AI agents are powerful, practical, and already being used in real-world systems — from automation and research to coding and multi-step reasoning.
In this video, we do not argue against AI agents.
Instead, we take an engineering-first approach.
We break down how AI agents actually work under the hood — autonomy, Chain-of-Thought, tool use, memory, planning, exploration, and stochastic sampling — and explain the tradeoffs each design choice introduces.
Every increase in capability comes with a cost:
- more autonomy means less reliability
- deeper reasoning increases error propagation and cost
- tool use adds latency and brittleness
- memory improves continuity but increases hallucination risk
AI agents are extremely useful and will continue to shape modern systems.
But understanding them through tradeoffs is what allows engineers to build reliable, scalable, production-grade solutions.
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