Recognition: no theorem link
A Research Agenda on Agents and Software Engineering: Outcomes from the Rio A2SE Seminar
Pith reviewed 2026-05-13 05:53 UTC · model grok-4.3
The pith
A seminar of 18 experts has produced a research agenda with six priority themes for agentic AI and software engineering.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
The central claim is that structured discussions among the eighteen participants yielded a community-driven research agenda organized around six thematic areas—Governance, Software Engineering for Agents, Agents for Software Architecture, Quality and Evaluation, Sustainability, and Code—each containing concrete short-term and long-term research directions that together offer the software engineering community a coordinated foundation for addressing agentic AI.
What carries the argument
The key machinery is the community-driven, opinionated research agenda produced by collaborative topic clustering and focused group discussions at the seminar.
If this is right
- Work on governance must develop policies and oversight mechanisms for agent use in engineering processes.
- New software engineering methods are required to design, test, and maintain agentic systems as first-class artifacts.
- Agents can be applied to architecture tasks, requiring evaluation of how they affect design quality and decisions.
- Quality assurance and evaluation frameworks need extension to measure agent contributions and system outcomes.
- Sustainability considerations must be integrated into agent-assisted development and code generation practices.
Where Pith is reading between the lines
- The agenda implicitly calls for greater collaboration between AI researchers and traditional software engineering groups that have historically operated in separate venues.
- Industry adoption of the short-term priorities could accelerate the creation of practical tools and standards faster than academic efforts alone.
- The six areas may need periodic re-clustering as agent capabilities evolve beyond current large-language-model limitations.
Load-bearing premise
The load-bearing premise is that the priorities distilled from discussions among these eighteen selected experts are broadly representative and actionable for the wider software engineering research community.
What would settle it
A larger, independently organized follow-up seminar or survey of software engineering researchers that arrives at substantially different thematic priorities or rankings would falsify the claim that the agenda is representative.
read the original abstract
The rise of agentic AI is reshaping software engineering in two intertwined directions: agents are increasingly applied to support software engineering tasks, and Agentic AI systems themselves are complex systems that require re-thinking currently established software engineering practices. To chart a coherent research agenda covering the two directions, we organized the A2SE seminar in Rio de Janeiro, bringing together 18 experts from academia and industry. Through structured presentations, collaborative topic clustering, and focused group discussions, participants identified six thematic areas: Governance, Software Engineering for Agents, Agents for Software Architecture, Quality and Evaluation, Sustainability, and Code, and they prioritized short-term and long-term research directions for each. This paper presents the resulting community-driven, opinionated research agenda, offering the SE community a structured foundation for coordinating efforts at this critical juncture.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper reports on the A2SE seminar held in Rio de Janeiro with 18 experts from academia and industry. Through structured presentations, collaborative topic clustering, and focused group discussions, participants identified six thematic areas—Governance, Software Engineering for Agents, Agents for Software Architecture, Quality and Evaluation, Sustainability, and Code—and prioritized short-term and long-term research directions within each. The manuscript presents the resulting community-driven, opinionated research agenda as a foundation for coordinating software engineering efforts at the intersection with agentic AI.
Significance. If the documented outcomes faithfully reflect the seminar process, the work supplies a timely, structured reference point for the SE community to align research on agentic systems. The explicit labeling of the agenda as opinionated, combined with transparent description of the seminar method, provides a reproducible template for future community agenda-setting and surfaces actionable priorities in governance, quality evaluation, and sustainability that could inform collaborative projects and funding calls.
minor comments (2)
- The abstract states that six thematic areas were identified but does not name them; listing the names (Governance, Software Engineering for Agents, etc.) would improve immediate readability.
- In the description of the seminar process, additional detail on the exact mechanics of topic clustering and how short-term versus long-term priorities were assigned during group discussions would allow readers to better evaluate the robustness of the final agenda.
Simulated Author's Rebuttal
We thank the referee for their positive and constructive review of our manuscript. We are pleased that the referee recognizes the value of the community-driven research agenda emerging from the A2SE seminar and recommends acceptance.
Circularity Check
No significant circularity detected
full rationale
The manuscript is a transparent report of seminar outcomes from structured discussions, topic clustering, and prioritization among 18 experts. It contains no equations, fitted parameters, predictions, derivations, or self-referential reductions. The six thematic areas and research directions are presented as direct outputs of the participant process without any load-bearing step that reduces to prior inputs by construction or self-citation chain.
Axiom & Free-Parameter Ledger
axioms (1)
- domain assumption Agentic AI is reshaping software engineering in two directions that require coordinated research attention.
Reference graph
Works this paper leans on
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[1]
G. A. Lewis, H. Muccini, I. Ozkaya, K. Vaidhyanathan, R. Weiss, L. Zhu, Software architecture and machine learning (dagstuhl seminar 23302), Dagstuhl Reports 13 (7) (2024) 166–188
work page 2024
- [2]
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[3]
T. Ornelas, A. A. Araújo, J. Araújo, M. Araújo, B. Trinkenreich, M. Kalinowski, LLM-assisted The- maticAnalysis: Opportunities, limitations, andrecom- mendations, in: 3rd IEEE/ACM International Work- shop on Methodological Issues with Empirical Studies in Software Engineering (WSESE), 2026. 6
work page 2026
discussion (0)
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