obvious reasons; healthcare and Trump,” said Tuuta Lynge-Larsen, a bank employee and Nuuk resident, adding that this election was especially important.

A poll in January suggested that the majority of Greenland’s inhabitants support independence but are divided on timing.

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Agentic AI

 


Agentic AI: What is it?

An AI system that acts independently, adjusts in real time, and resolves multi-step problems according to context and goals is referred to as agentic AI.

Multiple AI agents that make use of Large Language Models (LLMs) make up agentic AI systems. By improving natural language comprehension and decision-making, enabling AI agents with LLMs makes user interactions more efficient and natural.

Both traditional and generative AI first showed a lot of promise in tackling these issues. They did not, however, provide comprehensive enterprise solutions that could carry out difficult activities and accomplish company objectives on their own. Non-agentic rule-based AI systems are capable of automating basic activities, but their rigid adherence to rules limits them in important ways. This implies that they are incapable of learning, adapting, or making choices. Thus, they are unable to compete with contemporary businesses.

By giving computers the ability to do tasks autonomously, analyze issues, create strategies, and take action based on predetermined objectives, agentic AI offers a completely new paradigm for AI to power enterprise solutions.  By offering complete solutions that can independently accomplish business objectives, agentic AI transforms company operations.

Types of AI Agents in an Agentic Architecture

AI agents are the foundational elements of agentic AI architecture, driving the future of intelligent automation. At its heart, agentic AI is about seamlessly integrating specialized agents, each designed for a unique purpose.

Agent assist enables agents capable of efficiently handle their day-to-day tasks directly with human-AI collaboration. Some AI agents excel at aggregating and delivering information from diverse sources, making them ideal for dynamic, less-regulated environments. Others are meticulously crafted to operate within strict compliance frameworks, ensuring every action adheres to rigorous standards.

In addition, workflow-focused agents are the masterminds of automation. They intelligently generate and execute workflows across applications, autonomously identify the right APIs, determine the optimal sequence, and flawlessly fulfill user requests.

The true strength of the agentic AI system lies in the agentic orchestration of these diverse agents. The architecture enables them to be grouped into logical domains, simplifying deployment and management for different teams within an organization. This ensures that while each team can operate independently, they remain aligned with a cohesive AI strategy that amplifies the entire business.

Another compelling feature is the ability to integrate external agents that were not originally built on the platform. This flexibility allows companies to continuously innovate, incorporating new technologies without disrupting the existing system. It’s about fostering an ecosystem where all components work harmoniously, driving superior outcomes across the board.

The agents in agentic AI can be categorized into four types:

  • Generative Information Retrieval Agents: Agents for knowledge serving less-regulated environments/topics.
  • Prescriptive Knowledge Agent: Agents for knowledge serving highly regulated environments/topics.
  • Dynamic Workflow Agents:  Action Agents
  • User Assistant Agents: User assistant agents can help individual users directly with their day-to-day tasks

We will cover different types of agents in detail in a separate document. Let’s dive deeper into the architecture of agentic systems.

The Evolution and Architecture of Agentic AI Systems

At the core of agentic AI are sophisticated decision-making processes. These systems meticulously weigh options, anticipate outcomes, and respond effectively to unforeseen challenges. They enhance their problem-solving capabilities by consulting multiple large language models (LLMs) and cross-referencing their insights.

Moreover, agentic AI systems are engineered to thrive in dynamic environments, adjusting real-time strategies based on feedback. This dynamic approach ensures optimal performance across diverse and unpredictable environments.

Another key element in AI agents’ operations is Machine Learning. It enables them to learn from data, recognize patterns, and make predictions. These systems learn to identify patterns, make predictions, and refine their decision-making by ingesting vast amounts of data. This continuous learning empowers them to tackle an ever-expanding range of challenges with increasing sophistication.

At a high level, the process unfolds in the following key stages:

  1. User Provides Instruction: The user interacts with the AI system, offering a natural-language prompt, similar to directing a trusted AI assistant. The system interprets the user’s intent and may seek further clarification if needed.
  2. Agent System Plans, Allocates, and Executes Work: The system transforms the prompt into a structured workflow, dividing it into tasks and subtasks. A managing subagent assigns these tasks to specialized subagents. These subagents, equipped with relevant domain knowledge and various tools, leverage prior experiences and established expertise to coordinate and utilize organizational data and systems to complete the assignments.
  3. Agent System Iteratively Refines Output: During the process, the agent may request additional input from the user to ensure the accuracy and relevance of the work. The system refines the output based on user feedback, working iteratively until the desired result is achieved.
  4. Agent Executes Action: Finally, the agent carries out any necessary actions to fully complete the task the user requests.

There are different types of AI agents, which are fundamental building blocks of agentic systems.

