@inproceedings{saeidi-etal-2026-fama,
    title = "{FAMA}: Failure-Aware Meta-Agentic Framework for Open-Source {LLM}s in Interactive Tool Use Environments",
    author = "Saeidi, Amir  and
      Mishra, Venkatesh  and
      Mukhopadhyay, Souradeep  and
      Liu, Gaowen  and
      Payani, Ali  and
      Srinivasa, Jayanth  and
      Baral, Chitta",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1716/",
    pages = "34346--34367",
    ISBN = "979-8-89176-395-1",
    abstract = "Large Language Models are being increasingly deployed as the decision-making core of autonomous agents capable of effecting change in external environments. Yet, in conversational benchmarks, which simulate real-world customer-centric issue resolution scenarios, these agents frequently fail due to the cascading effects of incorrect decision-making. These challenges are particularly pronounced for open-source LLMs with smaller parameter sizes, limited context windows, and constrained inference budgets, which contribute to increased error accumulation in agentic settings. To tackle these challenges, we present the **Failure-Aware Meta-Agentic (FAMA)** framework. FAMA operates in two stages: first, it analyzes failure trajectories from baseline agents to identify the most prevalent errors; second, it employs an orchestration mechanism that activates a minimal subset of specialized agents tailored to address these failures by injecting a targeted context for the tool-use agent before the decision-making step. Experiments across open-source LLMs demonstrate performance gains up to **27{\%}** across evaluation modes over standard baselines. These results highlight that targeted curation of context through specialized agents to address common failures is a valuable design principle for building reliable, multi-turn tool-use LLM agents that simulate real-world conversational scenarios."
}