How to Build Production AI Agents That Don't Break

The Reality of Production AI Agents

Building a demo AI agent is easy. Deploying one that handles 10,000 requests daily without catastrophic failures? That's engineering.

After deploying dozens of AI agent systems for clients across healthcare, finance, and e-commerce, we've learned what separates toy demos from production-grade systems.

Principle 1: Every LLM Call Must Be Observable

AI agents are non-deterministic. The same input can produce different outputs. Without comprehensive observability, debugging production issues becomes a nightmare.

What to log:

Full prompts sent to the model

Complete responses received

Token counts and latency

Any retrieved context (for RAG)

Tool calls and their results

Final agent decisions

**Tools we recommend:** LangSmith, Helicone, or custom logging to your observability stack.

Principle 2: Implement Semantic Guardrails

LLMs can hallucinate, go off-topic, or produce harmful content. Guardrails are non-negotiable.

Types of guardrails:

**Input validation:** Reject prompts that attempt jailbreaking

**Output validation:** Check responses against business rules

**Semantic boundaries:** Ensure responses stay within defined topics

**PII detection:** Prevent accidental data exposure

Principle 3: Design for Graceful Degradation

LLM APIs fail. Rate limits hit. Networks timeout. Your agent must handle these gracefully.

Patterns:

Implement exponential backoff with jitter

Have fallback responses for common failures

Cache responses where appropriate

Circuit breakers for repeated failures

Principle 4: Human-in-the-Loop by Default

For critical decisions, always have an escape hatch to human review.

Confidence thresholds that trigger human escalation

Easy override mechanisms

Audit trails for all AI decisions

Principle 5: Test with Production Traffic Patterns

Your agent will encounter inputs you never imagined. Test with:

Edge cases and adversarial inputs

High volume stress tests

Real user behavior (shadow mode deployment)

Conclusion

Production AI agents require the same rigor as any critical system—plus additional considerations for non-determinism and model behavior. Build observability first, implement guardrails early, and always have fallback paths.