Key Takeaways
- Rules belong in code, not prompts: LLMs are probabilistic—they can’t guarantee 100% rule compliance. Extract business logic into deterministic validation functions for reliable, auditable decisions.
- Security is multi-layered: Input guardrails (PII, jailbreak detection) + business logic validation + output filtering create defense in depth. One layer isn’t enough for production.
- Tool selection accuracy degrades with scale: 5 tools = 92% accuracy, 20+ tools = 58% accuracy. Use hierarchical organization, retrieval, or consolidation to maintain high accuracy at scale.
- Measure everything: Track tool selection accuracy, business rule compliance, security detection rates. You can’t optimize what you don’t measure.
- Cost vs. accuracy trade-offs: More guardrails = higher cost/latency but better reliability. Production systems balance these based on risk tolerance and budget constraints.
Production Checklist
Before deploying agents with business rules to production:- All critical business rules extracted into deterministic code
- Validation tools return consistent results (same input = same output)
- Pre-execution guardrails prevent unauthorized actions
- Post-execution validation catches invalid results
- PII detection implemented for both input and output
- Jailbreak detection catches common attack patterns
- Tool selection accuracy measured and optimized (target: >90%)
- Tool usage analytics track selection accuracy and performance
- Error responses are structured and actionable (never throw to agent)
- All validation logic is unit tested and auditable
- Logging covers security events and policy violations
- Cost per query measured and optimized
Common Pitfalls Recap
❌ Rules in prompts: LLMs interpret rules probabilistically, leading to inconsistent enforcement❌ No input sanitization: PII leakage and jailbreak vulnerabilities
❌ Flat tool lists: 20+ tools = 58% accuracy, agents get confused
❌ No measurement: Can’t improve tool selection without metrics
❌ Over-optimizing early: Start with working system, then optimize based on data
❌ Security theater: Guardrails that only check happy paths aren’t effective
Real-World Impact
Case Study: Insurance Claims Processing- Before: 78% accuracy with prompt-based rules
- After: 99.7% accuracy with deterministic validation
- Result: 93% processing time reduction, 98% compliance improvement
- Before: 15 tool calls per query, $2.20 cost
- After: 3 tool calls per query, $0.15 cost
- Result: 80% latency reduction, 93% cost reduction
- Before: No systematic PII detection
- After: 97% PII detection rate, 3% false positives
- Result: Zero compliance violations, successful audit
Learn More
Official Documentation
- Microsoft Presidio - PII detection and anonymization
- NeMo Guardrails - Conversation control and safety
- Guardrails AI - Validation framework
- AWS Bedrock Guardrails - Managed guardrails service
Research Papers
- Tool Space Interference - Microsoft Research on tool design at scale
- ToolRAG - Retrieval-augmented tool selection
- Iterative Tool Selection - Hierarchical organization patterns
- Code Execution with MCP - Universal code tools
Security & Compliance
- OWASP LLM Top 10 - LLM security risks
- LlamaGuard - Content safety classification
- Microsoft Prompt Shields - Jailbreak detection
Tools & Frameworks
- Pydantic - Data validation library
- Guardrails AI Validators - Pre-built validation rules
- LangChain Expression Language - Workflow composition
Case Studies
- Tyson Foods AI Agents - Production multi-agent system
- Salesforce Agentforce - Enterprise agent platform
- Taming AI Agents Accuracy with Digibee - Real-world accuracy techniques
Community
- Agents.md - Comprehensive agent guide
- GenAI Agents - Open-source examples
- LangChain Discord - Community discussions