Visualizing Deep Agents in Long-Horizon Tasks: Towards Explainable and Trustworthy Agentic AI

1 Introduction

The capabilities of Large Language Models (LLMs) have evolved from simple request-response to autonomous agents capable of multi-step workflows. As these systems scale to Long-Horizon Tasks spanning hours or days, traditional interaction paradigms break down. In automated software engineering [Deng et al., 2025; Oskooei et al., 2025c; Wang et al., 2025; Oskooei et al., 2025b; Xu et al., 2024; Oskooei et al., 2025a], scientific discovery [Du et al., 2025; Li et al., 2025; Huang et al., 2025; Manus AI, 2025], or data analysis [Luo et al., 2025; Ye et al., 2025], supervisors can no longer monitor progress via terminal logs. The volume of intermediate steps exceeds human capacity, necessitating a shift in observability. We frame the required interface as a "Cognitive Scaffold" to track asynchronous agentic behaviors.

This capability leap is driven by a transition from standard ReAct architectures to Deep Agents (Agents 2.0) [Yu et al., 2025; Ruan et al., 2026; Huang et al., 2025; Schmid, 2025; LangChain, 2025c; LangChain, 2025a]. We formally define a Deep Agent system D as a tuple characterized by four dimensions:

Specifically, these systems are defined by: (1) Sub-agents (H), which allow for dynamic delegation and the spawning of workers with isolated contexts on-the-fly [Block, 2025; Kiro, 2025; Anthropic, 2024; Cursor, 2025]; (2) A TODO List (P), facilitating explicit stateful long-term planning [Erdogan et al., 2025; Wang et al., 2023]; (3) An Inner Monologue (C), separating the agent's reasoning trace from its action stream; and (4) A Filesystem (E), acting as permanent memory that enables the agent to offload and retrieve context [Sun et al., 2025; Zhang et al., 2025; Mei et al., 2025]. While these features enable autonomy, they introduce significant opacity. Deep Agents currently operate as "Black Boxes." Existing observability tools [LangChain, 2025b; Xi et al., 2025] offer linear trace logs, which flatten the multi-dimensional execution of parallel sub-agents into a single, overwhelming list. Debugging a recursive delegation failure or a planner hallucination using these traces is computationally challenging. Recent commercial solutions attempt to address this by employing secondary AI agents to interpret these traces, effectively using one black box to explain another. This adds latency and cost without measuring the fundamental lack of transparency. Similarly, agentic IDEs prioritize result generation over process visibility, leaving developers with a binary outcome: success or an unexplained failure. "You can't fix what you can't see."

To address this interpretability barrier, we propose a standardized, non-blocking observability framework that visualizes the four dimensions of deep agent execution: 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-toInsight (TTI) for complex behavioral analysis by 56% and significantly lowers cognitive load (NASA-TLX) compared to state-of-the-art linear traces.

2 SystemArchitecture

The system is architected as a decoupled, event-driven pipeline illustrated in Figure 1. The core engine is built upon the Deep Agents library [LangChain, 2026], an open-source agent harness that extends the industry-standard LangGraph runtime.

This engine orchestrates the lifecycle of the Main Agent (Architect) and its dynamically spawned Sub-agents, managing their isolated context windows and tool execution.

The Sidecar Bridge, implemented via an asynchronous Observer pattern, operates out-of-band to intercept tool calls and state transitions without interrupting reasoning. Events are captured in real-time and buffered in a thread-safe store to ensure high-frequency updates are serialized correctly. To avoid overwhelming the client, we use a hybrid synchronization: Transient events (streamed thoughts and tool calls) are pushed via Server-Sent Events (SSE), while Persistent state (the Virtual Filesystem) uses Event-Triggered Revalidation. This prevents "state-bloat" and ensures high UI performance even in repositories with thousands of files. This architecture preserves agent performance while remaining non-intrusive.

3 The Observability Framework

We propose decomposing deep agent observability into four distinct dimensions (4D visualization), as in Figure 2. This framework is designed to be application-agnostic; any LangGraph-based system that exposes standard state schema keys (e.g., plan, subagents) can integrate with the visualization layer with minimal configuration.

Temporal Observability (The Plan). Visualized via the PlannerPanel (Figure 2-1), this dimension tracks the agent's explicit checklist or TODO list. Unlike linear logs, this persistent view allows users to monitor global progress, detect planner loops, and identify divergence between the intended plan and actual execution state in real-time.

Cognitive Observability (The Brain). The BrainPanel (Figure 2-3) streams the agent's "Inner Monologue" separately from its tool outputs. By isolating the reasoning trace (Thought) from execution mechanics (Action), we enable users to understand the "Why" behind behaviors without parsing verbose JSON payloads. This is critical for diagnosing hallucinations where the agent's reasoning remains sound but its actions fail.

Hierarchical Observability (TheWorkers). Deep agents solve complex tasks by spawning sub-agents. The Worker- Grid (Figure 2-4) employs a dynamic grid layout to visualize this delegation tree. Cards appear instantly as sub-agents are spawned, displaying their unique tool calls and status. This reveals the "hidden work" of parallel execution that is typically flattened in traditional trace views.