Let’s take a closer look at the components of an agent. An AI agent within an agentic system consists of three main components:

  • A prompt
  • Memory for the Agent
  • The Tools

Prompt: This defines how the system operates and outlines the specific goals an agent must achieve, along with the constraints to follow. Think of the prompt as the blueprint for the multi-agent system, setting the master plan for what each agent needs to accomplish and the methods to do so. It acts as a compass, guiding the agents and ensuring they work towards shared objectives within a structured framework. For complex systems, breaking responsibilities among multiple agents helps keep each prompt straightforward, thus managing complexity more effectively.

Memory: This is the core of an LLM agent, serving as their repository of knowledge and experiences. Just as humans rely on past experiences to make decisions, LLM agents use memory to understand context, learn from previous interactions, and make informed choices. Memory can involve simply passing conversation history back to the LLM or providing it with extracted semantic information from conversations.

Tools: These are the versatile instruments that enable agents to perform various tasks efficiently. Tools can include APIs, executable functions, or other services that help agents complete their objectives. Understanding these fundamental components, we can now explore how they integrate and function within a single-agent system.

Agentic AI architecture Simplified

Single-Agent System

A single-agent system features a single AI agent with various tools to address specific problems. These systems are designed for autonomous operation, utilizing both the tools’ capabilities and the reasoning power of the LLM to formulate and execute a step-by-step plan. The agent creates a strategy to achieve the user’s simple or complex goals and applies the necessary tools to complete each step. As each step progresses, the outputs are compiled to produce the final result.

The approach to achieving a user goal can vary based on the available tools, overall objectives, and constraints. Therefore, it’s crucial to design the prompt effectively, ensuring it directs the agent’s behavior and optimizes resource use to meet goals efficiently.

Why Single-Agent Systems Remain Relevant

Single-agent systems offer several advantages. Their simplicity makes them easier to design, implement, and manage since there’s no need to coordinate between multiple agents. This reduces the complexity of communication and interaction within the system.

These systems also provide greater coherence and consistency in decision-making. With only one agent in control, there is no risk of conflicting goals or actions, leading to more predictable and stable behavior, simplifying system understanding and debugging.
Single-agent systems are particularly effective for tasks that don’t require complex coordination. They can efficiently achieve user goals when centralized decision-making is essential.

Limitations of Single-Agent Systems

However, single-agent systems have limitations. They are often narrowly focused, which can restrict their ability to handle diverse or rapidly changing tasks. Their specialized nature may hinder performance in environments with varied requirements.

Scaling a single agent to manage broader or higher-value tasks requires significant redesign. Simply enhancing a single agent with additional capabilities may not address all scalability challenges and can lead to performance bottlenecks.

Additionally, single-agent systems face constraints related to memory and processing power. With all responsibilities concentrated in one agent, it must contend with finite resources, which can impact its overall efficiency and effectiveness.

Single Agent System Architecture

Multi-Agent System

In a multi-agent system (MAS) architecture, multiple independent agents—each powered by language models—collaborate to tackle complex tasks. Unlike single-agent systems, where one agent handles everything, MAS leverages each agent’s unique roles, personas, and tools to enhance efficiency and decision-making. These agents bring diverse perspectives and specialize in specific areas, which allows them to work together seamlessly and solve problems more effectively.

One key advantage of multi-agent system (MAS) architecture is its scalability. As demands increase or task domains expand, additional agents can be integrated into the system without significant redesign.

This approach ensures the system can grow and adapt to new challenges quickly. To complement this, MAS in multi-agentic AI offers built-in fault tolerance; if one agent fails, others can step in, ensuring the system functions smoothly. By enabling specialization, collaboration, and resilience, multi-agent systems provide a powerful and flexible solution for handling complex, evolving tasks.

Multi Agent System Architecture in Agentic AI

Fundamental Principles Guiding Agentic Artificial Intelligence Architecture

Agentic AI architecture is built on foundational principles that ensure its effectiveness and adaptability in today’s dynamic technological landscape. These principles are as follows:

  • Modularity – Involves breaking down complex functions into specialized modules, each designed for specific tasks like perception or action. This approach simplifies development and maintenance, allowing seamless upgrades without disrupting the entire system. By embracing modularity, enterprises enhance flexibility and resilience, facilitating the seamless integration of new technologies as they emerge.
  • Scalability – Enables AI agents to expand their computational resources to manage increasing data and complexity. Leveraging distributed scalable computing power and cloud infrastructures ensures that systems can grow and adapt to rising demands without sacrificing performance, keeping enterprises agile in a dynamic environment.
  • Interoperability – Ensures that diverse modules and systems work together seamlessly. By utilizing standardized communication protocols and data formats, organizations can integrate various technologies and services effortlessly, maximizing operational efficiency and enhancing overall performance.
  • Reinforcement learning (RL) – Allows AI systems to improve continuously through adaptive learning. Unlike traditional AI, RL systems evolve by interacting with their environments and learning from feedback, optimizing decision-making and responses over time. This ensures that AI solutions remain responsive to user needs, driving greater satisfaction and engagement.

