---
title: 'AgentFail: Root Cause Diagnosis in Agent Systems'
url: https://www.emergentmind.com/topics/agentfail
type: topic
---

# AgentFail: Root Cause Diagnosis in Agent Systems

AgentFail is a dataset, taxonomy, and benchmark for diagnosing failure root causes in platform-orchestrated agentic systems, defined as multi-agent workflows built on low-code or template-based platforms such as Dify and Coze. Its central objective is to shift debugging from identifying where a failure happened to explaining why it happened. The motivating claim is that prior work mostly emphasized failure attribution at the level of “which agent/step failed,” whereas meaningful repair requires deeper root-cause diagnosis across prompts, workflow orchestration, tools, and platform runtime behavior [2509.23735].

## 1. Problem definition and formalization

AgentFail targets platform-orchestrated agentic systems in which failures can arise from the underlying agent model, prompt design, workflow orchestration, or platform/runtime issues. In this setting, a failure log is not treated merely as evidence of an erroneous step, but as a trajectory requiring diagnosis of the decisive error and its underlying cause. The paper explicitly frames root-cause diagnosis as a prerequisite for debugging, repair, and reliability improvement, because labels such as “bad prompt,” “incorrect task decomposition,” “deadlock in workflow control,” “tool mismatch,” or “platform outage” are more actionable than simple step localization [2509.23735].

The paper includes a compact formalism for failure localization. For a trajectory $\tau$, failure is represented by a binary variable: $\phi(\tau)=1$ for failure and $\phi(\tau)=0$ for success. For an error $E(i,t)$, the decisive-error indicator is

$$
\Delta_{E(i,t)}(\tau) =
\begin{cases}
1, & \text{if } \phi(\tau) = 1 \text{ and } \phi(\tau^{E(i,t)}) = 0, \\
0, & \text{otherwise}.
\end{cases}
$$

If multiple decisive errors exist, the earliest one is selected as

$$
E(i^*,t^*)=\arg\min_{E(i,t)\in C(\tau)} t,\quad \text{where } C(\tau)=\{E(i,t)\mid\Delta_{E(i,t)}(\tau)=1\}.
$$

This “first decisive error” principle aligns the benchmark with counterfactual reasoning: a step is causally important only if correcting it changes the outcome from failure to success. The paper also distinguishes two outcome types for impact analysis: **Execution Failure**, where the run is interrupted, and **Suboptimal Quality**, where the run completes but output quality is inadequate [2509.23735].

## 2. Dataset construction and annotation procedure

AgentFail contains **307 failure logs** collected from **10 platform-orchestrated agentic systems** built on **two representative platforms**, **Dify** and **Coze**, with **5 systems from Dify** and **5 systems from Coze**. The paper reports that these failures were collected from **1,387 test runs** in total. Each instance contains the **Query**, the **Failure log**, the **System workflow and configuration**, and the **Annotations**, including a root-cause label and explanation [2509.23735].

The corpus mixes two sources. One source is **community-shared issues**, consisting of publicly reported failures from users. The other is **controlled runs by the authors**, including executions of public benchmarks such as **HumanEval** and **TravelPlanner**, plus hand-crafted datasets tailored to each system. The ten systems span multiple task categories—software development, program repair, product QA, travel assistance, deep research, market research, industry analysis, and question answering—and multiple workflow structures: serial, parallel, branching, looping, and hybrid. This diversity is intended to capture failures caused by both task semantics and orchestration structure [2509.23735].

Annotation follows a **Grounded Theory** approach. **Three expert annotators** independently inspected the logs, identified the **decisive error** and corresponding root cause, and marked cases as **fully confident** or **uncertain**. Uncertain cases were resolved through discussion, followed by cross-review for consistency. Final inter-annotator agreement improved from **Cohen’s $\kappa = 0.85$** initially to **1.0** after reconciliation. The result is a curated expert-consensus dataset rather than an automatically labeled corpus [2509.23735].

