{"type":"ScholarlyArticle","title":"Visualizing Deep Agents in Long-Horizon Tasks: Towards Explainable and Trustworthy Agentic AI","authors":[{"name":"Amirkia Rafiei Oskooei","slug":"amirkia-rafiei-oskooei","given":"Amirkia","family":"Rafiei Oskooei","affiliation":""},{"name":"Mehmet S. Aktas","slug":"mehmet-s-aktas","given":"Mehmet S.","family":"Aktas","affiliation":""}],"abstract":"The transition from prompt-based Large Language Models (LLMs) to autonomous Deep Agents has enabled the automation of long-horizon tasks. However, as these agents adopt hierarchical architectures with nested tool usage, they suffer from significant opacity. Existing linear tracing tools fail to capture the multi-dimensional complexity of parallel sub-agent execution, hindering both debugging and user trust. We propose a general-purpose observability framework that decomposes agent execution into four distinct visualization dimensions: Temporal, Cognitive, Hierarchical, and Spatial. We validate this framework through RepoLearn, an open-source workbench for automated codebase comprehension. Our user study demonstrates that this multi-dimensional approach reduces the Time-to-Insight (TTI) for complex behavioral analysis by 56% and significantly lowers cognitive load (NASA-TLX) compared to state-of-the-art linear traces. The source code is available at https://github.com/amirkiarafiei/repo-learn and the demo at https://www.youtube.com/watch?v=s3U6E9o94gk.","doi":"10.24963/ijcai.2026/987","url":"https://ijcai.org/proceedings/2026/987","pdf":"https://ijcai.org/proceedings/2026/0987.pdf","markdown":"https://ijcai.org/proceedings/2026/987.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/987","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Demo Track","track":"Demo Track","number":987,"pages":{"first":8492,"last":8496},"keywords":["AI: Humans and AI","AI: AI Ethics, Trust, Fairnes","AI: Agent-based and Multi-agent Systems"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}