Multi-Turn Simulation: Part 1
Evaluation
Matt Wyman
,
Co-Founder & CEO
July 11, 2025
A Better Way to Test Conversational Agents
Imagine you’ve built a customer support chatbot. In isolated tests it answers correctly – great! But what happens when an unhappy customer pushes it over several back-and-forth turns? Will your bot stay polite and on-policy, or could it slip up and say something it shouldn’t? Conversely, what if a very friendly customer sweet-talks the bot – might it bend rules and give an unauthorized discount just to please them?
These scenarios reveal a hard truth: single-turn evaluations and basic observability aren’t enough to vet a conversational AI. Many failure modes only surface in multi-turn dialogues. The common (and inefficient) approach today is to have people manually “vibe-check” the agent – team members chatting with the bot and hoping to catch issues. This doesn’t scale and often misses subtler problems. There’s a better solution: multi-turn simulation of conversations.
From One-Shot to Full Conversations
In single-turn evaluation, we only see how the AI responds to one input at a time - even when a memory or conversation history is provided. It’s fundamentally a spot check on isolated responses. Real user interactions, however, are conversations: context builds up, each person will follow a unique path to a similar goal, people can reference past turns (or not), the AI must remember and stay consistent. Multi-turn simulation means testing the AI over an entire dialogue, not just a single exchange. By simulating multi-step conversations, we gain several advantages:
Conversation Dynamics: Multi-turn tests capture context and memory effects. Does the agent recall the user’s name or earlier details later on? Does it maintain its persona and follow instructions consistently over time? What if the user seeking the same goal approaches the conversation differently? Single-turn evals can’t assess these, but multi-turn simulation explicitly exercises them.
Robustness to Edge Cases: With simulation, we can inject adversarial or off-“happy path” turns mid-conversation to probe the agent’s robustness. For example, the simulated user might suddenly ask something off-topic or attempt a prompt injection. A single-turn test might try one malicious prompt in isolation, but a multi-turn scenario can see if the agent gradually gets tricked after some rapport is built.
Tool and Memory Usage: If your AI agent uses tools or external calls (e.g. functions, web search, database lookups), a multi-turn conversation is the only way to observe that in action. Simulations can follow and score those API calls or function invocations throughout the dialogue. This gives visibility into how the agent maintains session state or uses external knowledge over multiple steps – something basic observability might miss if it only logs final answers.
In short, use multi-turn simulation when success depends on how the assistant behaves over time, not just what it says once. Single-turn checks are a bit like quizzing a student on single questions; multi-turn is like observing them in a full interview or debate.
Why Traditional Testing Falls Short
Relying solely on logs and manual testing is risky. Observability (monitoring real chats) might eventually reveal a problem, but only after a user encounters it – too late! And spotting issues in long chat transcripts is like finding needles in a haystack. On the other hand, single-turn eval scripts (where you feed one prompt and check the answer) are easy to automate but narrow. They can tell you if the bot knows a fact or refuses a blatantly disallowed request, but they won’t catch slow failures. By “slow failures” we mean policy or performance breakdowns that happen gradually as a conversation progresses.
Consider our earlier examples: an upset customer who keeps pressing. Turn by turn, the AI’s patience or compliance might wear down until it says something inappropriate. Or a savvy user who tests boundaries – initially denied a coupon code, they keep chatting pleasantly and eventually the agent gives in with a freebie. These outcomes depend on the sequence of interactions. No single-turn test would trigger that behavior because it emerges from the interplay of multiple turns.
The industry’s workaround has been to have humans stress-test the AI with conversation role-play. That’s what we meant by “vibe-checking” – you literally chat with your bot pretending to be different user personas. It’s slow, inconsistent, and not comprehensive. You can’t possibly cover all the corner cases by hand, and each time your model or prompt is updated, the process repeats.
Meet Multi-Turn Simulation
Multi-turn simulation automates this conversational testing. The idea is to create a synthetic user persona (with a defined background, goals, and behavior style) and let it converse with your AI agent in a controlled environment. The synthetic user is powered by a language model as well – essentially it’s an LLM Driver that role-plays as the user. You program the Driver with instructions like a persona (“Angry customer who feels wronged”), goals (“wants a refund and an apology”), and tactics (“if agent resists, escalate politely but firmly”). This Driver will generate user messages turn by turn, driving the conversation with your AI (which we call the Target).
