Agent AI In-Depth Tutorial | Slides AGENT AI 1 AGENT AI 2 AGENT AI 3 AGENT AI 4 AGENT AI 5 AGENT AI 6 AGENT AI 7 AGENT AI 8 AGENT AI 9 AGENT AI 10 AGENT AI 11 AGENT AI 12 AGENT AI 13 AGENT AI 14 AGENT AI 15 AGENT AI 16 AGENT AI 17 AGENT AI 18 AGENT AI 19 AGENT AI 20 AGENT AI 21 AGENT AI 22 AGENT AI 23 AGENT AI 24 Tutorial on Agent AI Agent AI refers to systems that operate autonomously or semi-autonomously to complete tasks based on user inputs or goals, often leveraging artificial intelligence (AI) techniques such as machine learning (ML), natural language processing (NLP), computer vision, and other decision-making algorithms. These agents can range from simple rule-based systems to complex AI models capable of learning and adapting to new environments or data.Key Features of Agent AI:Autonomy: Agent AIs are designed to make decisions and perform actions without constant human intervention. They can handle tasks ranging from simple automation (e.g., scheduling emails) to complex decision-making (e.g., financial trading algorithms).Learning Capabilities: Through machine learning, agent AIs can learn from data, user interactions, or past experiences. They adapt their behavior based on new information, improving their performance over time.Goal-Oriented Behavior: Agent AIs work towards achieving specific goals defined by users or inferred from data. This could include finding optimal solutions (like routing in logistics), achieving business outcomes (like increasing sales through personalization), or assisting in decision-making.Interaction with the Environment: Agents perceive and interact with their environment. For example, in robotics, an AI agent might interact with the physical world (navigating obstacles), while a virtual assistant AI interacts with digital environments like websites or apps.Reactivity and Proactiveness: Some agents are reactive, meaning they respond to changes or events in the environment (e.g., stock trading bots reacting to market changes). Proactive agents can anticipate needs or potential problems, such as predictive maintenance systems identifying issues before they occur.Multi-Agent Systems: Agents can be part of a larger system where multiple agents work together, either collaborating or competing, to achieve their individual or collective goals. This is common in distributed AI systems like autonomous drones or smart grid systems.Personalization: Many AI agents offer personalized interactions based on user preferences or history. Examples include recommendation systems (like Netflix or Spotify) or personal shopping assistants.Communication: Advanced agent AIs often communicate using natural language, interpreting human inputs and generating meaningful responses. NLP allows them to interact with users in a conversational manner, which is common in virtual assistants like Siri or Alexa.Types of AI Agents:Reactive Agents: These agents make decisions based solely on the current state of the environment. They don’t store past data or plan for the future. Simple task automation tools can be considered reactive agents.Deliberative Agents: These have a model of the world and can plan by predicting the outcomes of their actions. They often use reasoning to make decisions based on goals.Hybrid Agents: Combining reactive and deliberative characteristics, these agents use both immediate responses and long-term planning to achieve goals.Utility-Based Agents: These agents evaluate actions based on a utility function, choosing the one that maximizes their performance. They are common in game AI or financial systems.Learning Agents: These can adjust their behavior based on past experience. Reinforcement learning agents, for example, learn to maximize a reward signal over time.Applications of Agent AI:Virtual Assistants: AI agents like Google Assistant, Alexa, and Siri help users perform tasks like setting reminders, answering questions, and controlling smart home devices.Autonomous Vehicles: AI agents in self-driving cars make decisions regarding navigation, obstacle avoidance, and traffic management.Financial Trading: Autonomous agents in stock trading analyze market data and execute trades based on pre-programmed or learned strategies.Healthcare: AI agents assist in diagnostics, treatment planning, and patient monitoring. They can recommend treatment options or alert medical professionals to anomalies.Customer Support: AI chatbots act as agents in helping customers with queries, resolving issues, and providing personalized assistance.Gaming: AI agents control non-player characters (NPCs) that can adapt their strategies and actions based on the player’s behavior.Robotics: AI agents in robots allow them to autonomously navigate environments, manipulate objects, and perform specific tasks (e.g., cleaning robots, manufacturing robots).Agent AI continues to grow, integrating advanced machine learning models, more sophisticated reasoning capabilities, and adaptive behaviors that make them suitable for complex, dynamic environments.Benefits of Using Agent AIAutomation and Efficiency:Task Automation: AI agents can automate repetitive and time-consuming tasks, such as data entry, customer support, and scheduling. This leads to increased productivity by freeing up human resources for more complex tasks.24/7 Operation: Unlike humans, AI agents can work continuously without fatigue, ensuring round-the-clock operations in customer