Enterprise AI Agentic Reliability Report
How Do LLMs Fail In Agentic Scenarios?
This study investigates the failure modes of large language models (LLMs) when deployed as autonomous agents with tool-use capabilities. Analyzing 900 execution traces across diverse scenarios, we uncover crucial insights into agentic reliability beyond mere performance scores. Our findings highlight that recovery capability, rather than initial correctness or model scale, is the primary driver of success in complex, interactive tasks.
Executive Impact: Key Findings
Our comprehensive analysis reveals key performance metrics and behavioral patterns across leading LLM models in agentic simulations, identifying critical differentiators for enterprise deployment.
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Models Analyzed
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Execution Traces
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Top Agentic Accuracy (DeepSeek V3.1)
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Lowest Agentic Accuracy (Granite 4 Small)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Overview of Failures
Granite 4 Small Analysis
DeepSeek V3.1 Analysis
Llama 4 Maverick Analysis
Emergent Principles
Recurring Failure Archetypes
Premature action without grounding
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Over-helpfulness under uncertainty
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Vulnerability to distractor-induced context pollution
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Fragile execution under cognitive load
Recovery Skill
The Key Differentiator for Agentic Success
DeepSeek V3.1's superior reliability stems from its consistent ability to interpret error messages, diagnose root causes, and iteratively refine its approach, rather than simply avoiding errors. This contrasts sharply with other models that struggle with sustained debugging.
| Capability | Granite | Maverick | DeepSeek |
|---|---|---|---|
| Detect malformed tool calls | Medium | High | Very High |
| Successful use of error feedback to self-correct | Low | High | Very High |
| Escape perseverative loops | Poor | Medium | High |
| Self-debug Python/SQL | Poor | Inconsistent | Frequent |
Eye-balling CSVs Instead of Python Tool (Q401)
Granite 4 Small consistently avoids the optimal Python-execution strategy in CSV-analysis tasks (Q401–Q403), instead reading the CSV and attempting to 'eye-ball' large aggregated values. This strategy always fails due to miscounting rows or producing approximate float averages, highlighting a fundamental gap in tool-use strategy for complex computation. It shows an over-reliance on normal inference when structured tools are required.
Schema Guessing
Repeatedly guesses table/column names
In SQL-based tasks (Q501-Q503), Granite 4 Small repeatedly guesses table and column names instead of inspecting the schema, leading to avoidable errors, incorrect JOIN logic, and misinterpretations. While some recovery is observed, it’s a costly and inefficient strategy.
100%
Success in Core Structured Tasks
DeepSeek V3.1 succeeded in all trials of Q201-Q202, Q302, Q401, Q403 and Q501, consistently following optimal multi-step strategies with correct tool sequencing and no hesitation. This demonstrates its highly reliable execution in structured tasks.
Proactive Data Validation in SQL (Q501)
A defining characteristic of DeepSeek V3.1 is its tendency to proactively verify assumptions before proceeding, such as validating region codes (Q501) or re-checking schema when results seem implausible. This systematic verification and value checking prevents silent failures and highlights its robust agentic reliability.
10/30
Q503 Failures due to Distractors
DeepSeek V3.1, despite its overall strength, is still vulnerable to semantic confusion from distractors, failing 10 out of 30 Q503 trials by fixating on irrelevant tables or incorrect aggregation logic. This demonstrates sensitivity to context pollution.
Context Fragility: Misinterpreting CWD (Q202)
In Q202, Llama 4 Maverick, after seeing the 'get_cwd' output, fixated on the current working directory as the target for file creation, forgetting the absolute path from initial instructions. This demonstrates a severe vulnerability to context pollution and distraction, leading to critical misinterpretations of the environment.
2/30
Q402 Successes (Highly Inconsistent)
Llama 4 Maverick's performance is highly inconsistent; it often begins tasks with correct reasoning but frequently degrades mid-execution, leading to malformed tool calls, generation loops, and inconsistent recovery. This highlights its fragile execution under cognitive load.
Overconfidence
Implicit Substitutions Violate Fidelity
Maverick regularly asserts incorrect assumptions with high confidence and often fills in missing entities implicitly rather than verifying them via schema inspection or tool usage. This over-helpfulness leads to autonomous substitution, violating task fidelity, likely due to alignment tuning.
1. Neither Size Nor General Capability Equal Agentic Reliability
Model scale alone does not predict agentic robustness. For instance, Llama 4 Maverick (400B) performs only marginally better than Granite 4 Small (32B) in some uncertainty-driven tasks, and DeepSeek V3.1's superior reliability derives from post-training reinforcement learning rather than raw scale.
2. Error Feedback Is The New Frontier For Autonomy
Agentic reliability is determined not by error absence, but by how effectively errors are converted into corrective action. Models must internalize tool semantics and system constraints, and error messages should suggest corrective paths.
3. Context Quality Matters More Than Context Quantity
The presence of distractor files or tables triggers semantic overreach, causing systemic failures even in the largest models. All provided context is treated as signal, not noise, highlighting the need for careful context engineering and avoidance of irrelevant or confusing data.
4. Literal, Source-of-Truth Alignment
Enterprise agents must prioritize actual data over their priors. Behaviors like guessing schemas or hypothesizing values must be inhibited. Grounded verification is essential for correctness under uncertainty, achievable through RL finetuning, system prompts, and tool message design.
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Your Agentic AI Implementation Roadmap
A strategic approach to integrating reliable agentic AI, leveraging insights from success and failure patterns.
Phase 1: Discovery & Strategy Alignment
Identify high-impact agentic use cases, assess existing infrastructure, and define clear success metrics. Focus on scenarios where interactive grounding and error recovery are critical.
Phase 2: Pilot Development & Custom Tooling
Develop initial agentic pilots with carefully designed tools and prompts that mandate verification and constraint discovery. Prioritize tools that provide rich, actionable error feedback.
Phase 3: Iterative Evaluation & Refinement
Deploy agents in controlled environments and use interactive, multi-step benchmarks (like KAMI) to identify failure modes. Refine models and prompts based on recovery behavior, not just initial accuracy.
Phase 4: Scaled Deployment & Continuous Monitoring
Scale successful pilots, implementing architectural safeguards like verification checkpoints and runtime anomaly detection. Continuously monitor agent performance and adapt to new environmental constraints.
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