Synthetic Users: The Complete Guide (2026)
What synthetic users are, how they're generated, and how teams use them to test AI agents at population scale — beyond hand-written personas.
How to test AI agents before (and after) production: step-by-step methodology, user simulation, vertical playbooks, and the regression practices that keep an agent reliable as it changes.
What synthetic users are, how they're generated, and how teams use them to test AI agents at population scale — beyond hand-written personas.
How to test AI agents before production: a 7-step method — define success, build a realistic user population, run multi-turn tests, score, and gate.
A practical guide to AI agent evaluation: outcome vs. trajectory metrics, four evaluation methods, and a step-by-step framework for running agent evals.
Why single-turn evals miss real failures, and how multi-turn evaluation works: scripted flows, simulated users, and conversation-level scoring.
A technical guide to user simulation for AI agents: simulator anatomy, the conversation simulation loop, what to log and score, and the classic pitfalls.
AI agent reliability, explained: why demos succeed while agents in production fail, why the demo is a sampling statement, and how to close the gap first.
LLM regression testing when the same input no longer gives the same output: pin seeds and populations, sample runs, score semantically, gate releases.
A layered method for testing a LangGraph agent: unit-test nodes, verify routing and state, then run simulated users against the compiled graph.
A practical playbook for testing customer support AI: intent coverage, policy compliance, escalation judgment, tone under fire, and segment-level results.
What voice ai testing adds beyond text — latency, barge-in, ASR errors, TTS artifacts — and why most voice agent failures are still dialog-logic failures.
Building an AI agent got easy. Deploying AI agents to production is where it breaks. What proving your agent works actually requires, step by step.