In a recent podcast episode with Bill Gurley and Brad Gerstner, Satya Nadella – CEO of Microsoft, discussed a wide range of topics related to his role at Microsoft, the state of the technology, business growth and capitalism in this new “AI Era”.
The podcast which you can watch on YouTube here covered some interesting topics including the Future of AI Agents and their potential to transform how we interact with technology. In this blog (worth a listen), Satya gives his predications/insights into the future of AI Agents and emphasises that AI agents will fundamentally change the landscape of software-as-a-service (SaaS) solutions, predicting that the traditional notion of business applications will collapse in the era of agentic AI.
What is Agentic AI?
Agentic AI refers to artificial intelligence systems that can make decisions and take actions autonomously, without direct human intervention. These systems are designed to perceive their environment, reason about the best course of action, and execute tasks independently. In short these agents are designed to function as workplace teammates, capable of handling various tasks across different applications.
As example, in e-commerce platforms, instead of static, rule based chatbots, agentic AI-driven systems can track a customer’s journey, personalised recommendations, and assist with returns seamlessly without user / supervisor input. These agents will be able to actively learn from interactions, optimising the customer journey in real time and learning about user preferences.
Agentic AI typically inhibits the following features:
- Autonomy: Agentic AI systems can operate independently, making decisions based on input data and predefined goals.
- Adaptability: These systems can adapt to changing circumstances and inputs, adjusting their actions to achieve their objectives.
- Proactivity: Agentic AI can anticipate user needs and take actions without explicit instructions, making them more proactive in their behavior.
- Collaboration: In the future, agentic AI systems will be able to work together in multi-agent networks, collaborating to handle complex tasks that a single agent cannot manage alone.
Changes Ahead for AI Agents
The video / podcast is an hour and a half (but an enjoyable and informative listen). Reading between the sections, Satya talks extensively about where he sees AI Agents evolving massively through 2025. I have summarised this below.
Increasing Sophistication and Capabilities
AI agents will become increasingly sophisticated and capable, eventually replacing traditional software applications. These agents will be able to understand and anticipate user needs, providing personalised and proactive assistance. They will leverage advanced natural language processing (NLP) and machine learning algorithms to interact with users in a more human-like manner.
Autonomous AI Agents
Through 2025. we will see autonomous AI agents handle more and more complex tasks with minimal human oversight, optimising workflows and enhancing efficiency across industries. These agents will streamline workflows, manage intricate operations, and simplify everyday activities. For example, OpenAI’s “Operator Agents” will autonomously execute multi-step processes, such as scheduling meetings or managing projects.
Multi-Agent Networks
Sayta talks abiut the “future” being a place that is not about singular agents but more about networks or systems of agents where agents can discover and collaborate with other agents. These multi-agent networks will enable agents to handle tasks that they can’t do themselves by invoking other agents (agents talking to other agents). This collaborative approach will enhance the overall capabilities and efficiency of AI agents.
Vertical AI Agents
Vertical AI agents, which are specialised for specific industries, are expected to have their moment in 2025. These agents will dominate their respective fields by offering tailored solutions that address industry-specific challenges. For example, retail AI agents will act as personal shoppers, offering personalised recommendations and optimising inventory management.
Persistent Memory and Personalisation
AI systems with persistent memory will enable highly personalized interactions, transforming AI into long-term companions that adapt to user preferences and habits. This capability will allow AI agents to provide more relevant and context-aware assistance, enhancing user experiences.
Emotional Intelligence
Future AI agents are expected to possess emotional intelligence, allowing them to understand and respond to human emotions. This will enable more empathetic and effective interactions, particularly in customer service and healthcare settings.
Integration with IoT and Personal Devices
AI agents will increasingly integrate with the Internet of Things (IoT) and personal devices. This integration will enable seamless interactions across various platforms and devices, creating a more connected and efficient ecosystem. For example, AI agents in smart homes will manage household tasks, monitor energy usage, and provide personalized recommendations.
Ethical AI and Transparency
As AI agents become more prevalent, there will be a greater emphasis on ethical AI and transparency in decision-making. Ensuring that AI agents operate responsibly and transparently will be crucial for gaining user trust and acceptance. This includes addressing issues related to data privacy, bias, and accountability.
Proactive AI Agents
Proactive AI agents will anticipate user needs and take actions without explicit instructions. For example, an AI assistant might reorganize your day based on traffic updates and weather, reschedule missed appointments, and even draft personalized messages. This proactive approach will make AI agents more valuable and indispensable in daily life.
Enhanced Communication and Collaboration Tools
AI agents will enhance communication and collaboration tools, making it easier for teams to work together. These agents will facilitate real-time collaboration, manage project timelines, and provide insights to improve productivity. They will also assist in content creation, research, and workflow automation.
Shift and the of SaaS apps?
Another interesting section to listen too is at around 31 minutes, where Satya talks about his vision of how AI agents could potentially replace traditional SaaS (Software as a Service) applications. Whilst something that will not happen over night, he talked about the shift from business apps with connectors into other apps, but in an agent to agent and agent to back-end system.
We can already have connectors into applications like SAP, Dynamics etc. A great quote he used was “when was the last time any of us really went to a business application” In the AI age, we access the data in these systems from a mesh of data sources which over time, these back-end SaaS systems would eventually become obsolete as AI agents take over multi-repository CRUD (Create, Read, Update, Delete) operations. This shift would lead to the collapse of conventional business applications, with AI agents handling the core logic.
The idea will be that you simply pull information from systems through AI Agents such as looking up customer details, updating inventories, changing a contact in NetSuite CRM for example or checking delivery status for an order.
Other examples, Sayta talked about with regards AI Agents included:
Infinite Memory: He explained that infinite memory refers to the ability of AI agents to retain and recall information over extended periods, much like a human’s long-term memory. This capability will allows AI agents to build on past interactions and experiences, making them more effective and personalized in their responses and actions.
Proactive Task Management: AI agents are envisioned to operate autonomously, handling complex tasks such as processing customer returns, managing shipping invoices, and optimizing supply chain operations. This proactive approach reduces the reliance on user-initiated interactions, further diminishing the need for traditional SaaS applications.
Automation of Business Logic: Satya explained that AI agents would be able to automate many backend business processes, creating a new tier of multi-agent orchestration. This means that business logic, which is currently hardcoded into individual applications, will be managed by AI agents across multiple apps or databases and will adapt based on useage and need.
Integration with Existing Tools: Nadella highlighted the integration of Python with Excel as an example. AI agents can use Excel’s visualisation capabilities for advanced tasks, transforming it into a more intelligent and autonomous tool. This integration demonstrates how AI agents can enhance existing applications, making them more efficient and reducing the need for traditional SaaS apps.