# The Future is Agentic in Recommender Systems

Page: https://stenobird.com/podcast/data-skeptic/the-future-is-agentic-in-recommender-systems
Text version: https://stenobird.com/podcast/data-skeptic/the-future-is-agentic-in-recommender-systems.md
Podcast: [Data Skeptic](https://stenobird.com/podcast/data-skeptic)
Published: 2026-04-25T18:00:00+00:00
Episode link: https://dataskeptic.com/blog/episodes/2026/The-future-is-agentic-in-recommender-systems
Audio file: https://pscrb.fm/rss/p/mgln.ai/e/35/traffic.libsyn.com/secure/dataskeptic/Yashar_No_Ads_V1.mp3?dest-id=201630
Processing state: processed
JSON: https://stenobird.com/v1/public/podcasts/data-skeptic/episodes/the-future-is-agentic-in-recommender-systems
Duration seconds: 2965

## Resource

Recommender systems are shifting from static ranked lists to 'agentic' systems capable of end-to-end task completion. This transition introduces significant new challenges in trust, specifically regarding hallucinations and adversarial vulnerabilities in LLM-driven models.

## Highlights
- Main idea: The future of recommendation lies in agentic systems that use memory and tools to perform complex, multi-step tasks rather than just providing item lists
- Failure mode: LLM-based recommenders are prone to hallucinations and context drift, which can be particularly dangerous in high-stakes domains like medicine
- Practical takeaway: Building trustworthy systems requires addressing multiple dimensions, including robustness to adversarial attacks, privacy, and explainability
- Main idea: Multimodal generative models can revolutionize industries like fashion by allowing users to visually preview and iterate on personalized outfits
- Technical challenge: Evaluating modern generative recommenders requires complex multi-dimensional radar charts to track accuracy, latency, and ethical risks

## Topics

Recommender Systems, Large Language Models, Agentic AI, Generative AI, Trustworthy AI, Machine Learning, Multimodal Models, Adversarial Attacks

## Chapters
- 1:00 — The Evolution of Recommender Systems: An overview of how the field has moved from image processing and video recommendation to the current era of generative models.
- 4:45 — Trust and Adversarial Risks: A discussion on the necessity of trustworthy AI and the different perspectives on achieving robustness in recommendation pipelines.
- 8:25 — New Risks in the LLM Era: Examining how large language models introduce unprecedented risks like hallucinations and context drift into recommendation tasks.
- 12:10 — Multimodal Generative Advantages: How generative models can enhance user engagement through visual previews and interactive, natural language suggestions.
- 15:45 — From Ranked Lists to Agents: The fundamental shift from traditional collaborative filtering to revolutionary agentic systems that execute tasks.
- 19:55 — The Power of Agentic Models: Exploring dynamic systems that can handle multiple constraints and perform complex, multi-tasking operations for users.
- 38:20 — Dimensions of Evaluation: Analyzing the input, output, and observation spaces required to evaluate modern, large-scale language agents.

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## Transcript

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