service, monitoring systems, or financial markets.Personalization:Tailored Experiences: Agent AI can analyze user preferences and behaviors to offer highly personalized experiences. Examples include personalized shopping recommendations, custom financial advice, or healthcare suggestions.Real-Time Adaptation: These agents can adjust recommendations or interactions based on real-time feedback, creating dynamic and tailored experiences for each user.Scalability:Handling Large Volumes: AI agents can handle massive volumes of tasks or requests simultaneously. For example, customer service bots can engage with thousands of users at once, something impossible with human operators alone.Efficient Resource Allocation: AI agents can optimize processes, allocate resources efficiently, and streamline operations in various industries like logistics, healthcare, and manufacturing.Cost Reduction:Reduced Human Intervention: By automating repetitive tasks and decision-making processes, AI agents reduce the need for manual labor, leading to significant cost savings over time.Lower Error Rates: AI agents can minimize human error in processes like financial transactions, data processing, and decision-making, reducing costly mistakes.Improved Decision Making:Data-Driven Insights: AI agents can analyze vast amounts of data, uncovering patterns and insights that humans might miss. This capability enhances decision-making in areas like investment, marketing, and operations.Predictive Capabilities: AI agents use predictive models to forecast outcomes, allowing businesses to anticipate market changes, customer behavior, and potential issues before they occur.Real-Time Processing:Instant Responses: AI agents can process and respond to events in real-time, making them valuable in high-speed environments like algorithmic trading, cybersecurity, and IoT-based systems.Immediate Problem Solving: In areas like predictive maintenance, AI agents can immediately identify issues and recommend fixes before failures occur, minimizing downtime.Adaptability and Learning:Continuous Improvement: Learning agents improve over time by learning from data, feedback, and interactions, making them more effective and capable of handling new challenges.Flexible Applications: Agent AI can be applied in diverse fields—from robotics to virtual assistants to autonomous vehicles—adapting to different environments and tasks.Enhanced Customer Service:Faster Response Times: Chatbots and virtual assistants powered by AI agents can quickly handle common customer inquiries, reducing wait times and improving the overall customer experience.Consistent Quality: AI agents ensure consistent service quality by providing accurate and relevant information every time without the variability of human agents.Challenges of Using Agent AIComplexity in Development:High Skill Requirements: Developing robust AI agents often requires advanced technical expertise in AI/ML, data science, and software engineering. Building agents that function autonomously and make sound decisions can be difficult and resource-intensive.Integration with Existing Systems: Incorporating AI agents into existing systems, especially in legacy environments, can be challenging. This involves ensuring smooth interoperability between the AI agents and the organization’s existing technology stack.Bias and Fairness:Biased Decision Making: AI agents, especially those built using machine learning, can inherit biases from training data. This could result in biased decisions or recommendations in areas like hiring, lending, or law enforcement, leading to ethical and legal concerns.Lack of Transparency: Many AI agents operate as black boxes, meaning it’s difficult to understand how they make decisions. This opacity can be problematic, especially in critical fields like healthcare, finance, and criminal justice.Data Requirements:Data Dependence: AI agents often require large amounts of data to function effectively. Acquiring, cleaning, and maintaining quality data can be costly and challenging, particularly for organizations that lack the infrastructure for data management.Privacy Concerns: Since AI agents rely heavily on user data, privacy concerns arise, particularly in how this data is stored, processed, and used. Protecting user data from misuse and ensuring compliance with regulations (like GDPR or CCPA) is essential.Security Risks:Vulnerability to Attacks: AI agents, especially those connected to the internet, can be vulnerable to cyberattacks, including data breaches, adversarial attacks, or model manipulation. Malicious actors can exploit these vulnerabilities to compromise sensitive information or manipulate decisions.Misuse or Malfunction: Misconfigured or poorly designed AI agents could act in unintended or harmful ways. For instance, an AI-driven autonomous vehicle might make the wrong decision due to a sensor malfunction or flawed algorithm, leading to accidents.Ethical Concerns:Autonomy vs. Accountability: As AI agents gain more autonomy, it raises questions about accountability when things go wrong. Who is responsible for the actions of an AI agent? This is particularly concerning in high-risk applications like autonomous weapons, healthcare, or financial trading.Job Displacement: Automation by AI agents could displace jobs, particularly those involving routine tasks. The societal and economic implications of widespread job