## 3. Three-level taxonomy of failure root causes

The core conceptual contribution of AgentFail is a **three-level taxonomy** that separates failures by where they originate: inside an individual agent, in workflow orchestration, or in the platform/runtime layer. The taxonomy is intended both as an annotation scheme and as structured guidance for automated diagnosis.

| Level | Description | Subcategories |
|---|---|---|
| **F1. Agent-level failures** | Within a single agent or its interaction with local resources | F1.1 Tool or action planning error; F1.2 Response formatting error; F1.3 Response content deviation; F1.4 Knowledge or reasoning limitation; F1.5 Prompt design defect; F1.6 Language or encoding defect; F1.7 Tool invocation or KB retrieval error |
| **F2. Workflow-level failures** | Orchestration, control flow, or inter-agent coordination | F2.1 Missing input validation; F2.2 Unreasonable node dependency; F2.3 Loops and deadlock; F2.4 Faulty conditional judgment; F2.5 Improper task decomposition; F2.6 Context conflict; F2.7 Cross-agent tool or interface mismatch |
| **F3. Platform-level failures** | Platform/runtime infrastructure | F3.1 Network and resource fluctuation; F3.2 Service unavailability |

Several distributional patterns are reported. **Agent-level failures dominate overall**. The most frequent categories are **F1.4 knowledge/reasoning limitations** and **F1.5 prompt design defects**, both with **more than 50 instances each**. Other common categories include **F1.2 response formatting errors** and **F1.3 response content deviations**. Workflow failures are less frequent overall, but **F2.1 missing input validation** and **F2.2 unreasonable node dependency** are especially common. Platform failures are the smallest share overall, consisting of **F3.1 network/resource fluctuation** and **F3.2 service unavailability** [2509.23735].

The paper also reports platform- and task-specific regularities. **Coze** shows more **F2.2 unreasonable dependencies**, attributed to very long serial workflows with many agents, which increase dependency-chain fragility. By task domain, software development is associated with reasoning limitations and prompt design issues; question answering with response formatting and tool or retrieval problems; task planning with language or encoding problems and dependency issues; and information insight tasks with response content deviation and reasoning limitations [2509.23735].

Impact analysis injects one failure type at a time and measures degradation in **Execution termination**, **Suboptimal quality**, and **Sum**. Reported high-impact cases include **F3.2 service unavailability: 90.3%**, **F1.5 prompt design defect: 90.1%**, **F1.2 response formatting error: 89.5%**, and **F3.1 network/resource fluctuation: 89.6%**. The paper further notes that **F1.4** and **F1.5** tend to produce suboptimal outputs, while **F1.2**, **F1.6**, and **F1.7** often cause execution termination. At the workflow level, **F2.3 loops/deadlock** and **F2.7 cross-agent interface mismatch** often terminate execution, whereas **F2.5 task decomposition** and **F2.6 context conflict** more often degrade quality without stopping execution [2509.23735].

## 4. Counterfactual validation of annotation reliability

A notable methodological feature is the use of **counterfactual reasoning-based repair strategy** to validate annotations. For a labeled root cause $\mathcal{D}$, experts design a repair specifically intended to fix that cause. If applying that repair changes the case from **failure to success**, the original label is considered validated. This operationalizes the question: “If this root cause were fixed, would the system succeed?” [2509.23735]

The paper samples the **top half of failure types by frequency** for this verification. Example repairs include enforcing strict JSON output with validation and reprompting for **response formatting error**; semantic similarity checks with a correction prompt for **response content deviation**; switching to a stronger or more domain-adapted model for **knowledge/reasoning limitation**; a standardized prompt template for **prompt design defect**; encoding normalization for **language/encoding defect**; explicit checks for **missing input validation**; workflow redesign for **unreasonable node dependency**; and fallback branches or backup models for **network/resource fluctuations** [2509.23735].

The confusion matrix of repair effectiveness shows **strong diagonal dominance**, meaning that the repair aligned with the annotated cause usually works best. Reported examples include **D1 response formatting error: 90.1%**, **D2 response content deviation: 95.6%**, and **D5 language/encoding defect: 96.3%**. The paper notes some off-diagonal effects, indicating that some fixes can incidentally help multiple failure types. Even so, the repair study is presented as evidence that the labels are reliable rather than purely descriptive [2509.23735].

## 5. Benchmark design and empirical results

The AgentFail benchmark evaluates whether large language models can identify the correct root-cause label from failure logs. The evaluated models are **gpt-4o**, **GEMINI-2.5-PRO**, **CLAUDE-SONNET-4**, **LLaMA-3.1-70B**, **QWEN-32B**, and **DeepSeek-R1**. Models are tested in two settings: **without taxonomy**, where the model relies on its own interpretation, and **with taxonomy**, where the taxonomy and definitions are supplied as guidance. To handle long logs, the benchmark uses three inference protocols: **All-at-once**, **Step-by-step**, and **Binary search**. The target is to predict the correct root-cause label, and the reported metric is **accuracy** [2509.23735].