Together, these principles create a robust framework that drives key innovations, flexibility, and efficiency in AI solutions, positioning organizations for success in an ever-evolving landscape.

Agentic AI Applications: Real-World Use Cases

Applications of Agentic AI have already shown their potential to revolutionize how individuals interact with technology with devices like Rabbit R1. This consumer-facing success offers a tantalizing preview of agentic AI’s impact on the enterprise. At its most basic, it can automate routine tasks, freeing employees to focus on higher-value work. At its zenith, it could become a fully autonomous digital workforce capable of understanding and executing complex business objectives.

One critical application area for agentic AI is supply chain management. These intelligent systems can analyze data, predict demand, and streamline workflows, enhancing efficiency and adaptability in complex supply chain scenarios.

But please remember Agentic architecture is not a one-size-fits-all solution. Enterprises need to tailor the implementation, considering various factors. Successful implementation demands a strategic, organization-specific approach.

Firstly, crystallize your objectives. What do you aim to achieve with agentic AI? Is it to streamline operations, enhance decision-making, or develop innovative products? Clearly defined goals provide a roadmap for development and evaluation.

Secondly, identify the organizational touchpoints. Understand which departments, processes, and data will be impacted. A comprehensive assessment ensures that agentic AI aligns with overall business strategy and minimizes disruptions.

Thirdly, cultivate a culture of continuous learning. Agentic AI is an evolving technology. Establish a framework for ongoing evaluation, adaptation, and improvement. This ensures the system remains aligned with business needs and delivers maximum value.

A standout feature of Agentic AI Architecture is its capability to strategically organize agents by functional domains such as IT, HR, Engineering, and more. This intelligent structuring enables enterprises to deploy highly specialized agents tailored to the unique demands of each department. By aligning agents with their specific functional areas, organizations can optimize AI workflows, boost task precision, and ensure that each agent operates within its area of expertise.

Let’s look at a real-time use case and how AI agents can bring transformations.

– Improving Code and Quality Management

High code quality is essential for successful engineering teams. However, managing code reviews, ensuring coding standards, and responding to incidents can be resource-intensive and prone to errors. By integrating agentic AI into code and quality management, teams can automate these tasks, empowering engineers to tackle more complex problems while keeping the codebase robust and reliable. Here’s how AI can enhance code and quality management:

– Code Acceleration and Standard Adherence

Text-to-code agents are powerful tools for engineering teams. They can generate code structures from simple text descriptions, allowing engineers to move quickly from concept to implementation. By creating boilerplate code structures automatically, these agents help ensure that coding standards and best practices are consistently applied.

– Automated Code Reviews and Quality Checks

Code reviews are vital for maintaining quality and catching bugs early. However, manual reviews can be time-consuming, especially with frequent code changes. Agentic AI can streamline this process by automatically analyzing code changes, flagging potential issues, and assessing code against predefined standards. This automation speeds up reviews, reduces the burden on senior engineers, and ensures that no pull request goes unreviewed, enhancing overall code quality.

– Incident Response Automation

Swift incident response is critical to minimizing impact and preventing future issues. Agentic AI can automate the incident response process by triggering predefined protocols when an incident occurs. The AI can notify team members, initiate rollback procedures, and generate comprehensive incident reports, ensuring that all relevant details are captured and tracked. This speeds up response times and leads to better post-incident analysis.

– Continuous Integration and Testing Automation

Maintaining code quality requires that changes do not introduce new bugs. Agentic AI can optimize continuous integration (CI) and testing processes by automating test execution and code integration. The AI can trigger tests with each new code commit, analyze results, and update Jira tickets. If tests fail, it can notify engineers and suggest fixes. By focusing on critical areas of the codebase, the AI ensures testing resources are used efficiently.

Customizing and Integrating Agentic AI Solutions

Customization is crucial to maximizing the effectiveness of agentic AI. This includes integrating AI with business databases, incorporating knowledge resources, tailoring models to specific roles, and aligning system responses with organizational goals. Successful implementation of agentic AI requires effective human-AI collaboration, ensuring that AI actions align with business strategies.
To harness the power of agentic AI, organizations must:

  • Knowledge Foundation: Codify business expertise and processes to inform intelligent agent behavior.
  • Infrastructure Optimization: Align data and systems for seamless AI integration.
  • Human-AI Collaboration: Implement robust oversight mechanisms to balance autonomy and control.

