The demo trap
Every AI demo looks impressive. A prompt goes in, a coherent response comes out, and stakeholders get excited about the possibilities. But demos hide the hard parts: error handling, edge cases, rate limits, cost management, and user trust.
Production AI systems face challenges that never appear in demos:
- What happens when the model hallucinates confidently?
- How do you handle the 5% of inputs that break your carefully tuned prompts?
- Who's responsible when the AI gives incorrect advice?
- How do you know if the system is degrading over time?
These questions don't have prompt-engineering answers. They require systems thinking.
Why simple LLM wrappers fail
The simplest AI integration is a thin wrapper: user input → LLM → response. This works for chatbots with low stakes, but fails when:
- Context is complex: The LLM needs information from multiple sources—databases, documents, APIs—that exceeds context windows
- Tasks are multi-step: The user's goal requires a sequence of actions, not a single response
- Stakes are high: Mistakes have real consequences (legal, financial, operational)
- Scale matters: 10,000 daily users reveal edge cases that 10 beta users never found
RAG: The foundation of knowledge-aware AI
Retrieval-Augmented Generation (RAG) addresses the context problem by fetching relevant information at query time. But production RAG requires attention to:
RAG Architecture Decisions
- Chunking strategy: How you split documents affects retrieval quality. Semantic chunking often beats fixed-size windows.
- Embedding model selection: Different models have different strengths. Domain-specific fine-tuning matters.
- Reranking: Initial retrieval is noisy. A reranker improves precision but adds latency.
- Hybrid search: Combining semantic and keyword search catches cases where embeddings fail.
The quality of your RAG pipeline often matters more than your choice of LLM.
Agents: When AI needs to take action
Agentic AI goes beyond Q&A. An agent can call tools, execute multi-step workflows, and make decisions. This power comes with complexity:
- Tool calling: The agent needs well-defined tools with clear schemas. Vague tools lead to vague behavior.
- Orchestration: Multi-step tasks need planning. Simple chains break; you need recovery mechanisms.
- State management: Long-running agents need to track progress and resume from failures.
- Cost control: Agents can loop indefinitely. Build in budget limits and iteration caps.
Guardrails: The invisible safety net
Production AI needs multiple layers of protection:
Essential Guardrails
- Input validation: Reject or sanitize inputs that could cause problems (prompt injection, PII exposure)
- Output filtering: Block responses that violate policies before they reach users
- Confidence scoring: Route low-confidence responses to human review
- Citation requirements: Force the model to cite sources for factual claims
- Semantic validation: Check that responses actually answer the question asked
Human-in-the-loop: Not a fallback, a feature
The best AI systems know their limits. Human-in-the-loop patterns aren't admissions of failure—they're design decisions:
- Approval workflows: High-stakes actions require human sign-off
- Escalation paths: When the AI is uncertain, it asks for help rather than guessing
- Feedback loops: Human corrections improve the system over time
- Audit trails: Every AI decision is logged with the context that led to it
Evaluation: Measuring what matters
You can't improve what you can't measure. Production AI needs systematic evaluation:
- Golden datasets: Curated test cases with known-good answers
- Automated evaluation: LLM-as-judge patterns for scalable quality assessment
- User feedback signals: Thumbs up/down, regeneration requests, task completion rates
- Latency and cost tracking: Per-request metrics that catch regressions
- Drift detection: Alerts when behavior changes unexpectedly
Trade-offs to acknowledge
Every architectural decision has trade-offs. Be honest about them:
- More guardrails = higher latency and more false positives
- Larger context windows = higher costs and potential for confusion
- Human review = slower end-to-end time but better accuracy
- Fine-tuning = better performance but harder maintenance
The right balance depends on your specific use case, user expectations, and risk tolerance.
When NOT to use agentic AI
Sometimes the answer isn't more AI. Consider alternatives when:
- The task is deterministic and well-understood (use rules or workflows)
- Errors are unacceptable (use human experts)
- The training data doesn't represent your domain (the model will hallucinate)
- You can't afford to monitor and maintain the system (AI requires ongoing attention)
Conclusion
Production agentic AI is systems engineering. The model is just one component—often not even the hardest part to get right. Success requires thoughtful architecture around orchestration, guardrails, observability, and human oversight.
If you're evaluating AI for your organization, look beyond the demo. Ask vendors how they handle failures. Ask about their evaluation methodology. Ask who's responsible when things go wrong.
