Agent Limitations
Understand constraints and design for reliability in AI agent systems
Your Progress
0 / 5 completedFailure Mode Simulator
Experience how agents fail in real-world scenarios. Select a failure mode and trigger it to understand why it happens and how to mitigate it.
🎯 Select Failure Scenario
🚨 Why Failures Happen
- • Statistical Models: LLMs predict tokens, don't verify truth
- • No Self-Awareness: Agents don't know when they're wrong
- • Constraint Ignorance: No built-in cost/context budgets
- • Lack of Planning: Can't anticipate multi-step failures
✅ How to Prevent Failures
- • Validation Layers: Check outputs with deterministic code
- • Resource Limits: Set max tokens, time, iterations
- • Human Oversight: Flag risky operations for review
- • Graceful Degradation: Have fallback when agents fail
💡 Design Principle
Assume failure will happen. Don't ask "how do I prevent all failures?" Ask "when the agent fails, what's the worst that can happen?" Design your system so that the answer is "not much." This is the difference between fragile and robust agentic systems.