Agentic Resources

Average speed of answer explained: the ASA formula, what to include in it, why it hides your longest waits, the Medicare rules, and what changes with AI agents.

Datasets for training and evaluating RAG models by job: retriever training, embedding choice and benchmarks, plus an 8-step plan to build your own test set.

LLM tracing records every model call, tool call and retrieval as linked spans. Learn what to capture, how to sample, and why a trace alone won't find the bug.

Queue management explained: physical, virtual and call queues, the math behind waits (Little's Law, Erlang C), and a checklist to test your call queue.

Virtual agent vs chatbot: see how each decides, acts and fails, what research says about reliability, when to pick each, and a repeat-run test plan to use.

Agentic AI vs conversational AI compared: what each one does, how tool-calling agents fail, what acting costs in a voice call, and a six-step test plan.

Best practices in conversational AI design: 11 rules for disclosure, repair, handoff, latency and barge-in, each with its evidence and a test to run pre-launch.

An LLM gateway routes, retries and meters every model call. See how it works, how it differs from an AI gateway, and how to test the failures it hides.

How automated feedback loops for voice agents work, in four stages. Cekura refits the judge to reviewer corrections and gates releases on a scenario suite.

The privacy implications of RAG systems: measured extraction rates, why embeddings are not anonymization, the GDPR deletion problem, and a test checklist.

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.