Modern conversational agents change fast. Prompts evolve, models get swapped, tools are updated, and infrastructure shifts. Without tight CI/CD integration, small changes can quietly break multi-turn behavior, tool calls, or latency targets.
Cekura’s testing framework is built to live directly inside GitHub and Jenkins pipelines, so conversational agents are validated the same way production software is validated.
GitHub-Native Workflow for Conversation Testing
Cekura fits naturally into GitHub-based development flows. Tests can be triggered automatically on pull requests, commits, or merges using native GitHub Actions rather than fragile custom scripts. Every change to prompts, models, or agent logic can kick off conversational test suites before it reaches main.
Test results are attached directly to the code lifecycle. Each run produces structured artifacts that stay linked to commits and releases, making it easy to trace regressions back to a specific change. For teams working in mono-repos or managing multiple agents in a single repository, test definitions and datasets remain versioned alongside code. Permissions stay tight through scoped tokens and secure secret handling, so CI runs never expose sensitive credentials.
Jenkins Pipelines That Enforce Conversation Quality
For teams standardized on Jenkins, Cekura integrates cleanly into both declarative and scripted pipelines. Conversational tests can run as first-class pipeline stages, parallelized across agents, models, or scenarios to keep feedback fast even as coverage grows.
Build gating is straightforward. Teams can define thresholds for accuracy, latency, hallucination signals, or tool-call correctness. If a new change crosses a failure boundary, the pipeline stops. This prevents broken conversational behavior from ever reaching staging or production. Over time, Jenkins artifacts accumulate into historical trends that show how agent quality evolves across releases.
Built for Real Conversational Complexity
The framework goes beyond single-turn checks. Test suites cover multi-turn dialogues with full context carryover, branching paths, and both stateful and stateless flows. User personas can be simulated to reflect real behavior patterns, accents, or intent ambiguity.
Because conversational systems are probabilistic, evaluations do not rely on brittle string matching. Responses are assessed for semantic alignment, intent accuracy, entity extraction, and correct tool or function invocation. Tests remain stable even when phrasing varies, and teams can pin model versions or temperature settings to ensure reproducibility across CI runs.
Assertions, Metrics, and Custom Evaluators
Cekura supports a wide range of assertions that map directly to conversational risk. These include semantic similarity thresholds, structured output validation, safety constraints, latency limits, and timeout detection. Results are returned with confidence scoring, not just binary pass or fail.
For advanced needs, teams can plug in custom evaluators using Python or JavaScript hooks. This allows bespoke scoring logic, domain-specific checks, or LLM-based judging with guardrails. Metrics such as intent precision, entity recall, response consistency, hallucination signals, and cost per test run can all be tracked automatically.
Versioned Test Data and Regression Control
Conversation fixtures live in GitHub alongside code. Datasets are versioned, tagged, and reusable across environments. Golden conversations define expected behavior, while diffing highlights exactly where new runs diverge from prior baselines.
Rollback is simple. Because test definitions are config-as-code, teams can revert a test suite or dataset just like any other file. Deterministic execution and environment isolation ensure that failures are reproducible, whether they occur in local runs, CI, or staging.
Observability That Feeds Back into CI
Every failed test comes with full conversational context. Transcripts, prompts, system messages, tool call traces, and token-level details are available for debugging. Failed CI jobs can be re-run exactly as they occurred, without guesswork.
Reports are produced in both human-readable and machine-readable formats, making it easy to plug results into dashboards, alerts, or downstream automation. Over time, failure clustering reveals recurring root causes, helping teams focus fixes where they matter most.
Secure, Scalable, and Enterprise-Ready
Cekura’s CI integrations respect enterprise security requirements. Secrets are managed through GitHub and Jenkins vaults, logs support PII redaction, and access is controlled through roles and audit trails. Execution can run in the cloud or in self-hosted environments depending on deployment needs.
Parallel execution and load testing support allow teams to validate concurrency limits and performance under stress, all before real users are affected.
By embedding conversational testing directly into GitHub and Jenkins, Cekura turns conversational agents into first-class CI/CD citizens. Every change is tested, every regression is caught early, and every release ships with confidence.