@inproceedings{inan-etal-2026-dialogue,
title = "Dialogue is the Plan: From Interface to Joint Action in Agentic {AI}",
author = "Inan, Mert and
Alikhani, Malihe and
Sicilia, Anthony",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 2: Short Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-short.63/",
pages = "770--779",
ISBN = "979-8-89176-391-3",
abstract = "Large Language Model agents can seeminglyplan and act, yet their language use is oftentreated primarily as an interface for instructingactions and reporting results. We argue that thisframing is one important cause of recurrent coordination failures in human-facing and multiagent settings, including ungrounded assumptions, silent goal misalignment, brittle protocoladherence, and failures to maintain or updateshared dialogue state over time, a limitation previously linked to the absence of explicit common ground tracking in collaborative systems(Geib et al., 2022). Drawing from classical dialogue system research on joint action, commonground, grounding, repair, and incremental processing, we re-frame dialogue as part of theplanning loop itself (rather than its output). Wedistill this re-framing into concrete implicationsfor agentic architecture and evaluation, including explicit representations of shared commitments, clarification as a first class action available to the policy, and process metrics that approximate grounding behavior, repair, and commitment formation rather than task completionalone. We lastly discuss how dialogue-centeredrequirements can inform standards and governance for safe deployment of agentic systems."
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<abstract>Large Language Model agents can seeminglyplan and act, yet their language use is oftentreated primarily as an interface for instructingactions and reporting results. We argue that thisframing is one important cause of recurrent coordination failures in human-facing and multiagent settings, including ungrounded assumptions, silent goal misalignment, brittle protocoladherence, and failures to maintain or updateshared dialogue state over time, a limitation previously linked to the absence of explicit common ground tracking in collaborative systems(Geib et al., 2022). Drawing from classical dialogue system research on joint action, commonground, grounding, repair, and incremental processing, we re-frame dialogue as part of theplanning loop itself (rather than its output). Wedistill this re-framing into concrete implicationsfor agentic architecture and evaluation, including explicit representations of shared commitments, clarification as a first class action available to the policy, and process metrics that approximate grounding behavior, repair, and commitment formation rather than task completionalone. We lastly discuss how dialogue-centeredrequirements can inform standards and governance for safe deployment of agentic systems.</abstract>
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%0 Conference Proceedings
%T Dialogue is the Plan: From Interface to Joint Action in Agentic AI
%A Inan, Mert
%A Alikhani, Malihe
%A Sicilia, Anthony
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-391-3
%F inan-etal-2026-dialogue
%X Large Language Model agents can seeminglyplan and act, yet their language use is oftentreated primarily as an interface for instructingactions and reporting results. We argue that thisframing is one important cause of recurrent coordination failures in human-facing and multiagent settings, including ungrounded assumptions, silent goal misalignment, brittle protocoladherence, and failures to maintain or updateshared dialogue state over time, a limitation previously linked to the absence of explicit common ground tracking in collaborative systems(Geib et al., 2022). Drawing from classical dialogue system research on joint action, commonground, grounding, repair, and incremental processing, we re-frame dialogue as part of theplanning loop itself (rather than its output). Wedistill this re-framing into concrete implicationsfor agentic architecture and evaluation, including explicit representations of shared commitments, clarification as a first class action available to the policy, and process metrics that approximate grounding behavior, repair, and commitment formation rather than task completionalone. We lastly discuss how dialogue-centeredrequirements can inform standards and governance for safe deployment of agentic systems.
%U https://aclanthology.org/2026.acl-short.63/
%P 770-779
Markdown (Informal)
[Dialogue is the Plan: From Interface to Joint Action in Agentic AI](https://aclanthology.org/2026.acl-short.63/) (Inan et al., ACL 2026)
ACL
- Mert Inan, Malihe Alikhani, and Anthony Sicilia. 2026. Dialogue is the Plan: From Interface to Joint Action in Agentic AI. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 770–779, San Diego, California, United States. Association for Computational Linguistics.