future of data and ai

Have you ever wondered what possibilities agentic AI systems will unlock as they evolve into true collaborators in work and innovation? It opens up a world where AI does not just follow instructions. It thinks, plans, remembers, and adapts – just like a human would.

With the rise of agentic AI, machines are beginning to bridge the gap between reactive tools and autonomous collaborators. That is the driving force behind the Future of Data and AI: Agentic AI Conference 2025.

This event gathers leading experts to explore the key innovations fueling this shift. From building flexible, memory-driven agents to designing trustworthy, context-aware AI systems, the conference dives deep into the foundational elements shaping the next era of intelligent technology.

 

 

In this blog, we’ll give you an inside look at the major panels, the core topics each will cover, and the groundbreaking expertise you can expect. Whether you’re just starting to explore what are AI agents or you are building the next generation of intelligent systems, these discussions will offer insights you won’t want to miss.

Ready to see how AI is evolving into something truly remarkable? Register now and be part of the conversation that’s defining the future!

Panel 1: Inside the Mind of an AI Agent

Agentic Frameworks, Planning, Memory, and Tools

Speakers: Luis Serrano, Zain Hasan, Kartik Talamadupula

This panel discussion marks the start of the conference and dives deep into the foundational components that make today’s agentic AI systems functional, powerful, and adaptable. At the heart of this discussion is a closer look at how these agents are built, from their internal architecture to how they plan, remember, and interact with tools in the real world.

 

 

1. Agentic Frameworks

We begin with architectures, the structural blueprints that define how an AI agent operates. Modern agentic frameworks like ReAct, Reflexion, and AutoGPT-inspired agents are designed with modularity in mind, enabling different parts of the agent to work independently yet cohesively.

These systems do not just respond to prompts; they evaluate, revise, and reflect on their actions, often using past experiences to guide current decisions. But to solve more complex, multi-step problems, agents need structure. That’s where hierarchical and recursive designs come into play.

Hierarchical frameworks allow agents to break down large goals into smaller, manageable tasks, similar to how a manager might assign sub-tasks to a team. Recursive models add another layer of sophistication by allowing agents to revisit and refine previous steps, making them better equipped to handle dynamic or evolving objectives.

 

You can learn more about what agentic AI is

 

2. Planning and Reasoning

Planning and reasoning are also essential capabilities in agentic AI. The panel will explore how agents leverage tools like PDDL (Planning Domain Definition Language), a symbolic planning language that helps agents define and pursue specific goals with precision.

You will also hear about chain-of-thought prompting, which guides agents to reason step-by-step before arriving at an answer. This makes their decisions more transparent and logical. Combined with tool integration, such as calling APIs, accessing code libraries, or querying databases, these techniques enhance an agent’s ability to solve real-world problems.

3. Memory

Memory is another key piece of the puzzle. Just like humans rely on short-term and long-term memory, agents need ways to store and recall information. The panel will unpack strategies like:

• episodic memory, which stores specific events or interactions
• semantic memory, that is, general knowledge
• vector-based memory, which helps retrieve relevant information quickly based on context

You will also learn how these memory systems support adaptive learning, allowing agents to grow smarter over time by refining what they store and how they use it, often compressing older data to make room for newer, more relevant insights.

 

 

Together, these components – architecture, planning, memory, and tool use – form the driving force behind today’s most advanced AI agents. This session will offer both a technical roadmap and a conceptual framework for anyone looking to understand or build intelligent systems that think, learn, and act with purpose.

Panel 2: From Recall to Context-Aware Reasoning

Architecting Retrieval Systems for Agentic AI

Speakers: Raja Iqbal, Bob Van Luijt, Jerry Liu

Intelligent behavior in both humans and AI is marked by memory playing a central role. In agentic AI, memory is more than just about storing data. It is about retrieving the right information at the right time to make informed decisions.

