Agentic Resources

Evals are systematic tests that measure AI output quality. What an eval contains, how graders score it, and the mistakes that make an eval suite untrustworthy.

G-Eval scores generated text with an LLM judge, no reference answer needed. How it works, what its correlation figures mean, and where the method breaks.

A human evaluator scores AI output that automated metrics cannot judge. What they measure, how much they agree with each other, and when to automate instead.

LLM testing checks model behaviour, not just code. Learn the five test layers, deterministic vs model-graded checks, and how to run an LLM test suite in CI.

How to automate voice agent regression testing with a coding agent, plus running evals in CI and wiring GitHub Actions into the same pipeline.

Compare Cekura's CLI, Python SDK, and MCP server against Hamming, Coval, and other platforms for programmatic voice agent testing and CI/CD integration.

Test AI chat agents for instruction-following across prompts, policies, tools, structured outputs, multi-turn workflows, regression tests, and production conversations.

Discover the 5 best voice agent testing platforms (2026) for automated call simulation, multi-turn conversation testing, regression validation, and reliability testing across real-world voice AI interactions.