displacement due to AI adoption are complex and require careful planning and intervention.Cost and Resources:High Initial Costs: Although AI agents can reduce long-term costs, developing and implementing them can be expensive. Building advanced AI systems may require significant investment in infrastructure, computational resources, and specialized talent.Ongoing Maintenance: AI agents require regular updates, retraining, and maintenance to keep them effective. Without proper maintenance, their performance could degrade over time, leading to inefficiencies or inaccurate decision-making.Generalization and Adaptability:Narrow Focus: Many AI agents are highly specialized, performing well within a limited scope of tasks but failing when faced with novel or unexpected situations. For example, a customer service chatbot might struggle to handle complex, nuanced queries beyond its predefined capabilities.Transfer Learning Limitations: While some AI agents can learn and adapt, generalizing knowledge from one domain to another remains challenging. Agents built for one task might not be easily repurposed for another without significant retraining or redesign.Regulation and Compliance:Evolving Legal Frameworks: As governments begin to regulate AI, compliance becomes increasingly complex. Navigating these regulations and ensuring that AI agents adhere to legal standards (e.g., in data protection or automated decision-making) can be challenging, especially across different jurisdictions.Lack of Standardization: AI agents often lack standardized protocols, which can lead to incompatibility across different systems and platforms. This fragmentation complicates development and integration efforts.In summary, while AI agents provide numerous advantages such as automation, personalization, and scalability, they also come with significant challenges, including data privacy, security risks, bias, and the need for substantial resources. Addressing these challenges is crucial for the responsible and effective deployment of AI agents across industries. 10 Use Cases Built AI Agent Tutorial Build Dataproducts AI Agent for Business Analysis KreateHub Build Budget Plan for GenAI What is KREATE and KreatePro What is KONTROLS What is KNOBS Kreate Articles Kreate Slides Kreate Websites Kreate AI Assistants Create AI Agent Develop data products with KREATE and AB Experiment Innovate with experiments RAG For Unstructred and Structred Data Why knobs matter Setup chatbots in minutes LLM and AI based Website generation CMS GenAI Effortless slides creation Your story writing is automated Hire expertise without commitment Agent AI refers to systems that operate autonomously or semi-autonomously to complete tasks based on user inputs or goals, often leveraging artificial intelligence (AI) techniques such as machine learning (ML), natural language processing (NLP), computer vision, and other decision-making algorithms. These agents can range from simple rule-based systems to complex AI models capable of learning and adapting to new environments or data. Autonomy: Agent AIs are designed to make decisions and perform actions without constant human intervention. They can handle tasks ranging from simple automation (e.g., scheduling emails) to complex decision-making (e.g., financial trading algorithms). Learning Capabilities: Through machine learning, agent AIs can learn from data, user interactions, or past experiences. They adapt their behavior based on new information, improving their performance over time. Goal-Oriented Behavior: Agent AIs work towards achieving specific goals defined by users or inferred from data. This could include finding optimal solutions (like routing in logistics), achieving business outcomes (like increasing sales through personalization), or assisting in decision-making. Interaction with the Environment: Agents perceive and interact with their environment. For example, in robotics, an AI agent might interact with the physical world (navigating obstacles), while a virtual assistant AI interacts with digital environments like websites or apps. Reactivity and Proactiveness: Some agents are reactive, meaning they respond to changes or events in the environment (e.g., stock trading bots reacting to market changes). Proactive agents can anticipate needs or potential problems, such as predictive maintenance systems identifying issues before they occur. Multi-Agent Systems: Agents can be part of a larger system where multiple agents work together, either collaborating or competing, to achieve their individual or collective goals. This is common in distributed AI systems like autonomous drones or smart grid systems. Personalization: Many AI agents offer personalized interactions based on user preferences or history. Examples include recommendation systems (like Netflix or Spotify) or personal shopping assistants. Communication: Advanced agent AIs often communicate using natural language, interpreting human inputs and generating meaningful responses. NLP allows them to interact with users in a conversational manner, which is common in virtual assistants like Siri or Alexa. Reactive Agents: These agents make decisions based solely on the current state of the environment. They don’t store past data or plan for the future. Simple task automation tools can be considered reactive agents. Deliberative Agents: These have a model of the world and can plan by predicting