Real-time agent performance, alerting, and SLA tracking in OpenAI Agents SDK agents — without rewriting your code, without adding a proxy to the request path.
The problem
OpenAI Agents SDK is great at what it does, but production-grade monitoring is bring-your-own. Most teams discover this when the first production incident hits, the first OpenAI bill arrives, or the first auditor asks for evidence.
Specifically:
- Built-in tracing is OpenAI-platform-bound and developer-focused
- No native cross-vendor model support in traces
- Policy enforcement (approval flows, spend caps) is bring-your-own
How Prefactor solves it
Prefactor wraps your OpenAI Agents SDK Agent and adds monitoring as a runtime layer. Specifically:
- alert on quality regression after a prompt change
- alert when p95 latency exceeds SLA
- monitor agent error rates per environment
- track success rates per intent
- dashboard for on-call view of agent health
Install and integrate
pip install prefactor-openai-agentsfrom openai_agents import Agent, run
from prefactor import Prefactor
pf = Prefactor(api_key="pf_live_...")
agent = pf.wrap(Agent(...), agent_id="customer-support-v1")
result = await run(agent, input="...")Specific use cases
- Alert on quality regression after a prompt change
- Alert when p95 latency exceeds sla
- Monitor agent error rates per environment
- Track success rates per intent
- Dashboard for on-call view of agent health
FAQ
Does this add latency? Per-span instrumentation adds ~2-5ms. Telemetry ships asynchronously. No synchronous network call in the agent's hot path unless you enable blocking policy enforcement.
Can I configure this per-agent? Yes. Monitoring settings are per-agent — different agents can have different policies, sampling rates, retention, etc.
Is this compatible with other observability tools? Yes. Prefactor exports OpenTelemetry; works alongside Datadog, Langfuse, LangSmith, and others.
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