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👉 If yes, connect with me via an introductory call or email me at [email protected] How do we work together?
What topics do I write about?
What kind of clients have I worked with? I have worked with numerous early-stage and growth-stage AI & SaaS startups including:
Pricing
Where can you read my blogs? Below is an archive with a selection of blogs that I've authored for various clients. Archive AI: Leadership & Best Practices
AI: Data & Governance
AI: Use cases
Team development
Misc
Comments
 This image illustrates a significant trend in OpenAI's innovative work on large language models: the simultaneous reduction in costs and improvement in quality over time. This trend is crucial for AI product and business leaders to understand as it impacts strategic decision-making and competitive positioning. Key Insights:
Generative AI startups can capitalize on the trend of decreasing costs and improving quality to drive significant value for their customers. Here are some strategic approaches 1. Cost-Effective Solutions:
2. Enhanced Product Offerings:
3. Strategic Investment in R&D:
4. Operational Efficiency:
What is Agentic AI?
Agentic AI refers to a type of artificial intelligence that can independently act and make decisions in dynamic environments. AI agents go beyond traditional data processing on a wide variety of tasks that mimic humanlike intelligence. It encompasses both hardware agents, such as robots and autonomous vehicles, and software agents, such as conversational agents and chatbots. Agentic AI has recently regained prominence with the advent of [large language models](https://www.nvidia.com/en-us/glossary/data-science/large-language-models/), such as OpenAI's [GPT-4](https://openai.com/research/gpt-4) and Meta's [Llama 2](https://ai.meta.com/llama/). These powerful models are trained on massive amounts of data and can understand goals, automate work and complete complex tasks. Additionally, conversational agents, such as [ChatGPT](https://chat.openai.com/auth/login) and [Claude](https://claude.ai), which were developed using these large language models, can act on users' goals, gather relevant information from the internet (or relevant knowledge bases) and offer solutions in an iterative fashion. In this article, you'll learn more about agentic AI, its capabilities and use cases, and its future prospects and possibilities. Why You Need Agentic AI? Agentic AI is built on a few fundamental characteristics that include perceiving the environment, making decisions based on perceived data and continuously learning in order to continually improve. For instance, hardware-based agents are embedded with sensors for vision, sound, motion, heat and humidity. Through these sensors, agents can collect data and learn to make sense of their environment. A popular example is [Mars exploration rovers](https://www.enterpriseai.news/2021/04/01/how-nasa-is-using-ai-to-develop-the-next-generation-of-self-driving-planetary-rovers/) that operate autonomously and perceive their environment to achieve objectives, such as collecting soil and rock samples. To make decisions in these environments, agents process the perceptual information using intelligent models to select the most optimal course of action. This decision-making mirrors how the human brain processes information, starting from basic perception and building up to higher-level cognitive functions. Moreover, it's not enough for agents to just perceive and act if they don't continuously improve their intelligence capabilities. This self-learning ability powered by mechanisms such as [reinforcement learning](https://en.wikipedia.org/wiki/Reinforcement_learning) is an essential feature of agents. Use Cases for Agentic AI The following are a few scenarios where agentic AI really shines: Autonomous Vehicles Self-driving cars or [autonomous vehicles](https://www.embedded.com/the-role-of-artificial-intelligence-in-autonomous-vehicles) are already in production. Companies like Tesla, Uber and Waymo have pioneered their development and deployed the vehicles in real-world environments. Armed with multiple AI modules that operate on various sensors, including [LiDAR](https://en.wikipedia.org/wiki/Lidar), which helps vehicles sense and understand their surroundings, autonomous vehicles represent sophisticated AI agents that can perform complex navigational tasks without human intervention. Quality Control in Manufacturing AI agents also find multiple applications in traditional industries, like manufacturing, logistics and agriculture. For instance, modern warehouses are powered by AI agents that can sort, pick up and place items without any human supervision. In agriculture, automated fruit-picking robots can detect fruits that are ready to be picked and harvest them without any damage. Similarly, at different points in the supply chain, robots are used to identify defects and weed out poor-quality products. Contextual Conversational Agents ChatGPT has become one of the most popular conversational agents ever, gaining more than a million users in the first week of its launch. These agents can encode a very large context or sequences of words, providing personalized responses to users' queries and demonstrating human-level capabilities across several tasks and domains. [Autonomous agents, like Auto-GPT and BabyAGI](https://www.fastcompany.com/90880294/auto-gpt-and-babyagi-how-autonomous-agents-are-bringing-generative-ai-to-the-masses), represent another wave of AI agents inspired by large language models like GPT-4 that can automatically complete multiple tasks on their own. Creative Content Generation Large language models are typically based on the [transformer model architecture](https://arxiv.org/abs/1706.03762) and are usually trained to predict the next word or sequence of words using a [masked language model training objective](https://huggingface.co/docs/transformers/main/tasks/masked_language_modeling). Given a specific input or prompt, models can generate multiple sentences and paragraphs of text that are consistent with the user input. This has significantly impacted the field of writing and [creative content generation](https://zapier.com/blog/ai-content-creation). How Agentic AI Pushes