Agentic Resources

An AI monitoring observability checklist for production agents: what to trace, which signals to alert on, how to verify tool calls, and what to count.

A RAG grounding benefits comparison of retrieval, web grounding, structured lookup, long context and fine-tuning: what each can cite, costs, and how it fails.

Cekura's programmatic voice agent testing API: a CLI, Python SDK, MCP server, and CI/CD gate for running and scoring voice-agent tests as code.

Compare open-source and hosted agent eval frameworks for trajectory, tool-call, and task-completion testing, then set up your first CI-gated eval run.

An LLM eval framework grades model outputs against benchmarks and rubrics. See how top tools compare and use the framework comparison table to choose yours.

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.

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