Tracing production agents inside closed runtimes that emit no telemetry
Evaluation tells you whether an agent was ready on release day. Observability tells you whether it is still behaving on live traffic. Most enterprises have the first and assume they have the second.
However, the failures that dominate production rarely show up in the agent’s reply: exhausted context, missing resources, timeouts and stale state. They only appear in a trace. Agents running inside vendor runtimes such as DevRev, Glean and ElevenLabs emit no trace at all, so the dashboard stays empty and the agent looks healthy.
Roughly three-quarters of enterprises now require monitoring and failure alerting before an agent can be deployed, and nearly two-thirds cite insufficient observability as a barrier to wider adoption.
This whitepaper gives platform engineers and AI leaders a proven pattern for rebuilding the trace from outside a closed runtime, without modifying the agent. QKTech’s Observability Engineering practice and partnerships with Arize and DevRev bring this pattern to production agent deployments.
What’s Inside
- Why evaluation and observability answer different questions, and why you need both
- How production agents actually fail, and why output-only checks miss most of it
- The bridge pattern: tracing agents in closed runtimes without touching the agent
- What tracing revealed across six production agents on DevRev, Glean and ElevenLabs
- How to keep continuous scoring affordable at scale, and why detection should run rules-first
Who Should Read This
- Platform, SRE and observability engineers running AI agents in production
- Teams building on closed agent runtimes such as DevRev, Glean and ElevenLabs
- AI engineering leads selecting or evaluating observability platforms
- CIOs and architects budgeting for continuous AI monitoring
Download the whitepaper to access the bridge architecture, the reference production stack, and the cost-governed evaluation approach that keeps agents observable from release to scale.
