Agent Limitations
Understand constraints and design for reliability in AI agent systems
Your Progress
0 / 5 completedLimitation Mastery Checklist
You've learned how agents fail and how to design around it. Here's your implementation checklist and quick reference guide.
📋 Limitation Quick Reference
| Limitation | Root Cause | Best Mitigation | When to Worry |
|---|---|---|---|
| Hallucination | Statistical prediction, no truth verification | RAG + validation layers + citations | Critical facts (medical, legal, financial) |
| Context Limits | Fixed token windows (128K-200K) | Semantic search + rolling windows | Large codebases, long conversations |
| 90-95% Ceiling | Model capability limits, non-determinism | Human-in-the-loop for final decisions | High-stakes automation (payments, deployments) |
| Infinite Loops | No self-awareness, poor planning | Max iterations + progress metrics | Multi-step autonomous tasks |
| Cost Explosion | No built-in budget awareness | Token limits + tiered models + caching | High-frequency operations, large-scale batch jobs |
✅ Pre-Production Checklist
Before deploying any agent system, verify you have:
🛡️ Safety Guardrails
- ☐ Output validation (schema checks, regex patterns)
- ☐ Confidence thresholds for autonomous actions
- ☐ Human review for high-risk operations
- ☐ Rollback mechanisms for agent mistakes
💰 Cost Controls
- ☐ Per-request token budgets enforced
- ☐ Response caching implemented
- ☐ Tiered models (cheap for simple, expensive for complex)
- ☐ Cost monitoring and alerts configured
⏱️ Reliability Safeguards
- ☐ Timeouts set for all LLM calls
- ☐ Retry logic with exponential backoff
- ☐ Graceful degradation when agents fail
- ☐ Max iteration limits to prevent infinite loops
📊 Observability
- ☐ Logging all prompts and completions
- ☐ Tracking accuracy metrics over time
- ☐ Failure mode categorization and analysis
- ☐ User feedback collection mechanism
❌ Common Mistakes
- • Expecting 100% accuracy (impossible with current tech)
- • Not budgeting for token costs (can spiral quickly)
- • Automating without human review (agents will fail)
- • Ignoring context limits (lost-in-the-middle effect)
- • No fallback when agents error (brittle systems)
✅ Success Patterns
- • Design for 90% accuracy, handle the 10% gracefully
- • Human-in-the-loop at decision points
- • Constrained scopes (narrow tasks work better)
- • Resource budgets enforced automatically
- • Monitoring and iteration based on real failures
🎯 The Ultimate Takeaway
Limitations aren't bugs to fix—they're constraints to design around.
The difference between production-ready agents and prototypes is accepting that agents will fail and building systems that remain useful despite those failures.
Copilot doesn't prevent hallucination—it makes reviewing suggestions effortless. Cursor doesn't solve context limits—it gives users control. Notion doesn't achieve perfection—it constrains scope to where 90% is enough.
Master limitations, and you'll build agents that actually work in production. Fight them, and you'll waste months chasing impossible perfection.