Spatial Observability (The Filesystem). Agents interacting with codebases modify their environment. The Virtual Filesystem (Figure 2-2) provides a real-time, read-only view of the agent's sandbox.

3.1 Comparative Analysis

Existing tooling falls into two categories: Result-Oriented (IDEs like Cursor, CLI tools like Claude Code) and TraceOriented (LangSmith, LangFuse). Result-oriented tools hide the process to reduce friction, leaving developers blind when reliable generation fails. Trace-oriented tools capture every event but present them as an overwhelming linear list. Our framework bridges this gap by offering Process-Oriented observability: it structures the data into human-readable dimensions, reducing the cognitive load required to build mental models of agent behavior.

4 Case Study &Evaluation

To validate the utility of our 4D observability framework, we integrated it into RepoLearn, an open-source workbench for automated codebase comprehension through a series of tutorials. Documentation of an unfamiliar repository is a representative Long-Horizon Task, typically requiring over 50 reasoning steps, recursive directory traversal, and the synthesis of multi-file contexts. This scenario serves as a stress test for our observability pillars, as it generates high-volume, multithreaded execution data that is practically indecipherable in standard linear trace formats.

4.1 Study 1: Efficiency Analysis

We conducted a controlled experiment to quantify the reduction in Time-to-Insight (TTI) for complex hierarchical reasoning tasks compared to State-of-the-Art tools.

Methodology. We recruitedN = 16AI Engineers (Avg. 1.5 years experience; 5 female, 11 male) proficient in LangGraphbased architectures. Using a between-subjects design, participants were randomly assigned to two balanced groups (n = 8): one using LangSmith (Linear Traces) and the other using our 4D framework. Both groups engaged in the same behavioral analysis task using five recent open-source repositories (DeepAgents, TOON, Agent Lightning, POML, RAG Anything), making sure the underlying LLM we used (GPT-OSS-120B) had not seen the codebases during training. For each repository, participants answered one question from each of five observability categories: (1) Attribution, (2) Causality, (3) Resource Usage, (4) Hallucination Detection, and (5) Planner Divergence. This resulted in a total of 25 questions per participant. Post-task, participants completed the NASA-TLX survey to measure cognitive load.

Results. The results demonstrate statistically significant performance gains across both metrics.

1. Time-to-Insight (TTI): The average TTI for answering the behavioral questions was reduced from 5.5 minutes using LangSmith to 2.4 minutes with our framework. An independent-samples t-test yielded a significant effect size (Cohen's d = 1.05, p = 0.004), indicating that the 4D decomposition effectively accelerates comprehension.
2. Cognitive Load: The NASA-TLX scores dropped from an average of 64.8 for the baseline to 43.7 for our framework. An independent-samples t-test confirmed a significant reduction in cognitive effort (p < 0.001, Cohen's d = 1.6). Participants reported that the ability to visually verify execution without mentally reconstructing the state from linear traces was the primary driver of this reduction.

These results support our hypothesis that 4D decomposition acts as an effective cognitive scaffold for agent supervision.

4.2 Study 2: User Trust

This study investigated whether high-fidelity process visibility (our 4D observability framework) increases user trust in autonomous agent outputs compared to standard linear traces.

Methodology. Using the same between-subjects assignment as Study 1, participants reviewed the documentation artifacts generated for the five repositories under their assigned observability paradigm:

(A) Linear Traces: Raw execution logs via LangSmith. (B) 4D Observability: Structured execution view via RepoLearn. Participants rated confidence (1-5 Likert scale) on: (1) Completeness ("Did the agent cover relevant files?"), (2) Accuracy ("Did the agent hallucinate?"), and (3) Deployability ("Would you publish without review?").

Results. The 4DObservability condition significantly outperformed the Linear Baseline in Accuracy and Deployability

(p < 0.01), as detailed in Table 1. Notably, while perceived Completeness was high across both conditions (4.0 vs 4.1), the divergence in Deployability (2.9 vs 3.8) underscores a critical finding: having the data (Linear) is not sufficient for trust. The structured "4D Observability" view enables users to effectively verify the agent's behavior, transforming raw information into actionable confidence.

5 Conclusion

The transition to Deep Agents requires a fundamental shift from goal-oriented to process-oriented interaction. Our 4D observability framework addresses the challenge of deep agent opacity by decomposing complex behaviors into humancomprehensible temporal, cognitive, hierarchical, and spatial dimensions. Through RepoLearn, we demonstrated that nonblocking, multi-dimensional visualization not only reduces cognitive load by approximately 33% but also establishes the necessary trust for human-agent collaboration in high-stakes environments. Future work will explore the generalizability of this framework to other long-horizon domains, such as scientific research and automated data analysis.

Acknowledgments

This work is supported by the Scientific and Technological Research Council of Turkey (TUBITAK). The authors are grateful for the resources and opportunities provided by the council for this research.

Disclosure: Use of Generative AI

In accordance with the IJCAI-ECAI 2026 policy on Large Language Models, the authors disclose that Gemini 3.0 Pro was utilized solely for grammatical proofreading and stylistic refinement of the manuscript. The core conceptual 4D framework, system architecture of RepoLearn, experimental design, and data analysis were developed and conducted exclusively by the human authors. The authors assume full responsibility for the accuracy and integrity of the entire submission.