Simulations: Stop Shipping AI Agents on Hope — Pressure-Test Them With AI Personas First

The "It Worked When I Tried It" Problem

You build an agent. You talk to it a few times. It answers nicely. You ship it. Then a real caller is impatient, switches languages mid-sentence, demands a refund they're not owed, or simply tries to jailbreak it into leaking another customer's data — and you find out in production, in front of a customer, on a recording.

Manually testing every persona, every edge case, every adversarial prompt doesn't scale. You can't sit there role-playing fifty angry customers before each release. So most teams don't — they ship and pray.

thinnestAI Simulations replace praying with proof. An AI persona caller role-plays a realistic (and sometimes difficult) user, holds a full multi-turn conversation with your real agent, and an AI judge scores exactly how it did — before anyone real is on the line.

How It Works

• Persona caller: A driver LLM plays a caller with a goal, a personality, an emotional state, and the facts they know — cooperative, confused, impatient, or outright adversarial.
• Your real agent: The conversation runs against your agent's actual configuration — its system prompt, model, tools, knowledge bases, and voice workflow. What you test is what ships.
• AI judge: A strong model (default GPT-4o, fully configurable) reads the transcript and returns a verdict: Goal Status (pass / review / fail), Conversation Quality (high / medium / low), and a pass/fail per success criterion — each with written reasoning.

One Click to a Whole Test Suite

You don't have to hand-write scenarios. Click Generate, and thinnestAI reads your agent's own configuration and produces a diverse, guardrail-probing suite — grounded in your rules, not generic templates. Add a line of guidance ("focus on customers disputing charges") and it tailors the set. Then edit, add, or remove anything before you run.

Three Kinds of Caller, By Design

• Happy path: the cooperative, ideal user — does the agent nail the easy case cleanly?
• Boundary: confused, off-topic, or rule-probing callers — where does the agent wobble?
• Red team: adversarial callers trying to break the rules, leak data, or jailbreak the agent — the cheapest place to find this is a simulation, not a screenshot from an angry customer.

Chat and Voice — Test What Customers Actually Hear

Text testing catches logic bugs. It can't catch the way an agent sounds, mishears, or talks over a caller. So thinnestAI runs both:

• Chat mode — a fast, inexpensive text conversation. Available on every plan; perfect for iterating on prompts and guardrails.
• Voice mode — a real voice call. An AI caller actually speaks to your agent through the live voice pipeline (Vega speech-to-text + Aero text-to-speech), so you validate the agent exactly as a phone caller would experience it. Available on Pay-as-you-go and Enterprise plans.

Workflow Agents, Fully Covered

If your agent uses a visual voice workflow, simulations drive the actual workflow runtime — nodes, transitions, and variable extraction — so a test reflects the real branching flow, not a flattened prompt. Side-effecting steps (API calls, tools, transfers) are simulated, not executed, so your tests are safe and repeatable: the agent is recorded as having attempted the action, but no real external call fires.

thinnestAI vs. Competitors: Agent Testing

Capability	thinnestAI	Cekura	Coval	Hamming
Auto-generated scenarios from your agent	Yes — one click	Manual / scripted	Manual / scripted	Manual / scripted
No-code, visual	Full panel in the builder	Dashboard + setup	SDK / config	SDK / config
Chat and voice	Both, same suite	Voice-focused	Both	Voice-focused
Tests the real deployed agent	Yes — same config, in-platform	External harness	External harness	External harness
Workflow-aware (branching flows)	Yes — drives the real runtime	Limited	Limited	Limited
Built into the agent platform	Yes — where you build the agent	Separate product	Separate product	Separate product

Reading the Results

• Goal Status: did the agent meet the scenario's success criteria — pass, review, or fail?
• Conversation Quality: how well did the whole conversation flow — high, medium, or low?
• Per-criterion verdicts: a pass/fail for each yes/no question you defined ("Did the agent verify identity before sharing account details?").
• Reasoning + transcript: every run saves the judge's reasoning and the full turn-by-turn conversation, so a failure is something you can read, not guess at.

A run rolls its scenarios up into pass / review / fail counts and a quality breakdown — health at a glance, with one click to drill into any failure.

Make It a Habit, Not a Launch Ritual

1. Generate a suite from your agent's config.
2. Run it in chat to iterate fast, then validate the final flow in voice.
3. Read the judge's reasoning on every review and fail.
4. Fix the prompt, guardrail, or workflow.
5. Re-run — treat the suite as a regression test for every change.

That's data-driven shipping. Not "tweak the prompt and hope."

Get Started

Open any agent, head to the Simulation tab, and click Generate. Chat simulations are available on every plan; voice simulations run on Pay-as-you-go and Enterprise.

Pressure-Test Your Agent Free →

No credit card required • Auto-generated scenarios • Chat + voice • AI-judged