the Limits of AI Conventional AI is rooted in domain-specific applications with limited ability to transfer their learnings from one task or domain to another. AI models that are adept at recognizing objects in images are not as capable in other domains like speech or language. To achieve true humanlike intelligence, AI needs to be adaptable across several tasks and domains. AI agents are a promising step in this direction as they combine perception, decision-making and self-learning abilities in dynamic environments (*eg* autonomous vehicles driving in the real world). While conventional AI may power some of the underlying capabilities, such as image segmentation and object detection, these capabilities cannot help the vehicle drive safely in real-world environments. This is where agentic AI shines. Limitations of Conventional AI Unlike agents that continually learn from interactions with their environment, conventional AI remains static. Conventional AI also struggles to operate effectively across various domains. In the following sections, you'll take a deeper look at some of the shortfalls of conventional AI and how agentic AI overcomes these challenges. Limited Learning Capacity Conventional AI models learn from data, and the knowledge that they learn during model training is confined to the specific data set and the domain. While some models are capable of doing well on related tasks with [transfer learning](https://en.wikipedia.org/wiki/Transfer_learning), the transferred knowledge is usually restricted to the same domain. For instance, models that are trained to identify sentiment in text are not capable of detecting sentiment in voices. This limited learning capacity of conventional AI models is a bottleneck in the development of humanlike intelligence. Meanwhile, agentic AI is endowed with self-learning capacity. Agents actively interact with their environment and constantly update their knowledge and capabilities. Narrow Task Focus Related to their limited learning capacity, another shortcoming of conventional AI is the narrow task or domain in which they excel. While some AI models are accurate when it comes to specific tasks and domains, they tend to significantly underperform when the nature of the data or the task changes. In comparison, AI agents can learn new tasks as needed. This makes them more versatile and adaptable than conventional AI models, which operate well in narrow task domains. Difficulty Handling Ambiguity Conventional AI also struggles to operate in ambiguous situations. For instance, a speech recognition model that is trained in American English may struggle to recognize British English accents. The difference in distribution between the data in the training set and the real-world test set evolves over time, and this results in the models being unable to handle novel and ambiguous inputs. AI agents are adept at handling ambiguity as they constantly sense their environment for changes and learn to respond to these changes. This feedback-based learning helps them adapt to novel or ambiguous features in their environment and be more robust and reliable than conventional AI models. High Maintenance and Rigidity Another downside of conventional AI is its high requirements for maintenance and the rigidity of its performance. Conventional AI models need to be constantly monitored, updated and retrained to better reflect the real-world data, or they'll struggle from [data drift](https://www.openlayer.com/blog/post/surefire-ways-to-identify-data-drift) and poor performance. The models can also be rigid in the sense that they can perform very well only in highly specific conditions and may struggle with changing environments. AI agents do not have substantial requirements for high levels of maintenance and monitoring as they're capable of monitoring their own responses to adapt to their environment. As long as the goals of the agents remain the same, agents do not need to be constantly updated, unlike conventional AI models, which require periodic retraining. Future Prospects and Possibilities of Agentic AI Agentic AI has a [bright future](https://ts2.space/en/the-rise-of-ai-agents-the-future-of-artificial-intelligence) that's bolstered by the breakneck growth and development in the field of large language models and [generative AI](https://www.mckinsey.com/featured-insights/mckinsey-explainers/whats-the-future-of-generative-ai-an-early-view-in-15-charts). Software-based agents built on large language models have boosted the efficacy of conversational intelligence, knowledge search and management, content generation and other business applications. Hardware-based agents, such as robots, also stand to benefit from agentic AI. Agentic AI can empower several fundamental aspects of robotics, such as design, generation of realistic simulations of robotic systems as well as human-robot interaction. According to [Gartner](https://www.gartner.com/en/topics/generative-ai), up to 40 percent of enterprise applications will have embedded conversational AI by 2024, and up to 100 million workers will engage agents to assist in their work by 2026. Conclusion Artificial intelligence is now ubiquitous, powering multiple consumer and enterprise applications across industries. Agentic AI represents a type of AI that can act and make decisions to accomplish specific goals in real-world environments. While robots as a form of agentic AI have been around for a while, recent advances in conventional AI based on large vision and language models are paving the way for a new breed of AI agents. In this article, you learned about the elements of agentic AI, how it's different from conventional AI, what its real-world applications are as well as what the future prospects and potential of agentic AI are. In particular, conversational agents like ChatGPT are increasingly being adopted for various use cases. These chat agents leverage a different style of technology stack that includes new elements, such as [vector databases](https://www.singlestore.com/built-in-vector-database/) that are used for storing smaller or compressed pieces of information and retrieving relevant information chunks based on a [user query in chat applications]. |
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