Close the Loop Automatically
Most teams fix agent issues manually — listening to calls, rewriting prompts, re-testing. This course teaches you to build a loop that does that continuously: eval results feed into diagnosis, diagnosis feeds into prompt rewrites, rewrites get validated automatically.
Works With Your Stack
The patterns cover VAPI agents (squads + tool definitions), ElevenLabs Conversational AI, and self-hosted pipelines (Pipecat, custom WebSocket). Bring your own agent — the framework adapts.
Ship Faster, Break Less
Self-improving loops catch regressions before users do. You'll learn to set up rubrics, run batches, and surface root causes so every deploy is backed by data, not gut feel.
About the Course
Voice agents break in subtle ways — latency spikes, hallucinated tool calls, personality drift mid-conversation. Fixing them manually doesn't scale. This course walks you through building self-improving loops: automated cycles that evaluate your agent against real scenarios, diagnose failures, propose prompt changes, and re-validate — all without manual intervention. You'll go from "listen to 50 calls and guess what went wrong" to a system that tightens itself with every batch. Built by the Cekura team, based on patterns we've seen across hundreds of agent deployments.
What You'll Learn
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Welcome to Cekura University
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How to connect an Agent
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Writing Test Cases
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Dynamic Variables
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Mock Tools
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Running Evaluations and Reading Results
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Folders and Evaluators
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Running Test Suites
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Test Generation
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Observability and Ingestion
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Predefined vs Custom vs LLM Judge Metrics
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Workflow Metrics and Accuracy Loop
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Alerts and Dashboards
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Cekura MCP No-UI
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Skills Demo
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Self-Improving Agent Loop
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Ready to Level Up? Start Today.
Get the exact framework Cekura uses to help teams ship better voice agents. No cost, no fluff, just the patterns that work.