Agentic AI represents a paradigm shift in artificial intelligence, driving systems that not only respond to commands but proactively anticipate needs, act independently, and collaborate seamlessly with humans and machines. Unlike traditional reactive AI, these autonomous agents learn from their environments, adapt strategies in real-time, and orchestrate complex tasks with minimal human oversight. This transformation is fundamentally changing how we interact with technology, promising smarter, more efficient operations across industries and everyday life.
Imagine a smart city where agentic AI-powered controllers oversee traffic, energy consumption, and emergency response. During morning rush hour, AI agents dynamically reroute vehicles to minimize congestion and optimize public transportation schedules, leveraging real-time data from road sensors, GPS trackers, and traffic cameras. If an accident occurs, these agents instantly coordinate with emergency services, clearing routes for ambulances and dispatching drones to assess the situation. Simultaneously, energy grids are autonomously balanced, lowering costs and preventing outages as agents respond to real-time consumption data and weather forecasts. For instance, during a heatwave, AI can pre-emptively manage load by adjusting the output of renewable sources and selectively dimming non-essential city lighting, ensuring uninterrupted supply for hospitals and critical infrastructure.
In the retail district, inventory management is automated, AI agents detect low stock, negotiate with suppliers, and coordinate deliveries to keep shelves full. For example, when a popular product runs low, the system initiates automatic restocking by analyzing sales trends and sourcing from the most efficient supplier. It can even adjust pricing in real-time to manage demand or offer personalized promotions to customers. Personalized AI shopping assistants greet customers in-store and online, curating product recommendations based on purchase history, preferences, and current trends. These assistants can arrange seamless logistics for pickups or home delivery, track orders, and handle returns, providing an integrated and highly personalized shopping experience.
Meanwhile, educational institutions are transformed by autonomous AI tutors and classroom managers. These agents tailor lessons to individual student needs by analyzing performance data, identifying areas where a student may be struggling, and adjusting the curriculum accordingly. For group projects, AI matches students based on complementary skills and learning styles, fostering effective collaboration. Classroom managers automate administrative tasks like scheduling exams, monitoring attendance through facial recognition, and providing real-time feedback to educators. This allows teachers to focus on mentorship, creativity, and the holistic development of students, while parents receive timely updates and progress reports via AI-driven communication platforms.
This interconnected ecosystem of agentic AI creates a future of smoother commutes, hyper-efficient retail experiences, and deeply personalized learning environments—all powered by intelligent systems that anticipate and adapt to our needs. The synergy between diverse AI agents ensures that urban living becomes more sustainable, responsive, and tailored to the aspirations of citizens.
Another active area of research is Deep agents, which represents the next stage in the evolution of AI autonomy. Unlike agentic systems that execute short, repetitive tool-calling loops, deep agents operate over longer timeframes and address more complex, open-ended tasks. Their key differentiators include dynamic planning tools, file systems for robust context management, the use of subagents for isolating domain-specific tasks, and advanced prompt engineering to align agent behavior with developer intent. These advancements enable deep agents to plan, delegate, remember, and reason with greater sophistication, offering businesses enhanced automation capabilities and improved operational efficiency. Models will keep getting cheaper, faster, and smarter. Deep agents are about trusting that loop - trusting the model to do more.
Emerging technologies and key areas for submissions:
| Key Area | Scenario Application |
| Multi-Agent Systems | Networks of intelligent agents collaborating to solve complex, dynamic problems, such as city-wide disaster management, where different agents coordinate evacuation, resource allocation, and communication with the public. |
| Reinforcement Learning | Techniques that enable AI agents to learn optimal strategies through trial and error, such as robots in warehouses learning the fastest routes for order picking or traffic controllers adapting to unpredictable patterns caused by public events. |
| Autonomous Decision-Making Frameworks | Architectures that empower AI to act independently based on evolving data, for instance, self-regulating water supply systems adjusting distribution based on consumption patterns and weather forecasts. |
| Collaborative AI Architectures | Systems designed for seamless interaction and task coordination among agents and humans, exemplified by healthcare teams where AI assists doctors in diagnosis, treatment planning, and follow-up care, ensuring holistic and timely patient support. |
As agentic AI becomes the backbone of intelligent ecosystems, submissions in these areas will help shape the next generation of autonomous, proactive, and collaborative technologies. Consider how your abstract addresses real-world scenarios and leverages emerging innovations in multi-agent systems, edge computing, IoT, automation, and reinforcement learning.
Here are a few scenarios that you may wish to consider –
- Intelligent virtual agents understand context, orchestrate multi-step problem resolution, and integrate data from multiple systems to improve response times and customer satisfaction.
- Agentic AI manages end-to-end content generation, brand voice consistency, scheduling, and customer engagement strategies across platforms with minimal human oversight.
- Agentic AI systems independently analyse market data and geopolitical events to execute multi-asset trading strategies and dynamically adjust risk exposure without human intervention.
- AI agents continuously monitor supply conditions like weather, geopolitical risks, and demand fluctuations, autonomously rerouting shipments and adjusting procurement to optimize inventory and reduce waste.
- Agentic AI autonomously manages urban systems such as traffic flow, energy distribution, and emergency response coordination by processing massive sensor data in real time.
- Agentic agents monitor regulatory updates, automatically update internal policies, conduct compliance checks, and orchestrate risk mitigation workflows.