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The AI Reasoning Paradox: Why Agents FAIL
Large Reasoning Models (LRM) have been the new rage for the last few months. The age of LLMs is over, it’s time for LRMs. Be it Gemini 2.5, Claude thinking mode, or GPT o-series models, all of them have moved towards reasoning models. Fundamentally, all of them are still LLMs only, but suddenly, these models feel much better and smarter.
Agentic AI sounds very cool, but anyone who has tried to build agents for real-world problems knows exactly how unreliable current agents really are. The complex the agentic architecture is, the harder it becomes to contain the agent. So, today we are going to take a deep dive into AI Reasoning Paradox.
Table of Contents
- Role Of Determinism and Stochasticity In Choosing The Correct AI Model
- Do Not Trust Reasoning Benchmarks
- Dilemma For Agents
- Can We Use MCP To Solve This Issue?
- Conclusion
Role Of Determinism and Stochasticity In Choosing The Correct AI Model
As the saying goes, there is no one-size-fits-all solution, and the same is true for the AI models, or specifically, LRMs. The models we call reasoning models are really not a reasoning model. It is just a clever hack where…
