Advancing multimodal and agentic AI: systems, storage & scalability

The core advantage of multi-agent systems in computer vision lies in their ability to divide complex tasks into smaller, manageable sub-tasks, allowing for more efficient and scalable processing. This paradigm fosters collaboration, where agents can exchange information and update each other’s knowledge, resulting in a more refined outcome, keeping every agrnt in sync.

For instance, in generative tasks, one agent might focus on generating the background while another agent handles foreground details or texture. The key to success in these systems is their ability to dynamically coordinate actions, allowing the agents to adapt to new visual contexts, learn from each other, and provide creative, high-quality results in real-time. Libraries like smolagents, crewai have provided platforms to build and experiment with multi-agent setups

Key Takeaways:

1. Libraries and Frameworks: Popular libraries like Ray smolagents, browseruse, CrewAI enable the development and experimentation with MAS in computer vision applications (will discuss these frameworks in detail).

2. Experimentation and Innovation: Experiments in MAS-based computer vision have led to breakthroughs in areas like real-time image generation and adaptive learning, where agents learn to collaborate and improve over time, offering more dynamic and context-aware outputs.

3. How to build robust and scalable systems around MAS (Multi Agentic Systems) to scale to millions of users

4. How to detect and handle failures/hallucinations in thise systems

I am Akshat. I work as Tech Lead, Machine Learning at Glance. InMobi, working on a few generative AI tracks involving vision, audio and RL.
https://www.linkedin.com/in/agupta28/