Oliver Alf’s Post

2025 - Year of the Agent "Intelligent agents in AI will make your AI more useful Today’s AI models perform tasks such as generating text, but these are “prompted” — the AI isn’t acting by itself. That is about to change with agentic AI, or AI with agency. By 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously. Intelligent agents in AI are goal-driven software entities that use AI techniques to complete tasks and achieve goals. They don’t require explicit inputs and don’t produce predetermined outputs. Instead, they can receive instructions, create a plan and use tooling to complete tasks, and produce dynamic outputs. Examples include AI agents, machine customers and multiagent systems. Intelligent agents in AI are nascent but quickly maturing While agentic AI is still in early stages, it's not to soon to gain an understanding of the technology, determine how to manage risk and prepare your tech stack. The future of AI is about agency — and productivity By giving artificial intelligence agency, organizations can increase the number of automatable tasks and workflows. Software developers are likely to be some of the first affected, as existing AI coding assistants gain maturity. Agentic AI has the potential to significantly empower workers. It’ll enable them to develop and manage complicated, technical projects — whether microautomations or larger projects — through natural language. Intelligent agents in AI will change decision making and improve situational awareness in organizations through quicker data analysis and prediction intelligence. While you’re sleeping, agentic AI could look at five of your company’s systems, analyze far more data than you ever could and decide the necessary actions. Current AI agency is low, but expect it to grow AI agency is a spectrum. At one end are traditional systems with limited ability to perform specific tasks under defined conditions. At the other end are future agentic AI systems with full ability to learn from their environment, make decisions and perform tasks independently. A big gap exists between current LLM-based assistants and full-fledged AI agents, but this gap will close as we learn how to build, govern and trust agentic AI solutions." #ai #genai

Tired of outdated information in your AI apps? RAG is your solution! Why should your business consider Retrieval-Augmented Generation (RAG) for building AI-driven applications? ✅ Access to Updated Information: Unlike traditional models, RAG pulls the latest info from external sources like news articles or company documents, keeping responses current and accurate. ✅ Reduce AI Hallucinations: RAG taps into relevant knowledge bases to minimize the risk of your AI making things up. ✅ Domain-Specific Expertise: Tailor your AI’s knowledge base for your industry—whether it's healthcare, finance, or retail—RAG ensures precise, domain-specific information. ✅ Flexibility: No need to retrain the entire model. Just update the knowledge base as needed and stay adaptable. ✅ Real-World Use Cases: From customer service bots to research assistants, RAG-powered AI delivers practical, up-to-date results. Stay ahead of the competition by integrating RAG for more reliable, real-world applications. Follow our page for more practical tips on implementing AI agents and boosting your business with RAG technology!

So many businesses have been clamoring to introduce AI products to their internal or external product suite in the last year. Many hours have been spent developing ill-conceived and un-validated POCs. The companies with the most opportunity for AI success at this time are those with the CONTENT and DATA in place to support RAG AI models. Where there is a need to synthesize, summarize, or rewrite content for more digestible delivery to different platforms, current LLM models can provide useful and efficient solutions. If those things aren’t in a good state, then funds will be better used to cleanup data or develop strong content models and repositories. I’ve been working with a client who is using LLMs to increase publishing efficiency - with keen attention to workflows, editorial oversight, user testing, and model feedback. What AI use cases have you seen lately that are doing it right - or missing the mark?

# How Agilebase Harnesses AI: Enhancing Efficiency While Ensuring Integrity The hype about AI is overwhelming. So, we at Agilebase thought it might be helpful to share how we use AI. We think it is a balanced, practical approach to artificial intelligence. ## Our AI Philosophy We recognize both the potential and the concerns surrounding AI. Our goal? To use AI's benefits, while maintaining our unwavering commitment to ethical practices and data security. ## How We Use AI We've integrated AI into three specific areas of our no-code CRM system: 1. Initial System Setup AI generates a suggested schema based on client descriptions. It provides a quick starting point. 2. Test Data Generation AI creates realistic sample data, accelerating the testing process. 3. Complex SQL Calculations AI assists with intricate queries, enhancing precision and ease of use. ## Our Safeguards AI operates within defined parameters. We make sure it serves as a helpful assistant rather than a decision-maker. We don't use AI for subjective tasks or those with potential unintended consequences. AI systems don't access sensitive information; all training data is anonymized and protected. ## The Bigger Picture While we've seen a 20% productivity boost from AI, we're realistic about its limitations: AI excels at "Common Practice." However, it may offer a limited competitive advantage once adoption becomes widespread. We're cautious about hype. We have all seen recent re-examinations of AI performance claims. We consider the resource implications and broader societal impacts of AI development. ## Our Commitment At Agilebase, we're dedicated to using AI to align with our values and benefit our clients. We believe in transparency and continuous evaluation of our AI practices. #AIinBusiness #EthicalAI #NoCodeCRM #TechInnovation #AgilebaseAI

It is important to us at Agilebase that we ue AI in an ethical way.

𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗮𝗻𝗱 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 Agentic AI stands out as a transformative force redefining the landscape of business operations, customer engagement, and decision-making processes. As we embrace the potential of machine learning, we are witnessing the emergence of advanced AI agents capable of performing complex tasks, learning from experience, and ultimately enhancing productivity and efficiency. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜? Agentic AI refers to artificial intelligence systems designed to operate autonomously, making decisions and taking actions on behalf of users. Unlike traditional AI, which typically requires human intervention, Agentic AI can analyze data, draw insights, and execute tasks independently. This paradigm shift not only optimizes workflows but also empowers businesses to focus on strategic initiatives rather than mundane operational tasks. 𝗧𝗵𝗲 𝗥𝗼𝗹𝗲 𝗼𝗳 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 At the heart of Agentic AI's rise is machine learning - a subset of AI that enables systems to learn from data and improve over time without explicit programming. Modern machine learning algorithms can analyze vast datasets, identify patterns, and make predictions with remarkable accuracy. This capability allows AI agents to refine their performance continuously, becoming more effective in their roles as they gather insights from real-world interactions. 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗼𝗳 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 - 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: Agentic AI can automate repetitive tasks such as data entry, scheduling, and customer inquiries. By streamlining these processes, businesses can significantly reduce operational costs and free up human resources for high-value activities. - 𝗗𝗮𝘁𝗮-𝗗𝗿𝗶𝘃𝗲𝗻 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀: With the ability to analyze large volumes of data in real time, AI agents can provide actionable insights that inform decision-making. This leads to more informed strategies, optimized marketing campaigns, and ultimately better business outcomes. - 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: As businesses grow, so do their operational demands. Agentic AI can scale effortlessly, accommodating increasing workloads without compromising quality or performance. This scalability is essential for businesses aiming for rapid growth and expansion. - 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗘𝗱𝗴𝗲: By integrating advanced AI agents into their operations, businesses can stay at the forefront of innovation. This not only differentiates them in a crowded marketplace but also enables them to pivot quickly in response to market changes. Book an appointment and see how we can help your business by utilizing AI.

Check out what our founder James Faure has to say about how to ‘Transform your Workflows with Agentic AI’. Needless to say it seems to be all about AI these days but one has to dig deeper to identify those that can genuinely deliver the solution at a fixed price model and ensure that its clients don’t end up with escalating costs, pricing themselves out in the process and ending up in a disastrous position. This is why Clairo AI matters beyond being yet another solution in the space, so reach out to James and book a non obligatory consultation to learn more how Clairo can assist you with your wants and needs in the space, designed to disrupt for the real benefits in becoming more efficient and better optimized for an improve bottom line results! Any business solution must always positively impact your bottom line, not increase your cost centre liabilities! #clairomatters #sustainablesolution

AI Agents represent a vital shift towards autonomy in enterprise AI strategies, expanding businesses' operational scope and support employees with complex day-to-day tasks. These systems are characterised by their ability to learn from interactions and adapt to new situations, automate both routine and complex tasks, and make business-driven decisions. This unique combination of features makes Agentic AI a powerful tool across various industries. At Clairo AI, we are at the forefront of this transformation. Our platform harnesses the full potential of Agentic AI, providing businesses with the tools they need to build custom AI agents that are both powerful and sustainable. By utilising sustainable data centres, we not only reduce operational costs but also significantly lower the carbon footprint associated with running AI systems. Additionally, our secure infrastructure ensures that your proprietary data remains protected, enabling your agents to make informed decisions without compromising data integrity.Learn more about the power of Agentic AI, and how your business can leverage Clairo AI's technology to build powerful agents: https://lnkd.in/dBUcySqS #AgenticAI #AIAgents #ClairoAI #EnterpriseAI

In my opinion, the key to driving real results with AI initiatives lies in the harmonious combination of Data Engineering (DE), User-Centric Design (UD), and AI itself. Data Engineering is the foundation. Without reliable, well-structured data, even the most sophisticated AI models are bound to fail. DE ensures that the data is clean, moves efficiently, and is available for analysis, allowing AI to perform at its best. User-Centric Design is equally critical. As Jeremiah Chienda pointed out during one of our recent chats, no matter how powerful the AI model is, it won’t generate meaningful impact unless it’s designed with the user in mind. It’s not enough to just build great technology; we need to ensure it’s intuitive, solves real problems, and is easy for users to adopt. This focus on user needs ensures that AI solutions don’t just exist—they’re actually used and valued. Finally, AI acts as the engine driving everything forward. Once the data is properly engineered and the user experience is prioritized, AI has the power to extract insights and automate processes, and decision making. At the end of the day, Results = DE + UD + AI. It’s this combination that leads to real, impactful transformation. #AI #DataEngineering #UserExperience #Innovation

🔍 𝗥𝗔𝗚 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻: 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝘆𝗶𝗻𝗴 𝗔𝗜 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝘄𝗶𝘁𝗵 𝗩𝗲𝗿𝘁𝗲𝘅 𝗔𝗜 Making sure AI models deliver precise and quick replies is essential in a world where data is continuously changing. Retrieval Augmented Generation (RAG) can help in this situation. RAG provides real-time access to external knowledge sources, in contrast to traditional models that rely on fixed data, which improves the model's capacity to provide contextually accurate and current replies. 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗲𝗿𝗲 𝗩𝗲𝗿𝘁𝗲𝘅 𝗔𝗜 𝗺𝗮𝗸𝗲𝘀 𝗮 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 Smooth RAG Integration: Vertex AI offers the tools and infrastructure you need to integrate RAG into your AI processes with ease. As a result, retrieval-based systems and huge language models can be combined with less complexity. 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻  Vertex AI streamlines the entire process, from gathering your datasets to refining and optimizing the model, freeing developers to concentrate on creativity rather than overcoming technical obstacles. 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 You can implement RAG-enabled models at scale with Vertex AI, guaranteeing real-time updates and ongoing improvement based on fresh data. 💻 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Whether it’s building a chatbot that references live data or enhancing your AI system’s decision-making with real-time insights, Vertex AI guides you through each stage—data preparation, training, and model deployment—while optimizing for performance. For developers and organizations seeking to leverage cutting-edge AI with minimal complexity, Vertex AI offers an accessible path to unlock the full potential of RAG technology. How are you planning to use RAG in your AI applications? Let’s discuss below!👇 #RAG #TechticsAI #AIInnovation #MachineLearning #RealTimeAI #DataScience #AIIntegration #TechTransformation