This panel takes you straight into the core of these memory systems, focusing on retrieval mechanisms, from static and dynamic vector stores to context-aware reasoning engines that help agents act with purpose and adaptivity.

1. Key Themes

At the center of this conversation is how agentic AI uses episodic and semantic memory.

• Episodic memory allows an agent to recall specific past interactions or events, like remembering the steps it took to complete a task last week.
• Semantic memory is more like general knowledge, helping an agent understand broader concepts or facts that it has learned over time.

These two memory types work together to help agents make smarter, more context-aware decisions. However, these strategies are only focused on storing data, while agentic systems also need to retrieve relevant memories and integrate them into their planning process.

The panel explores how this retrieval is embedded directly into an agent’s reasoning and action loops. For example, an AI agent solving a new problem might first query its vector database for similar tasks it has encountered before, then use that context to shape its strategy moving forward.

 

 

2. Real-World Insights to Understand What are AI Agents

The conversation will also dive into practical techniques for managing memory, such as pruning irrelevant or outdated information and using compression to reduce storage overhead while retaining useful patterns. These methods help agents stay efficient and scalable, especially as their experience grows.

You can also expect insights into how retrievers themselves can be fine-tuned based on agent behavior. By learning what kinds of information are most useful in different contexts, agents can evolve to retrieve smartly.

The panel will also spotlight real-world use cases of Retrieval-Augmented Generation (RAG) in agentic systems, where retrieval directly enhances the agent’s ability to generate accurate, relevant outputs across tasks and domains. Hence, this session offers a detailed look at how intelligent agents remember, reason, and act with growing sophistication.

 

Here’s a guide to learn about retrieval augmented generation (RAG)

 

Panel 3: Designing Trustworthy Agents

Observability, Guardrails, and Evaluation in Agentic Systems

Speakers: Aparna Dhinakaran, Sage Elliot

This final panel tackles one of the most pressing questions in the development of agentic AI: How can we ensure that these systems are not only powerful but also safe, transparent, and reliable? As AI agents grow more autonomous, their decisions impact real-world outcomes. Hence, trust and accountability are just as important as intelligence and adaptability.

1. Observability

The conversation begins with a deep dive into observability, that is, how we “see inside” an AI agent’s mind. Developers need visibility into how agents make decisions. Tools that trace decision paths and log internal states offer crucial insights into what the agent is thinking and why it acted a certain way.

While these insights are useful for debugging, they serve a greater purpose. They build the reliability of these agentic systems, enabling users to operate them confidently in high-stake environments.

 

Here’s what you need to know about LLM observability and monitoring

 

2. Guardrails

Next, the panel will explore behavioral guardrails for agentic AI systems. These are mechanisms that keep AI agents within safe and expected boundaries, ensuring the agents operate in a way that is ethically acceptable.

Whether it is a healthcare agent triaging patients or an enterprise chatbot handling sensitive data, agents must be able to follow rules, reject harmful instructions, and recover gracefully from mistakes. Setting these constraints up front and continuously updating them is key to responsible deployment.

3. Evaluation

However, a bunch of rules and constant monitoring is not the only solution. You need an evaluation strategy for your agentic systems to ensure their reliability and practical use. The panelists will shed light on best practices of evaluation, like:

• Simulation-based testing, where agents are placed in controlled, complex environments to see how they behave under different scenarios
• Agent-specific benchmarks, which are designed to measure how well an agent is performing beyond just accuracy or completion rates

While these are some evaluation methods, the goal is to find the answer to important questions during the process. These questions can be like: Are the agent’s decisions explainable? Does it improve with feedback? These are the kinds of deeper questions that effective evaluation must answer.

The most important part is, you will also get to learn from our experts as they share their lessons from real-world deployments. They will reflect on what it takes to scale trustworthy agentic AI systems without compromising performance.

 

You can also explore LLM evaluation in detail

 

Ranging from practical trade-offs and what works in production, to how organizations are navigating the complex balance between oversight and autonomy. For developers, product leads, and AI researchers, this session offers actionable insights into building agents that are credible, safe, and ready for the real world.

