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Monitor Synthflow voice agents in production: what Synthflow logs natively, how to forward calls to Cekura, and which metrics to set. Get the field mapping.

How to test voice agents for prompt injection: spoken attacks, tool and knowledge base payloads, multi-turn escalation and pass rules. Get the six-step plan.

Test voice AI agents from your IDE in Cursor, VS Code or Claude Code: LiveKit and Pipecat local tests, Cekura MCP, CLI and CI gates. Get the 5-layer table.

Average speed of answer explained: the ASA formula, what to include in it, why it hides your longest waits, the Medicare rules, and what changes with AI agents.

Datasets for training and evaluating RAG models by job: retriever training, embedding choice and benchmarks, plus an 8-step plan to build your own test set.

Drift detection for voice AI agents: score every production call you send in, alert when a metric moves away from its EWMA baseline, and tag calls by agent version. How Cekura does it, and what it costs.

LLM tracing records every model call, tool call and retrieval as linked spans. Learn what to capture, how to sample, and why a trace alone won't find the bug.

No-code voice agent testing tools import your agent, generate simulated test calls, and score them against plain-English criteria. Cekura does this from a dashboard at $0.25 per voice testing minute on Pay as you go, first seat free.

Queue management explained: physical, virtual and call queues, the math behind waits (Little's Law, Erlang C), and a checklist to test your call queue.

Real-time voice agent call monitoring: what the platform shows mid-call, what Cekura scores on each completed call, and how failure, trend and threshold alerts reach Slack.

Virtual agent vs chatbot: see how each decides, acts and fails, what research says about reliability, when to pick each, and a repeat-run test plan to use.

Voice agent tracing splits each turn into STT, LLM, tool and TTS spans to show which stage failed. Cekura ties those traces to scored test and production calls.