AI engineering is more than writing good prompts. It's standing up a system where every LLM call is routed, versioned, evaluated, monitored, and governed - with observability that explains what happened. Here's the full stack, and how the pieces fit together.
1. The gateway and routing
Every request should go through one OpenAI-compatible gateway. Routing modes (balanced, quality-first, cost-optimized, speed, throughput), fallback chains, and circuit-breakers keep traffic flowing even when one provider degrades. Auto-routing picks a model per request based on prompt, capabilities, and cost.
2. Prompt management as code
Prompts are code. Store them in prompt repositories, version them with prompt git, review in pull requests, and deploy by push. Prompt management gives every model behavior the same review cycle as every feature.
3. Evaluation with LLM evals and AI judges
Before a prompt ships, score it. LLM evals - boolean, range, categorical, comparative judges - plus code and deterministic evaluators catch regressions. Regression gates block merges when quality drops, and online evals score live traffic.
4. LLM observability and AI monitoring
After it ships, watch it. LLM observability logs every request with latency, cost, tokens, model, provider, and status. LLM traces follow multi-step agent and RAG workflows. AI monitoring alerts on latency, error rate, cost, and quality drift.
5. Security and governance
Guardrails at the edge - threat detection, content moderation, budgets, IP allowlists, and compliance routing - plus full audit trails. Governance lets teams move fast without leaking data or blowing budgets.
The unified stack
The key insight is that these aren't separate tools. When prompt management, evaluation, and observability share one platform, you can trace a quality regression back to the exact prompt commit and roll it back. That closed loop is what makes production LLM systems sustainable.
# The closed loop
routing → prompt management → evaluation → observability → rollback
↑ │
└────────────────────────────────────────────────────────────────────┘Production AI engineering is a loop: route, version, evaluate, observe, and improve - every time.
Getting started
Infere brings the whole stack together: a universal gateway, Git-native prompt management, LLM evals and AI judges, LLM observability and monitoring, and edge security. Start with routing, then layer in evaluation and observability as you scale.
Understanding the full cost picture matters as you scale - see our breakdown of the true cost of agentic coding to keep spend predictable alongside engineering quality.