The Future of AI Is Agentic – Are You Ready?

As we move into an era where AI systems are not just tools but thinking partners, the ideas explored in these panels offer a clear signal: agentic AI is no longer a distant concept, but is already shaping how we work, innovate, and solve problems.

The topics of discussion at the Agentic AI Conference 2025 show what is possible when AI starts to think, plan, and adapt with intent. Whether you are just learning what an AI agent is or you are deep into developing the next generation of intelligent systems, this conference is your front-row seat to the future.

Don’t miss your chance to be part of this pivotal moment in AI evolution and register now to join the conversation of defining what’s next!

Artificial Intelligence (AI), Machine Learning (ML), and data science have become some of the most significant topics of discussion in today’s technological era.

In one of the speakers’ sessions on the ‘Future of Data and AI‘, several experts in these fields came together to discuss the latest advancements, how they are using them in their everyday work, and the connection of AI and society.

Introduction of Panelists

The session starts with Hamza, a research science manager at Google, introducing himself and explaining how he runs a few ML models and helps build models that can predict user abuse. Hamza works in the trust and safety group within search, where they prioritize the protection of users.

Generative AI: Trends, Ethics and Societal Impact – Watch the complete session  

The other experts introduce themselves as well. Batool, who has experience working as an AI scientist at Amazon, focused on dialogue machines and natural language understanding.

Meanwhile, Francesca, a Principal Data Scientist manager at Microsoft, leads teams of data scientists and ML scientists, working on internal problems at Microsoft. Raja, the Founder, and Chief Data Scientist at Data Science Dojo has been working in data science before it was even called data science.

Use of Generative AI

The conversation then shifts to the use of generative AI, which has been used in the field of data science and ML for a while. Francesca explains that there are three main categories where generative AI is used every day in her work.

The first is generating natural language, which includes summarization, translation, and question-answering systems. The second is an image and video generation, which has applications in industries like gaming and advertising. The third is generating music, which can be used for composing, arranging, and creating personalized music.

A Deeper Understanding of the Current State of the Field

The experts then discuss the latest advancements in these fields. Raja emphasizes the importance of the latest advancements in deep learning, specifically transformers, in NLP tasks. He also mentions the development of large-scale language models like GPT-3, which can perform tasks like translation, summarization, and question-answering.

Matul discusses how chatbots have evolved from rule-based systems to data-driven systems, where they can use data to train and improve their performance. This includes using natural language processing to understand and respond to user queries more effectively.

Francesca highlights the importance of democratizing AI and making it accessible to all people, regardless of their technical background. This involves developing user-friendly tools that can be used by people without technical expertise, which can be used to address common business problems.

Generative AI – The Impact of Ground-Breaking Generative AI Technologies

Open AI has brought about a major transformation in the field of artificial intelligence (AI), data science, and machine learning. One of the most significant contributions of open AI is its generative AI capabilities that help in generating code, images, and troubleshooting bugs. These capabilities are particularly useful for data scientists who need to deploy and operationalize their machine-learning applications.

Generating code from one programming language to another is one of the three main categories where generative AI applications have been seeing a lot of demand. Another popular application of generative AI is in generating images, especially for use cases such as generating images from text descriptions.

For data scientists like the speaker, who work mostly in the AI, data science, and machine learning space, most of their work is done on the cloud. With open AI, data scientists can now access pre-trained generative AI models and customize them with their data. They can also use built-in tools to detect and mitigate any biases or unfair dynamics that may exist in their applications.

Open AI has made accessing these tools easier through the open AI studio, where one can build AI models and deploy them faster. The speaker has found this to be a privileged situation and has been using generative AI for various communication purposes such as spot-checking, rephrasing, and creating snippets for social media posts.

