The Business Standard article highlights a growing shift in AI development: companies are no longer necessarily choosing one AI model for every task. Instead, AI agents can dynamically decide which model is best suited to a particular request, balancing factors such as reasoning ability, speed, cost, context length and reliability. This approach—often called model routing—treats different AI models as interchangeable components within a larger system.
The advantage is that organizations can match intelligence to the difficulty of the task. A simple request might be sent to a smaller, cheaper model, while a complex coding, research or reasoning task could be routed to a more capable model. This can reduce costs and latency while maintaining quality, particularly when an agent handles thousands or millions of requests. Instead of asking “Which is the best model?”, companies increasingly need to ask “Which model is best for this particular job?”
This also changes the role of the AI agent itself. The agent becomes an orchestrator sitting above multiple models, deciding when to use each one and potentially evaluating the quality of the resulting answer before continuing. That makes routing logic, evaluation systems, observability and fallback mechanisms increasingly important. The model is becoming one component in a broader AI infrastructure stack rather than the entire product.
The broader takeaway is that the future AI stack may be less about model loyalty and more about intelligent model allocation. As the number of capable models grows, the winning systems could be those that automatically select the right combination of intelligence, speed and cost for every task. In that world, the competitive advantage shifts from simply having access to the strongest model to building the best system for deciding when, where and how each model should be used.
