Archive · 19 Jun 2025
Infosys Discusses Hybrid AI Architectures and Agentic AI
Imagine AI like a team of specialists instead of one person.
Hybrid AI is like that team, using different types of AI (like traditional machine learning, large language models, and small language models) to solve tricky business problems.
It's better because it's more accurate, cheaper, and keeps your data safe.
Infosys is using this approach, mixing different AI models to help businesses.
This new way of AI is also helping to create 'agentic AI', which are AI systems that can think and act on their own.
Infosys is transitioning from single-model AI to hybrid, multi-paradigm architectures (Poly AI).
Hybrid AI combines ML, LLMs, SLMs, and domain-specific models for enterprise problem-solving.
This approach enhances accuracy, reduces costs, and ensures data privacy via flexible deployment.
Infosys is using hybrid AI by integrating SLMs with LLMs and traditional ML techniques.
Hybrid AI is crucial for agentic AI systems, supporting planning, execution, and adaptation.
- Who
- Infosys, businesses seeking effective AI solutions.
- What
- Transition from single-model AI to hybrid AI for enterprise solutions, leading to agentic AI.
- When
- Ongoing shift, with Infosys deploying over 200 enterprise AI agents.
- Where
- Across various industries and verticals where Infosys operates.
- Why
- To solve complex business problems more effectively, improve accuracy, reduce costs, and ensure data privacy.
- Key Concept
- Hybrid AI (Poly AI)
- Main Benefit
- Improved Accuracy and Reliability
- Primary Method
- Combining multiple AI approaches
- Key Technologies
- ML, LLMs, SLMs
- Companies Involved
- Infosys
- Goal
- To transform enterprise processes