Core Agent Capabilities
Explore what modern AI agents can do: from tool use to complex reasoning
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0 / 5 completedKey Takeaways
Let's distill everything we've learned about agent capabilities into actionable insights you can apply immediately.
💎 Five Core Insights
1. Capabilities Compound
An agent missing even one core capability (reasoning, tool use, memory, planning) will fail complex tasks. Don't think of capabilities as optional—think of them as compounding multipliers.
2. Constraints Are Features, Not Bugs
The 90-95% accuracy ceiling, context windows, and cost limits aren't problems to solve—they're reality to design around. The best systems embrace constraints from day one.
3. Scope Before Scale
Don't build a general-purpose agent. Start with the minimum viable capability set for your specific use case. GitHub Copilot succeeds by staying in its lane—code completion, not architecture.
4. Guardrails > Capabilities
Limiting what agents can do is more important than expanding what they can do. Max iterations, read-only tools, confidence thresholds, and human escalation prevent catastrophic failures.
5. Fallbacks Are Mandatory
Every capability will fail. Plan for it. Human escalation, graceful degradation, clear error messages, and logging aren't nice-to-haves—they're requirements for production systems.
� Capability Cheat Sheet
Quick reference for designing around capability constraints
| Capability | Best For | Watch Out For | Design Pattern |
|---|---|---|---|
| 🧠 Reasoning | Multi-step logic, synthesis | Hallucination, no common sense | Chain-of-Thought, validate outputs |
| 🛠️ Tool Use | API calls, data retrieval | Tool overload, unclear docs | Limit count, crystal-clear descriptions |
| 💾 Memory | Context, history, knowledge | Context window limits, retrieval accuracy | RAG, summarization, hybrid approach |
| 📋 Planning | Multi-step execution | Infinite loops, short horizon | Max iterations, exit conditions, ReAct |
🚀 Quick Start: Building Your First Agent
Define Success Criteria
What accuracy do you need? What latency is acceptable? What's your cost budget?
Start Minimal
Begin with reasoning only. Add tool use if needed. Add memory if context matters. Add planning only for complex workflows.
Build Guardrails First
Max iterations, timeouts, cost limits, confidence thresholds. Constraints prevent disasters.
Test Against Failures
Don't just test happy paths. What happens when the LLM hallucinates? When tools fail? When memory fills up?
Iterate Based on Real Usage
Log everything. Where do agents fail? What capability is the bottleneck? Adjust constraints based on data, not intuition.
📚 Continue Learning
Next Module
Explore Current Limitations to deepen your understanding of agent systems.
Go to Current Limitations →Practice
Apply what you've learned by building your own capability-aware agent system.
• Add capabilities incrementally
• Test against edge cases
🎓 Final Thought
Understanding agent capabilities isn't about memorizing what's possible—it's about developing intuition for what's reliable. The difference between a prototype and a production system is respecting constraints. Build agents that work with their limitations, not against them.