Showing posts with label agenticai. Show all posts
Showing posts with label agenticai. Show all posts

Friday, May 22, 2026

Agentix AI Agents The World’s First AutoGPT Autonomous Business Agent System

You KNOW the opportunity is life-changing. You KNOW people made money chatting with AI back in 2022-2023. AI is moving so FAST right now, kids, college dropouts, stay-at-home moms, retirees… Are making LIFE-CHANGING income without learning a single line of code.

Upwork reported AI-related freelance skills demand grew 109% in 12 months. AI video generation demand surged 329%. The buyers are already looking for what agents can deliver. ChatGPT is a great chat buddy. It does a bit of research and low level tasks that you have to copy and paste between tabs to get stuff done.

Every week Claude drops new features aggressively competing with OpenAI and Gemini. Google Gemini on the other hand is like a yo-yo. Sometimes brilliant and most times… useless. Then you have OpenClaw, that requires a computer science degree to set up and runs at HIGH risk. Have you ever woken up to $456 spent overnight by OpenClaw running in a loop?

You see, EVERY ONE of these AI companies are cutting each others throat to get ahead of each other. FINALLY… we figured out how ANYONE can launch their own fleet of autonomous AI agents that run for hours. NO VPS. NO Mac Mini. NO terminal. NO Node.js. NO B.S.

In 1-click YOU CAN FINALLY deploy agents or multiple agents that can search the web, execute code, connect to thousands of external tools and MAKE YOU MONEY. We didn’t scour the web and dumped together a bunch of useless generic “write a blog post” automations, tools and skills.

These are SPECIFIC agents we use in our business.

  • Every agent knows exactly what it is. Email writer. Market researcher. Image creator. Page builder. No confusion, no drift.
  • One clear objective per agent. “Write 7 promo emails.” “Build a landing page.” It doesn’t stop until the mission is complete.
  • Guardrails that keep agents on track. No hallucinating. No going rogue. Every action stays inside the lines you set.
  • Pre-loaded from the $535K Skill Library. Not generic “write content” fluff. The exact skills behind documented, proven income.
  • 9,430+ connections. Gmail, Google Drive, Mailchimp, Shopify, WordPress, Twitter/X, IG, FB… your agent logins and acts accordingly.
  • You control what each agent can access. Your Gmail, your Shopify, your Stripe. Nothing happens without your say-so.
  • The agent thinks, acts, evaluates, and adjusts autonomously for hours unlike a chatbot waiting for your next prompt.

Agentix AI Agents is automatically plugged into 9,000+ tools, MCP servers and websites for Multi-Stage Automation that runs for hours on secured, enterprise-grade cloud infrastructure WITHOUT writing a single line of code, setting up a VPS or installing Node.js……

Source: Agentix AI Agents 

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AgenticAI ,ArtificialIntelligence ,AIInnovation ,MachineLearning ,SmartTechnology ,FutureOfAI ,TechTrends ,AIRevolution ,DigitalEmpowerment ,IntelligentSystems ,AutoAI ,AIforGood ,AIinAction ,AdvancingAI ,NextGenAI ,AIResearch ,CognitiveComputing ,TechForGood ,AIApplications

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Tuesday, August 26, 2025

Agentic AI Has Companies Excited and Security Experts Freaked Out

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Agentic AI is being heralded as the future of the generative AI revolution by leaders in the field. From ChatGPT’s integration of agentic features to the rise of Comet (the agent-based web browser from Perplexity) and Chinese-born Manus, the trend of handing more control to AI tools seems inevitable. At least, that’s the view of Microsoft CEO Satya Nadella, Shopify CEO Tobias Lütke, Amazon executive chairman Jeff Bezos, and Nvidia CEO Jensen Huang…….Continue reading….

By: Chris Stokel-Walker

Source: Fast Company

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Critics:

Agentic AI is a class of artificial intelligence that focuses on autonomous systems that can make decisions and perform tasks without human intervention. The independent systems automatically respond to conditions, to produce process results. The field is closely linked to agentic automation, also known as agent-based process management systems, when applied to process automation.

Applications include software development, customer support, cybersecurity and business intelligence. The core concept of agentic AI is the use of AI agents to perform automated tasks but without human intervention. While robotic process automation (RPA) and AI agents can be programmed to automate specific tasks or support rule-based decisions, the rules are usually fixed. Agentic AI operates independently, making decisions through continuous learning and analysis of external data and complex data sets. 

Functioning agents can require various AI techniques, such as natural language processing, machine learning (ML), and computer vision, depending on the environment. Particularly, reinforcement learning (RL) is essential in assisting agentic AI in making self-directed choices by supporting agents in learning best actions through the trial-and-error method. Agents using RL continuously to explore their surroundings will be given rewards or punishment for their actions, which refines their decision-making capability over time.

All the while deep learning, as opposed to rule-based methods, supports agentic AI through multi-layered neural networks to learn features from extensive and complex sets of data. Further, multimodal learning enable AI agents to integrate various types of information, such as text, images, audio and video. As a result, agentic AI systems are capable of making independent decisions, interacting with their environment and optimising processes without a human directly intervening.

AI agents can be used to perform small tedious tasks during web browsing and potentially even perform browser actions on behalf of the user. Products like OpenAI Operator, Perplexity Comet and Dia (from The Browser Company) integrate a spectrum of AI capabilities including the ability to browse the web, interact with websites and perform actions on behalf of the user. In 2025, Microsoft launched NLWeb, a agentic web search replacement that would allow websites to use agents to query content from websites by using RSS-like interfaces that allow for the lookup and semantic retrieval of content.

Products integrating agentic web capabilities have been criticised for exfiltrating information about their users to third-party servers and exposing security issues since the way the agents communicate often occur through non-standard protocols. In 2025, MIT’s study revealed that about 95% of enterprise generative-AI pilots fail to deliver measurable P&L impact[22]; a failure rate in business outcomes.

The report titled “The GenAI Divide: State of AI in Business 2025,” based on 150 executive interviews, a survey of 350 employees, and analysis of 300 deployments, and it attributes the failures largely to integration issues. The MIT NANDA report finds only about 5% of corporate generative-AI pilots are achieving rapid revenue acceleration, with the vast majority showing little to no impact on profit and loss statements.

It also notes a mismatch in spending (heavy on sales/marketing tools versus higher ROI in back-office automation) and highlights “shadow AI” usage complicating measurement and governance. One of the first reason on study was about “integration gap”, that chatbot does not find enough time to deploy themselves and adopt the workflow environment, leading to little to no measurable impact. Over half of budgets go to sales/marketing tools, while bigger returns often lie in back-office automation that reduces outsourcing and agency costs.

Study also notes that while building internal tools is possible, parternership with external agency is often more profitable than not. The study explicitly reports that external partnerships have about twice the success rate of internal builds (~67% vs ~33%), often yielding faster time-to-value and lower total cost, so buying/partnering can be more profitable than building in-house given today’s high failure rates.

With roughly 95% of enterprise GenAI efforts failing to reach measurable P&L impact, failed internal tools represent a significant sunk cost risk compared with proven vendor solutions that integrate and learn within workflows. For example, building your own AI tool that may not work as expected can result in loss than to buy an externel tool that perform the tasks more easily, resulting in profit.

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