the outcomes of their actions. They often use reasoning to make decisions based on goals. Hybrid Agents: Combining reactive and deliberative characteristics, these agents use both immediate responses and long-term planning to achieve goals. Utility-Based Agents: These agents evaluate actions based on a utility function, choosing the one that maximizes their performance. They are common in game AI or financial systems. Learning Agents: These can adjust their behavior based on past experience. Reinforcement learning agents, for example, learn to maximize a reward signal over time. Virtual Assistants: AI agents like Google Assistant, Alexa, and Siri help users perform tasks like setting reminders, answering questions, and controlling smart home devices. Autonomous Vehicles: AI agents in self-driving cars make decisions regarding navigation, obstacle avoidance, and traffic management. Financial Trading: Autonomous agents in stock trading analyze market data and execute trades based on pre-programmed or learned strategies. Healthcare: AI agents assist in diagnostics, treatment planning, and patient monitoring. They can recommend treatment options or alert medical professionals to anomalies. Customer Support: AI chatbots act as agents in helping customers with queries, resolving issues, and providing personalized assistance. Gaming: AI agents control non-player characters (NPCs) that can adapt their strategies and actions based on the player’s behavior. Robotics: AI agents in robots allow them to autonomously navigate environments, manipulate objects, and perform specific tasks (e.g., cleaning robots, manufacturing robots). Agent AI continues to grow, integrating advanced machine learning models, more sophisticated reasoning capabilities, and adaptive behaviors that make them suitable for complex, dynamic environments. 24/7 Operation: Unlike humans, AI agents can work continuously without fatigue, ensuring round-the-clock operations in customer service, monitoring systems, or financial markets. Personalization: Real-Time Adaptation: These agents can adjust recommendations or interactions based on real-time feedback, creating dynamic and tailored experiences for each user. Scalability: Efficient Resource Allocation: AI agents can optimize processes, allocate resources efficiently, and streamline operations in various industries like logistics, healthcare, and manufacturing. Cost Reduction: Lower Error Rates: AI agents can minimize human error in processes like financial transactions, data processing, and decision-making, reducing costly mistakes. Improved Decision Making: Predictive Capabilities: AI agents use predictive models to forecast outcomes, allowing businesses to anticipate market changes, customer behavior, and potential issues before they occur. Real-Time Processing: Immediate Problem Solving: In areas like predictive maintenance, AI agents can immediately identify issues and recommend fixes before failures occur, minimizing downtime. Adaptability and Learning: Flexible Applications: Agent AI can be applied in diverse fields—from robotics to virtual assistants to autonomous vehicles—adapting to different environments and tasks. Enhanced Customer Service: Integration with Existing Systems: Incorporating AI agents into existing systems, especially in legacy environments, can be challenging. This involves ensuring smooth interoperability between the AI agents and the organization’s existing technology stack. Bias and Fairness: Lack of Transparency: Many AI agents operate as black boxes, meaning it’s difficult to understand how they make decisions. This opacity can be problematic, especially in critical fields like healthcare, finance, and criminal justice. Data Requirements: Privacy Concerns: Since AI agents rely heavily on user data, privacy concerns arise, particularly in how this data is stored, processed, and used. Protecting user data from misuse and ensuring compliance with regulations (like GDPR or CCPA) is essential. Security Risks: Misuse or Malfunction: Misconfigured or poorly designed AI agents could act in unintended or harmful ways. For instance, an AI-driven autonomous vehicle might make the wrong decision due to a sensor malfunction or flawed algorithm, leading to accidents. Ethical Concerns: Job Displacement: Automation by AI agents could displace jobs, particularly those involving routine tasks. The societal and economic implications of widespread job displacement due to AI adoption are complex and require careful planning and intervention. Cost and Resources: Ongoing Maintenance: AI agents require regular updates, retraining, and maintenance to keep them effective. Without proper maintenance, their performance could degrade over time, leading to inefficiencies or inaccurate decision-making. Generalization and Adaptability: Transfer Learning Limitations: While some AI agents can learn and adapt, generalizing knowledge from one domain to another remains challenging. Agents built for one task might not be easily repurposed for another without significant retraining or redesign. Regulation and Compliance: In summary, while AI agents provide numerous advantages such as automation, personalization, and scalability, they also come with significant challenges, including data privacy, security risks, bias, and the need for substantial resources. Addressing these challenges is crucial for the responsible and effective