Core Agent Capabilities
Explore what modern AI agents can do: from tool use to complex reasoning
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How do successful teams design agents around capability constraints? Let's examine production systems that work within—not against—agent limitations.
🏢 Production Case Studies
Select a real-world system to see how they handle capability constraints
GitHub Copilot: Code Completion
Capability Design
- •Reasoning: Pattern matching on code context (limited to ~10-20 lines)
- •Memory: Short context window—only sees current file + imports
- •No Planning: Single-shot completions, not multi-file refactors
- •No Tool Use: Can't run code or access external systems
Design Decisions
- ✓ Fast completions (200-500ms) prioritized over perfect accuracy
- ✓ Stateless design—no learning from user feedback within session
- ✓ Human always in control—suggestions, not autonomous execution
- ✓ Scope limited to what it does well: code patterns, not architecture
Why It Works
Copilot succeeds by accepting its limitations. It doesn't try to be a full agent—it's a capability-constrained assistant that stays in its lane. Users know it will suggest code, not refactor entire codebases.
🎯 Capability-Aware Design Framework
Use this framework to design your agent system around capability constraints:
1. Define Success Criteria
What does "good enough" look like?
- • 90% accuracy acceptable? 99% required?
- • Latency tolerance: seconds or milliseconds?
- • Cost budget per interaction?
2. Map Required Capabilities
Which capabilities does your use case need?
- • Complex reasoning vs. simple pattern matching?
- • External tool calls necessary?
- • Long-term memory or stateless?
- • Multi-step planning or single actions?
3. Design Constraints & Guardrails
How will you limit agent behavior?
- • Max iterations to prevent infinite loops
- • Read-only vs. write permissions for tools
- • Confidence thresholds for escalation
- • Cost/time budgets per request
4. Build Fallback Strategies
What happens when capabilities fail?
- • Human escalation paths
- • Graceful degradation (simple response if complex fails)
- • Clear error messages to users
- • Logging for post-mortem analysis
⚡ Key Insight
The best production agent systems aren't those that maximize capabilities—they're those that carefully scope capabilities to match the problem. Start with the minimum viable capability set, then add more only when constraints become bottlenecks.