How to Measure and Improve Conversational AI Reliability with Cekura

Reliability is the core requirement for any conversational AI system. Users expect accurate answers, steady behavior across sessions, safe interactions, and predictable reasoning. To meet that standard, teams need tools that expose weaknesses, track improvements, and validate performance across the full range of real conversations.

Cekura gives you a complete environment for evaluating conversational reliability. It checks factual grounding, consistency, safety, robustness, and reasoning quality by running controlled conversational tests and scoring the results with a mix of structured metrics, simulated personas, and LLM evaluators. This helps you understand how your agent behaves across long dialogues, complex states, and unexpected user paths.

Below is a breakdown of the reliability dimensions and how Cekura supports each one.

Factual Accuracy and Grounding

Reliable agents stay truthful and avoid unsupported claims. Cekura evaluates factual strength by:

• Detecting hallucinations against your knowledge base

• Checking whether responses follow the instructions and allowed sources in the agent description

• Flagging deviations from expected workflows

• Running LLM-as-a-judge metrics tuned to your domain

If an agent introduces new facts, invents steps, or contradicts stored knowledge, the system highlights those failures with timestamps so you can review and fix them.

Consistency Across Turns and Repeated Runs

A dependable agent must give stable answers, even across long conversations or repeated attempts. Cekura uncovers inconsistency by:

• Testing the same scenario multiple times

• Using predefined metrics like response consistency and relevancy

• Logging where the agent repeats itself, contradicts earlier turns, or drifts from expected outcomes

• Running multi-turn scenarios that track context carryover

This shows where an agent loses track, changes tone, or produces different outcomes for the same input.

Safety, Tone, and Alignment

Cekura checks for safety issues directly in conversation. Your agent is evaluated for:

• Toxic content

• Harmful suggestions

• Biased language or unequal treatment across personas

• Failure to follow explicit instructions or guardrails

Cekura simulates a variety of user types, accents, and tones to surface unsafe or unwanted responses.

Robustness Under Stress and Ambiguity

Real users interrupt, change goals, or phrase things incorrectly. Cekura includes stress conditions to test resilience by:

• Running adversarial and jailbreak scenarios

• Simulating ambiguous or conflicting instructions

• Testing interruption handling through personalities that cut in frequently

• Introducing noise, pauses, and irregular speech patterns

• Simulating biased or uneasy user behavior

This ensures your agent performs reliably beyond the polished demo flow.

Reasoning Quality and Step Logic

Poor reasoning shows up in gaps between user asks and the agent’s final action. Cekura evaluates reasoning by:

• Checking logical coherence with LLM-based scoring

• Matching agent actions against the expected outcome for each scenario

• Highlighting invalid leaps or broken steps in multi-turn flows

• Detecting failures that occur midway through a chain of reasoning

Each reasoning failure is tied to a metric explanation and timestamp, making issues easy to trace.

Rigorous Evaluation Methods

Cekura offers multiple scoring paths so you can validate your agent from different angles.

Human-in-the-loop

• Annotate and correct evaluator outputs

• Downvote incorrect labels and refine metric definitions

• Build test sets to tune your LLM-as-a-judge metrics

Automated scoring

• Predefined metrics covering accuracy, safety, flow, and audio quality

• Custom LLM-as-a-judge metrics

• Python metrics for advanced logic

• Rule based scoring using your own criteria

Benchmarking

A/B test two models, prompts, or infrastructure setups on identical scenarios

Compare versions with charts, numeric diffs, and per-metric breakdowns

Transparency and Interpretability

Cekura makes reliability failures explicit and traceable.

• Each issue includes a timestamp

• Every metric explains why it passed or failed

• Deviations link back to the agent description

• Full transcripts and audio recordings reveal the exact failure point

• Visual run dashboards track improvements across versions

This gives your team clarity on why the agent behaved a certain way.

Multi-Turn and Long-Horizon Evaluation

Many failures only appear after several turns. Cekura tests:

• Context retention

• Long conversation stability

• Goal continuity

• Multi-node workflow accuracy

• Changing user intent handling

The platform runs entire dialogue paths end-to-end to surface mistakes that only show up mid-conversation.

Domain-Specific Scoring

For specialized agents, accuracy depends on domain rules. Cekura supports:

• Uploading domain documents or knowledge snippets

• Generating scenarios directly from your knowledge base

Custom metrics for domain standards

• Safety scoring tailored to specific compliance constraints

This is especially valuable for industries like healthcare, finance, or logistics, where the cost of an incorrect answer is high.

Privacy, Security, and Compliance

Cekura protects sensitive information through:

• Redaction of transcripts and audio for observability

• Secure API keys

• On-prem or custom integrations

• Encryption in transit and at rest

This ensures reliability evaluation can run safely even for regulated workloads.

Easy Integration and Continuous Testing

Reliable agents need continuous evaluation. Cekura supports:

• Full API access

• GitHub Actions for CI pipelines

• Scheduling via cron

• Regression baselines

• Text, voice, WebRTC, and SMS test modes

• Integrations with Retell, VAPI, ElevenLabs, Pipecat, LiveKit, and more

This lets you run evaluations automatically each time your team updates prompts, models, or infrastructure.

Adaptability and Customization

Every team defines reliability differently. Cekura lets you:

• Create your own scoring rubrics

• Build custom metrics in Python

• Set strictness thresholds

• Tune metrics with real call data

• Generate or author scenarios manually

You get complete control over how reliability is measured.

Cost Efficiency and Scale

Cekura supports high-volume testing:

• Batch runs

• Parallel calls

• Load testing with latency and failure metrics

• Scenario generation at scale

Teams can test dozens of workflows, personas, and variations without manual labor.

Community and Support

Cekura provides:

• Direct founder support

• Detailed documentation

• Assisted setup for complex agents

• Ongoing updates, new metrics, and new integrations

This helps teams maintain reliable agents even as their use cases evolve.

Why teams choose Cekura for reliability evaluation

Cekura gives you a structured way to validate every part of a conversational agent. It stress tests accuracy, consistency, safety, and reasoning while giving you clear explanations, reproducible metrics, and automated regression coverage. This helps you ship dependable agents faster, avoid silent failures, and keep performance steady as your product evolves.

If you want to measure and improve reliability across every turn, scenario, and version, Cekura brings the full toolset to your workflow.

Learn more about Cekura's reliability evaluation suite: Cekura