The best reported result is **33.6%**, achieved by **DeepSeek-R1** under the **with-taxonomy + step-by-step** setting. Without taxonomy, models achieve about **8.3% to 13.0% accuracy**. With taxonomy, accuracy rises to roughly **24.1% to 33.6%**. The paper therefore reports an absolute gain of about **15–20 percentage points** from taxonomy guidance. At the same time, the ceiling remains low: even the best system gets only about one-third of cases correct. The authors interpret this as evidence that failure logs are long, noisy, and causally tangled, so later symptoms may be visible while the true trigger remains difficult to trace [2509.23735].

A common misconception is that a high-quality taxonomy would make automated diagnosis nearly solved. The reported results do not support that interpretation. Rather, the taxonomy **can largely improve the performance**, thereby confirming its utility, while also showing that **root cause identification reaches at most 33.6%**, which indicates that the task remains challenging. In that sense, the benchmark functions both as an evaluation resource and as a measurement of current limitations in root-cause diagnosis [2509.23735].

## 6. Practical guidelines and design implications

Beyond annotation and benchmarking, AgentFail extracts system-design guidance from the observed failure modes. The paper recommends **clear role specification and modular prompt design**, **explicit input/output validation**, **fallback mechanisms and secondary checks**, and **progressive workflow design** that starts with simpler serial or parallel structures before adding complex branching, looping, or hybrid structures. These recommendations are tied directly to the taxonomy rather than offered as generic best practices [2509.23735].

The rationale is visible in the empirical patterns. Prompt defects, reasoning limitations, and response-formatting errors are frequent and high-impact; missing input validation and unreasonable dependencies are the most common workflow-level faults; and platform failures, although rarer, are highly destructive and usually terminate execution. A plausible implication is that reliability engineering for platform-orchestrated agentic systems should not be concentrated in a single layer. Prompt design, schema enforcement, workflow structure, and fallback runtime behavior all appear in the root-cause distribution [2509.23735].

The paper also notes qualitative findings that long serial workflows are especially vulnerable to dependency problems and error propagation, and that some failures are **symptom-shared**: fixing a reasoning issue may also resolve what was labeled as a formatting issue. This suggests that root causes can interact causally even when the taxonomy assigns a single decisive label for benchmarking purposes [2509.23735].

## 7. Position within agent-failure research

AgentFail belongs to a broader research shift from outcome-only evaluation toward trajectory-grounded diagnosis, but it occupies a distinct niche. Its emphasis is root-cause diagnosis for **platform-orchestrated agentic systems**, whereas several adjacent works focus on failure attribution or localization in other settings. **A2P** reframes automated failure attribution as a causal inference problem over long conversation logs and asks which single corrective intervention would have prevented failure [2509.10401]. **FAMAS** treats failure attribution as a spectrum-based localization problem over repeated multi-agent executions, ranking the most suspicious agent-action-state tuple [2509.13782]. **AgentTrace** reconstructs causal graphs from execution logs and ranks candidate root causes without requiring LLM inference at debugging time [2603.14688]. **POIROT** turns the agents in a multi-agent system into the diagnostic layer itself through peer interrogation and weighted consensus on fault attribution [2606.02282].

Other work extends the diagnosis problem into software engineering and general agent execution traces. **AgentRx** diagnoses failed trajectories by synthesizing global and dynamic constraints, producing an auditable validation log, and using an LLM-based judge to localize the first unrecoverable failure step and its category [2602.02475]. **TrajAudit** addresses repository-level coding trajectories by combining preliminary diagnosis from test-failure reports with semantic saliency folding and tool-based retrieval over long, noisy traces [2605.26563]. These systems focus on step localization and critical failure identification; AgentFail instead foregrounds the taxonomy of underlying causes and the behavior of platform-built workflows [2509.23735].

This suggests a useful conceptual distinction. Failure attribution methods ask which step, node, or agent introduced the decisive error. AgentFail asks what structural, prompting, workflow, or platform-level cause made the system fail. The two questions are not interchangeable. The first supports localization; the second supports repair planning. AgentFail’s contribution is to make that second question explicit, annotated, and benchmarkable [2509.23735].

Source: https://www.emergentmind.com/topics/agentfail