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Agent Limitations

Understanding the hard constraints and failure modes of AI agent systems

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Deep Dive: Limitation Categories

Let's examine each limitation category in technical depth—understanding not just what fails, but why it fails and how to design around it.

🔬 Limitation Deep Dive

Select a category to explore its root causes and mitigation strategies

🧠 Reasoning Failures: Root Causes

1. No World Model

LLMs don't build internal representations of reality. They predict tokens based on statistical patterns, not causal understanding.

Example: An agent might suggest "use more RAM to speed up network requests" because these words co-occur in training data, despite being causally unrelated.
2. Hallucination is Fundamental

The same mechanism that enables creativity (generation) guarantees hallucination. You can't eliminate one without losing the other.

Mitigation: Retrieval-Augmented Generation (RAG), fact-checking layers, confidence thresholds, citation requirements.
3. Training Distribution Dependency

Performance degrades on problems outside training distribution. Novel edge cases break even well-prompted agents.

Design Principle: Scope agents to problems similar to training data. For novel domains, require human review.
🎯 Production Strategy
  • • Accept 5-10% failure rate as baseline, design for graceful degradation
  • • Use Chain-of-Thought to expose reasoning for human review
  • • Validate critical outputs with deterministic checks (regex, schemas)
  • • Log failures to identify systematic reasoning gaps

⚖️ Limitation Impact Comparison

Not all limitations are equal. Some are hard walls, others are soft constraints you can work around.

💡 Key Insight

Understanding WHY limitations exist is more valuable than memorizing WHAT they are. Root causes inform design decisions. Hard walls require architecture changes. Soft constraints can be optimized. Know the difference and design accordingly.