Anatomy of AI Agents: Inside LLMs, RAG Systems, & Generative AI

Item: Anatomy of AI Agents: Inside LLMs, RAG Systems, & Generative AI
Rating: 4
Author: PostSingularity Institute

by Jeff Crume

Formal & Physical Sciences AI — News & Applications UYComputer science UYQArtificial intelligence (AI)

🎙 Jeff Crume 👥 1.8M 📅 December 11, 2025 ⏱ 10 min 👁 94K 📄 science communication 🧭 2026-08-06

Available in: English (current) Français

Keywords

AI agentLLMRAGsensingthinkingactingfeedback loopreinforcement learning

Summary

The video, presented by Jeff Crume of IBM Technology, explains the anatomy of AI agents by breaking them down into three main components: sensing, thinking, and acting. Sensing involves gathering input from the environment through text, sensors, or APIs. The thinking phase incorporates a knowledge base (including facts, rules, and policies) and reasoning mechanisms such as if-then logic, task decomposition, and machine learning, often leveraging large language models. The acting phase generates outputs like text, speech, or actions, potentially interfacing with actuators for real-world tasks. A crucial feedback loop, often using reinforcement learning with human feedback, allows the agent to evaluate and improve its performance. The video illustrates this with a travel booking example, showing how inputs like dates and destination, combined with personal preferences and company policies, lead to actions like booking flights and hotels. The presentation is clear and accessible, aiming to demystify AI agents for a general audience, but it stays at a conceptual level without delving into technical implementation details.

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Critical Evaluation

The video provides a solid, accessible introduction to the architecture of AI agents, effectively using the sensing-thinking-acting framework to structure the explanation. The content is accurate and aligns with current AI paradigms, particularly the integration of LLMs and RAG systems. The use of a concrete example (travel booking) helps ground the abstract concepts, making it easier for viewers to grasp the practical application. However, the video remains at a high level, lacking technical depth. It does not delve into the specifics of how LLMs are trained, how RAG retrieves and integrates knowledge, or the nuances of reinforcement learning. The discussion of reasoning is somewhat superficial, mentioning chain-of-thought but not exploring its implications. The sources cited are limited to IBM promotional links, which, while relevant, do not provide direct references to the underlying research or technical documentation. This reduces the video’s utility for viewers seeking to verify or expand upon the information. The title accurately reflects the content, and the presentation is well-structured and engaging. The video’s strength lies in its clarity and pedagogical approach, making it a good starting point for beginners. However, for those with prior knowledge, it may feel too basic. The lack of critical examination of limitations or potential biases in AI agents is a notable omission. Overall, the video is a reliable but introductory overview, suitable for a general audience, but not for those seeking in-depth technical understanding.

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Title / Content Match

The title accurately reflects the content, which systematically dissects the anatomy of AI agents into sensing, thinking, and acting components, with a focus on LLMs and RAG.

Quality & Reliability

7/10

The video provides a clear, high-level overview of AI agents, accurately explaining core concepts like LLMs, RAG, and reinforcement learning. The information is consistent with established AI principles, but it lacks depth and specific citations to primary sources, limiting its reliability for advanced audiences.

Key Moments

Markers derived by PSI from the transcript: the creator did not define chapters.

Cited Sources

IBM watsonx Assistant Engineer certification — Mentioned in the video description as a promotional offer for certification.
IBM AI agents information — Linked in the description as a resource to learn more about AI agents.
IBM AI newsletter — Linked in the description for monthly AI updates.

Concurring Sources

IBM watsonx Assistant Engineer certification — The video's description links to IBM's certification, which aligns with the content's focus on AI agents.
IBM AI agents information — The linked resource likely provides further details on AI agents, supporting the video's claims.

Contribution & Novelties

The video offers a clear and structured introduction to AI agents, breaking down their anatomy into sensing, thinking, and acting components, and emphasizing the role of feedback loops. It effectively uses a practical example to illustrate the concepts. While not novel for experts, it serves as a valuable educational resource for beginners.

Pour aller plus loin :

Large language model — Provides foundational knowledge on LLMs, which are central to the video’s discussion.
Retrieval-augmented generation — Explains the RAG technique mentioned in the video for enhancing LLMs with external knowledge.
Reinforcement learning from human feedback — Details the RLHF approach highlighted in the feedback loop section.

106 words

Radar Profile

The radar profile shows a balanced distribution across scores, with slightly higher scores in quality and reliability compared to quantity and technical depth. This indicates a well-presented but introductory video that prioritizes clarity over exhaustive detail.

Reliability 7/10