While doing this, here are some critical considerations that enterprises should keep in mind while integrating and adopting Agentic AI

  • Explainability: Understanding the reasoning behind an agentic AI’s decisions is often complex, making it difficult to troubleshoot issues or build trust.
  • Bias: If the training data is biased, the agentic AI may perpetuate those biases, leading to discriminatory outcomes.
  • Data privacy and security: AI systems often handle large amounts of sensitive data, making data privacy and security a critical concern.
  • Ethical considerations: AI-powered autonomous agnets nature raises ethical questions about responsibility, accountability, and the potential for misuse.
  • Integration complexity: Integrating agentic AI into existing enterprise infrastructure and workflows can be challenging and time-consuming.
  • Risk management: It is crucial to identify and mitigate potential risks associated with AI systems, including system failures, data breaches, and reputational damage.

As enterprises integrate agentic AI, a strategic approach is essential for success. Enterprises should adopt a gradual deployment strategy, starting with controlled pilot projects to refine AI capabilities. Continuous monitoring ensures transparency and accountability by tracking AI decisions in real time. A layered security framework is crucial for protecting AI systems, incorporating multiple defenses to safeguard against potential threats. Collaboration with AI and cybersecurity experts further optimizes the integration process, ensuring that best practices are followed and the system is secure and effective.

Another important aspect enterprises should consider when working with enterprise AI agents is setting clear boundaries between humans and AI agents. This is essential for maintaining control and ensuring responsible use.

While agentic AI is powerful, it relies on human expertise and validation to deliver accurate, relevant, and ethical outputs. Domain experts and technical specialists play a crucial role in guiding these systems, determining the data used in training, and overseeing what AI “sees” in both the real and virtual worlds. Continuous human involvement ensures AI remains a reliable and trustworthy tool, particularly for more autonomous Artificial Intelligence I systems. The objective is to use these advanced agents to enhance human capabilities, not replace them. As AI evolves to optimize and improve itself, we must provide it with the correct data and guidance to do so effectively.

The Future Landscape of Agentic AI

Enterprises heavily rely on considerable datasets to successfully implement agentic AI. The quality and diversity of training these datasets are critical to the success of AI agents. High-quality data ensures accurate learning, while diverse data helps AI agents generalize across different scenarios. Without diverse data, AI agents risk being biased or performing poorly in unexpected situations.

Companies often struggle to implement AI due to data-related challenges. These challenges can be attributed to data regulations, sensitivity, financial implications, and scarcity.

This is where synthetic data comes into play, offering a valuable solution. Synthetic data is a tool that can be utilized to create complex and varied datasets that are like real-world data but without any personal information, which reduces the risk of violating compliance regulations. Moreover, synthetic data can be produced whenever required, addressing the problem of data scarcity and enabling more effective AI model training.

Combining Synthetic and Real-world Data

The quality and breadth of data received by an agentic AI system are crucial for enabling it to navigate complex business environments. For AI agents to operate independently, they must possess foundational AI reasoning skills and domain-specific knowledge, including autonomous goal-setting, planning, and adaptability, tailored to specific industries or functions.

Companies increasingly use synthetic and real-world data to train their AI systems to achieve this. While real-world data offers genuine insights, it often comes with limitations like scarcity, privacy concerns, and inherent biases. On the other hand, synthetic data allows for a controlled environment where diverse scenarios and edge cases can be generated, though it may not always perfectly replicate real-world complexities.

The synthetic and real-world data blend offers a compelling solution for training AI models. By integrating synthetic data, AI models can gain from increased diversity, more extensive data volumes, and reduced biases. Synthetic data can fill gaps in real-world datasets, simulate rare events, and ensure balanced representations, ultimately enhancing model robustness.

Looking ahead, there is a strong expectation that agentic AI will increasingly rely on synthetic and real-world data to train complex models. However, it’s crucial to ensure the quality of this data and maintain consistency and compatibility between the two data types. Generative models used to create synthetic data can degrade over time, so continuous monitoring and refinement are necessary to sustain performance and accuracy.

Conclusion

Agentic AI is poised to revolutionize how businesses operate. By empowering AI with independence and problem-solving abilities, we’re ushering in an era where AI and humans collaborate seamlessly. These agentic systems can tackle complex challenges, make data-driven decisions, and continuously learn to improve performance. However, realizing this potential requires a delicate balance. Transparency, ethics, and human oversight are paramount.

By responsibly integrating agentic workflows, organizations can unlock unprecedented productivity and innovation.

Aisera is leading the enterprise Generative AI revolution with a comprehensive, enterprise-grade platform built on the core principles of modularity, scalability, interoperability, and reinforced learning. Our solution offers a full spectrum of agentic AI capabilities, including intelligent information retrieval, prescriptive guidance, dynamic workflow automation, and intuitive user assistance.

By seamlessly integrating with existing enterprise systems, Aisera provides a smooth path to unlocking new possibilities and the full potential of enterprise GenAI. Book a custom AI demo to experience the future of GenAI with Aisera today!