Modern conversational AI agents, whether voice or chat, can’t rely on manual QA. Reviewing call recordings, clicking through chat transcripts, or running ad-hoc spot checks is slow, inconsistent, and error-prone. Teams building production-grade agents need automation to test at scale, catch failures before customers do, and continuously improve performance.
Cekura is purpose-built for this.
Why Automating AI Agent Evaluation Matters
AI agents face challenges across their lifecycle:
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During development: Component testing to validate each part.
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Before launch: End-to-end simulations to prevent regression failures.
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In production: Continuous monitoring to detect issues without manually listening to thousands of calls.
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At scale: Safe model swaps, prompt iterations, and compliance checks without breaking existing workflows.
Cekura replaces fragile manual QA with automation-first evaluation, enabling companies to launch reliable agents in minutes, not weeks.
Core Capabilities of Cekura’s Automated Evaluation
1. Scenario Generation
Automatically generate diverse test cases from your agent description or prompt. Cover edge cases like accents, background noise, one-word answers, impatience, or broken English.
2. Metrics-Driven Evaluation
Evaluate performance using pre-defined, industry-specific, and custom metrics:
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Instruction following
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Latency and interruptions
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CSAT and sentiment
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Voice quality and speech clarity
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Tool-call accuracy
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Compliance (HIPAA, PCI DSS)
3. Custom Personas & Real-World Simulation
Simulate real users with varied accents, tones, and conversational quirks. For example:
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Hannah, Female, American accent
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Ananya, Female, Indian accent
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Nick, Male, German accent, impatient
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Chris, Male, British accent
4. Regression Testing & A/B Testing
Compare new prompts, models, or infrastructure. See which version performs better across 30+ calls, with detailed metrics on success rates, latency, and talk ratio.
5. Real-Time Observability & Alerts
Monitor production calls as they happen. Cekura highlights drop-offs, missed intents, latency spikes, or instruction failures. Alerts integrate with Slack, making it possible to catch issues before customers complain.
6. Continuous Automation
Set up cron jobs to run tests on a schedule, automatically replaying scenarios and logging results. Even better, failed real-world calls can be converted into new evaluators, turning user pain points into future test cases.
7. Enterprise-Grade Readiness
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In-VPC deployment
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SSO & role-based access controls
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24/7 support & custom integrations
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API-driven testing for tool calls and workflows 
Manual QA vs. Automated Evaluation with Cekura
| Aspect | Manual QA (Traditional) | Automated AI Agent Evaluation with Cekura |
|---|---|---|
| Coverage | Limited spot checks, often misses edge cases | Thousands of automatically generated scenarios covering accents, interruptions, tool calls, and compliance |
| Speed | Slow—weeks of manual reviews | Launch reliable agents in minutes, not weeks |
| Scalability | Not scalable; requires large QA teams | Test and monitor unlimited conversations across voice and chat simultaneously |
| Consistency | Subjective human judgment | Metric-driven evaluation: instruction following, latency, sentiment, CSAT, compliance |
| Regression Testing | Painful and error-prone | Automated replays ensure updates don’t break existing workflows |
| A/B Testing | Difficult to compare models or prompts | One-click A/B testing across prompts, models, or infrastructure |
| Production Monitoring | Manual review of random calls | Real-time observability with proactive alerts on errors, drop-offs, or latency spikes |
| Continuous Improvement | Depends on ad-hoc human feedback | Failed real-world calls auto-convert into new evaluators for future runs |
| Enterprise Readiness | Not secure, no standardization | In-VPC deployment, SSO, RBAC, API integrations, 24/7 enterprise support |
Benefits of Automated Agent Evaluation with Cekura
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Faster Launches: Cut testing cycles from weeks to minutes.
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Proactive Reliability: Identify failures before they reach production.
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Data-Driven Improvement: Metrics highlight exactly where and why agents fail.
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Safe Scaling: Roll out model or prompt changes without breaking what works.
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Cross-Channel Coverage: Unified evaluation for both voice and chat agents.
Who Uses Cekura?
Cekura supports 70+ teams across industries - from healthcare (HIPAA compliance) to banking (PCI DSS), retail, and enterprise contact centers.
Customers rely on it to:
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Validate workflows (bookings, returns, transfers)
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Ensure compliance in regulated sectors
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Detect hallucinations in real-time conversations
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Continuously tune prompts for higher accuracy
Ready to Automate Your AI Agent Evaluation?
With Cekura, you can build, test, and monitor AI agents that scale reliably.
Learn more on Cekura.ai Book a demo