Production AI Systems

Shipping AI agents to production on cloud platforms requires the same discipline as any service: SLOs, guardrails, and deployment automation.

Operational concerns

Concern	Approach
Safety	Policy filters, tool allowlists, human escalation
Latency	Streaming tokens, caching frequent retrievals
Cost	Token budgets, model routing (small vs large models)
Quality	Offline evals, online feedback, regression suites

Instrumented agent call

const response = await agent.run({
  input: userMessage,
  maxSteps: 8,
  onToolCall: traceTool,
});

Logging rules

• Log prompt hashes, tool names, latencies, and outcome — not raw secrets or PII
• Export traces to your observability stack
• Version prompts and tools like open-source releases

Related guides

• RAG pipelines
• Kubernetes — scale agent APIs