Welcome back to AI Unraveled, your strategic briefing on the business impact of artificial intelligence.

Today, we are doing something different. We are skipping the daily news cycle to focus on a single, massive piece of research that just dropped from a powerhouse team at Stanford, Princeton, Harvard, and the University of Washington that proposes the first proper taxonomy for Agentic AI Adaptation.

If you are building or scaling agent-based systems, this is your new mental model. The researchers argue that almost all advanced agentic systems—despite their complexity—boil down to just four basic feedback loops. We explore the "4-Bucket" framework (A1, A2, T1, T2) and explain the critical trade-offs between changing the agent versus changing the tools.

Key Topics:

Intro: Why "learning from feedback" is the definition of adaptation.
The Definition: What actually counts as "Agentic AI"?
Bucket A1 (Agent + Tool Outcome): Updating the agent based on whether code ran or queries succeeded.
Bucket A2 (Agent + Output Eval): Updating the agent based on human feedback or automated scoring.
Bucket T1 (Frozen Agent + Trained Tools): Keeping the LLM fixed while optimizing retrievers and external models.
Bucket T2 (Frozen Agent + Agent-Supervised Tools): Using the agent’s own signals to tune its toolkit.
Trade-offs: Cost vs. Flexibility in modern system design.

Links & Resources:

Read the Paper: Adaptation Strategies for Agentic AI Systems (GitHub): https://github.com/pat-jj/Awesome-Adaptation-of-Agentic-AI/blob/main/paper.pdf

Keywords: Agentic AI, AI Taxonomy, AI Research, Stanford AI, Princeton AI, Large Language Models, LLM Agents, Reinforcement Learning, Tool Use, RAG, A1 A2 T1 T2, AI Adaptation, Etienne Noumen, AI Unraveled.

🚀Credits: This podcast is created and produced by Etienne Noumen, Senior Software Engineer and passionate Soccer dad from Canada.

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🛠️How Agents Actually Learn: The 4-Bucket Taxonomy You Need to Know

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