Agent Limitations
Understand constraints and design for reliability in AI agent systems
Your Progress
0 / 5 completedLimitation-Aware Architecture
Real production systems that embrace limitations and design around them. Learn from companies building reliable agents at scale.
📖 Production Case Studies
Select an example to see how real systems handle agent limitations
GitHub Copilot: Constrained Autocomplete
Limitation Embraced: Hallucination
Strategy: Treat output as "suggestions," not "answers." User remains in control, reviews every line before accepting.
Limitation Embraced: Context Limits
Strategy: Only include nearby files in context (~20 files, not entire repo). Prioritize open tabs and imports.
Limitation Embraced: Cost
Strategy: Debounce requests (wait 100ms after typing stops). Cache similar completions. Use smaller models where possible.
🏗️ Core Design Patterns
1. Human-in-the-Loop
Don't automate end-to-end. Put humans at decision points. Show diffs, require approval for risky actions.
2. Tiered Models
Cheap models for simple tasks, expensive for complex. Let users choose quality vs. speed trade-off.
3. Constrained Scope
Narrow task definitions prevent failure modes. "Fix typos" > "Improve writing" > "Write essay."
4. Resource Budgets
Hard limits on tokens, time, iterations. Prevent runaway costs and infinite loops.
💡 The Pattern
Notice what these successful systems have in common: They don't fight limitations—they design around them.
Copilot doesn't try to prevent hallucination; it makes reviewing suggestions effortless. Cursor doesn't solve context limits; it gives users control. Notion doesn't achieve 100% accuracy; it constrains tasks to where 90% is good enough.