deployment of AI agents across industries. Dataknobs has developed a wide range of products and solutions powered by Generative AI (GenAI), Agent AI, and traditional AI to address diverse industry needs. These solutions span finance, healthcare, real estate, e-commerce, and more. Click on to see in-depth look at these use cases - Stocks Earning Call Analysis, Ecommerce Analysis with GenAI, Financial Planner AI Assistant, Kreatebots, Kreate Websites, Kreate CMS, Travel Agent Website, Real Estate Agent etc. Here are slides and AI Agent Tutorial. Agentic AI refers to AI systems that can autonomously perceive, reason, and take actions to achieve specific goals without constant human intervention. These AI agents use techniques like reinforcement learning, planning, and memory to adapt and make decisions in dynamic environments. They are commonly used in automation, robotics, virtual assistants, and decision-making systems. Building data products using Generative AI (GenAI) and Agentic AI enhances automation, intelligence, and adaptability in data-driven applications. GenAI can generate structured and unstructured data, automate content creation, enrich datasets, and synthesize insights from large volumes of information. This helps in scenarios such as automated report generation, anomaly detection, and predictive modeling. DataKnobs has built an AI Agent for structured data analysis that extracts meaningful insights from diverse datasets such as e-commerce metrics, sales/revenue reports, and sports scorecards. The agent ingests structured data from sources like CSV files, SQL databases, and APIs, automatically detecting schemas and relationships while standardizing formats. Using statistical analysis, anomaly detection, and AI-driven forecasting, it identifies trends, correlations, and outliers, providing insights such as sales fluctuations, revenue leaks, and performance metrics. At its core, KreateHub is designed to enable creation of new data and the generation of insights from existing datasets. It acts as a bridge between raw data and meaningful outcomes, providing the tools necessary for organizations to experiment, analyze, and optimize their data processes. CIOs and CTOs can apply GenAI in IT Systems. The guide here describe scenarios and solutions for IT system, tech stack, GenAI cost and how to allocate budget. Once CIO and CTO can apply this to IT system, it can be extended for business use cases across company. KREATE empowers you to create things - Dataset, Articles, Presentations, Proposals, Web design, Websites and AI Assistants Kreate is a platform inclide set of tools that ignite your creatviity and revolutionize the way you work. KReatePro is enterprise version. KONTROLS enable adding guardrails, lineage, audit trails and governance. KOntrols recogizes that different use cases for Gen AI and AI have varying levels of control requirements. Kontrols provide structure to select right controls. Well defined tunable paramters for LLM API, LLM fine tuning , Vector DB. These parameters enable faster experimentation and diagosis for every state of GenAI development - chunking, embedding, upsert into vector DB, retrievel, generation and creating responses for AI Asistant. Create articles for Blogs, Websites, Social Media posts. Write set of articles together such as chapters of book, or complete book by giving list of topics and Kreate will generate all articles. Design impactful presentation by giving prmpt. Convert your text and image content into presentations to win customers. Search in your knowledbe base of presentations and create presentations or different industry. Publish these presentation with one click. Generate SEO for public presentations to index and get traffic. AI powered website generation engine. It empower user to refresh website daily. Kreate Website AI agent does work of reading conent, website builder, SEO, create light weight images, create meta data, publish website, submit to search engine, generate sitemap and test websites. Set up AI Assistant that give personized responss to your customers in minutes. Add RAG to AI assistant with minimal code- implement vector DB, create chunks to get contextual answer from your knowlebase. Build quality dataset with us for fine tuning and training a cusom LLM. AI agent independently chooses the best actions it needs to perform to achieve their goals. AI agents make rational decisions based on their perceptions and data to produce optimal performance and results. Here are features of AI Agent, Types and Design patterns As per HBR Data product require validation of both 1. whether algorithm work 2. whether user like it. Builders of data product need to balance between investing in data-building and experimenting. Our product KREATE focus on building dataset and apps , ABExperiment focus on ab testing. Both are designed to meet data product development lifecycle In complex problems you have to run hundreds of experiments. Plurality of method require in machine learning is extremely high. With Dataknobs approach, you can experiment thru knobs. Here are several value propositions for Retrieval-Augmented Generation (RAG) across different contexts: Unstructred Data, Structred Data, Guardrails. See Drivetrain appproach for building data product, AI product. It has 4 steps and levers are key to success. Knobs are abstract mechanism on input that you can control. Redmond, WA USA