Скачать или смотреть Adaptation of Agentic AI

Modern AI agents can reason, plan, and use tools…
yet they still fail in surprising ways. Why?
The real challenge isn’t building smarter agents —
it’s teaching them how to adapt.
In this video, we break down a clean and powerful framework for Agentic AI adaptation, based on recent research. You’ll learn how agents improve over time, where adaptation signals come from, and why most real-world systems mix multiple strategies instead of relying on just one.
We cover:
• Why static agents fail in real environments
• The difference between adapting the agent vs adapting the tools
• Four core adaptation paradigms explained simply
• Real examples from code execution, search, and memory systems
• Key trade-offs around cost, generalization, and modularity
• Common pitfalls that silently break agent learning
• Practical tips for designing more reliable agentic systems
If you’re building AI agents, LLM pipelines, or tool-augmented systems — this framework will change how you think about architecture and training.
This is a high-level but technical walkthrough designed for engineers and researchers who already know LLM basics and want deeper system-level insight.
Stay tuned for the next video, where we dive into co-adaptation — when agents and tools learn together.
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