 

 

Human Intelligence in Conjunction with AI

While AI has brought about a significant change in the field of content creation, the speaker warns against relying solely on AI. Human intelligence should be used in conjunction with AI to create the best results. AI is just another tool that should be used with caution, as a few wrong jumps can take you in the wrong direction.

The other speakers in the panel discussion also shared their experiences with generative AI. One of them is writing a book that covers popular machine learning algorithms using fiction. While, until a few years back, his biggest concern was hiring graphic designers and concept artists, now, with generative AI, he can create his book’s graphics on his own.

Generative AI’s Impact on Creative Work

Generative AI is impacting creative work and work in general in many ways. In creative industries, such as marketing, graphic design, animation, and content creation, generative AI is a valuable tool that allows for faster and more efficient production of high-quality content. It can also democratize access to expensive resources like models for photo shoots, making them more accessible to smaller designers.

In other industries, such as manufacturing, healthcare, and energy, generative AI can also be used to improve efficiency and productivity. For example, it can be used to design new products, optimize manufacturing processes, and analyze medical images.

Overall, generative AI has the potential to impact work across many different industries, and its adoption is likely to continue to grow as more businesses discover its benefits. While it may not eliminate jobs, it will likely change the nature of work in many industries, requiring workers to learn new skills to work effectively with these tools.

 

Read about 12 must-have AI tools to revolutionize your work 

 

Francesca, emphasizes the importance of considering the ethical implications of working with AI systems, not just generative AI. She has a checklist of principles that she follows, such as fairness, reliability, safety, privacy and security, inclusiveness, accountability, and transparency, which are industry standards developed by tech companies.

While principles are essential to keep in mind, Francesco also suggests that tools such as interpretML and fair AI can be leveraged to understand the impact of data on predictions and results better.

OpenAI and generative AI have many benefits, such as improving content quality, variety, and personalization. However, to ensure these benefits follow ethical principles, the model life cycle, which starts with data collection, pre-processing, model building, and tuning model parameters and ends with prediction and interpretation, must involve humans in all stages.

 

 

Generative AI in Healthcare and Energy

AI in Healthcare

There are many exciting ways that generative AI is being used to tackle important problems in the fields of healthcare and energy. One area where generative AI is being used in healthcare is in the creation of medical images such as X-rays and MRIs. With the help of generative AI, researchers can generate high-quality medical images that can help in the diagnosis and treatment of various medical conditions.

It is also being used to develop new drugs and treatments. With the help of deep learning algorithms, researchers can analyze large amounts of medical data to identify new drug candidates and develop personalized treatment plans for patients.

In the field of energy, generative AI is being used to optimize energy systems and reduce energy consumption. For example, AI models can be trained to predict energy usage patterns and adjust energy supply, accordingly, reducing waste and increasing efficiency.

Another area where generative AI is being used is in the creation of virtual environments for training purposes. With the help of generative AI, researchers can create realistic virtual environments that can be used to train individuals in various fields such as medicine, engineering, and military training. This can help to reduce the risk of accidents and injuries during training and improve overall safety.

Generative AI and Government Regulations

Overall, the role of the government in regulating the use of generative AI to create content is a highly debated topic. Some believe that the government should intervene to prevent monopolies from happening and to fund open-source projects to democratize data. Others argue that too much regulation could stifle innovation and competition.

It is essential to strike a balance between promoting innovation and protecting consumers’ interests. Legislation and regulations could be created to define what constitutes fair use and set standards for the ethical use of AI, such as the AI bill of rights. Ultimately, governments will act following the general culture and society’s values in their region, making laws that align with what is considered acceptable.

Closing of the Session – Generative AI and Society

In conclusion, AI, ML, and data science have become vital to our daily lives, with advancements in these fields impacting various industries. With the continuous development of new technology, it is essential to keep up to date with the latest trends and advancements to stay competitive in the industry. The experts in the session provided valuable insights into the latest advancements and how they are using them in their everyday work. As these fields continue to evolve, it will be exciting to see what new advancements will come next.