mlsanityData to 14 Oct 2026

Making Sense of AI Agents Hype: Adoption, Architectures, and Takeaways from Practitioners

arXiv 2604.00189 ↗First posted 31 Mar 2026cs.SE cs.AI cs.NIcited by 0

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

To support practitioners in understanding how agentic systems are designed in real-world industrial practice, we present a review of practitioner conference talks on AI agents. We analyzed 138 recorded talks to examine how companies adopt agent-based architectures (Objective 1), identify recurring architectural strategies and patterns (Objective 2), and analyze application domains and technologies used to implement and operate LLM-driven agentic systems (Objective 3).

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 3 of its 8 references that are in the map. First posted on arXiv 31 Mar 2026.

Builds on

3 of 8 references.

References · 3

  1. 20246GSoft: Software for Edge-to-Cloud Continuumcited by 5
  2. 2025What Were You Thinking? An LLM-Driven Large-Scale Study of Refactoring Motivations in Open-Source Projectscited by 4
  3. 2025Emerging Trends in Software Architecture from the Practitioners Perspective: A Five Year Reviewcited by 2

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