Meanwhile, the Target can be whatever AI you want to test – a hosted foundation model (e.g. OpenAI GPT-4 or Google Gemini), or even your own API/service if you have a custom-built conversational agent. The simulation platform (e.g. Okareo) mediates between the Driver and Target: it feeds the Driver’s messages to the Target and the Target’s responses back to the Driver, alternating turns just like a real chat. The conversation continues until a stop condition is met – perhaps a fixed number of turns or a goal achieved/failure detected.
Crucially, the entire interaction is evaluated automatically. You define or enable Checks – essentially assertions or metrics to apply on the dialogue. Some checks are built-in and very handy for conversational AI: for example, a Behavior Adherence check monitors if the assistant stayed in character and followed your guidelines, a Model Refusal check verifies the assistant correctly refused any disallowed requests, and a Task Completion check sees if the assistant ultimately achieved the user’s main goal. You can also write custom checks specific to your domain (maybe a check that no discount was given, or that the agent didn’t reveal confidential info). After the simulation, you get a report of which checks passed or failed, and even turn-by-turn annotations of issues. This makes it easy to pinpoint where in the conversation things went off track.
A New Testing Superpower
By running these multi-turn simulations, you essentially stress-test your AI agent in advance with a variety of scenarios – happy customers, angry customers, curious but rule-bending customers, you name it. It’s like a flight simulator for your chatbot: better to crash in the simulator than with real passengers! And unlike manual testing, you can run dozens or hundreds of these simulated conversations, covering lots of edge cases, whenever you need. Got a new model update or a tweaked prompt? Just rerun your suite of simulation scenarios and see if any regressions pop up.
Multi-turn simulation is thus a powerful complement to other evals: it gives you observability into conversation dynamics that static logs or one-shot tests would miss. Instead of waiting for a user to stumble on a flaw, you proactively discover how your agent behaves in complex dialogues. This leads to more robust, trustworthy AI systems.
In the upcoming posts, we’ll dive deeper. Next, in Part 2, we’ll show how to set up a multi-turn simulation step by step – defining personas, goals, and checks, and even give a peek at code and tools that make it easy. Then in Part 3, we’ll explore advanced tips (like crafting effective adversarial user prompts and integrating simulation into your development cycle). By the end of this series, you’ll be ready to leave single-turn “pop quizzes” behind and start grinding your agent through full conversational workouts. Your users (and your peace of mind) will thank you!
A Better Way to Test Conversational Agents
Imagine you’ve built a customer support chatbot. In isolated tests it answers correctly – great! But what happens when an unhappy customer pushes it over several back-and-forth turns? Will your bot stay polite and on-policy, or could it slip up and say something it shouldn’t? Conversely, what if a very friendly customer sweet-talks the bot – might it bend rules and give an unauthorized discount just to please them?
These scenarios reveal a hard truth: single-turn evaluations and basic observability aren’t enough to vet a conversational AI. Many failure modes only surface in multi-turn dialogues. The common (and inefficient) approach today is to have people manually “vibe-check” the agent – team members chatting with the bot and hoping to catch issues. This doesn’t scale and often misses subtler problems. There’s a better solution: multi-turn simulation of conversations.
From One-Shot to Full Conversations
In single-turn evaluation, we only see how the AI responds to one input at a time - even when a memory or conversation history is provided. It’s fundamentally a spot check on isolated responses. Real user interactions, however, are conversations: context builds up, each person will follow a unique path to a similar goal, people can reference past turns (or not), the AI must remember and stay consistent. Multi-turn simulation means testing the AI over an entire dialogue, not just a single exchange. By simulating multi-step conversations, we gain several advantages:
Conversation Dynamics: Multi-turn tests capture context and memory effects. Does the agent recall the user’s name or earlier details later on? Does it maintain its persona and follow instructions consistently over time? What if the user seeking the same goal approaches the conversation differently? Single-turn evals can’t assess these, but multi-turn simulation explicitly exercises them.
Robustness to Edge Cases: With simulation, we can inject adversarial or off-“happy path” turns mid-conversation to probe the agent’s robustness. For example, the simulated user might suddenly ask something off-topic or attempt a prompt injection. A single-turn test might try one malicious prompt in isolation, but a multi-turn scenario can see if the agent gradually gets tricked after some rapport is built.
Tool and Memory Usage: If your AI agent uses tools or external calls (e.g. functions, web search, database lookups), a multi-turn conversation is the only way to observe that in action. Simulations can follow and score those API calls or function invocations throughout the dialogue. This gives visibility into how the agent maintains session state or uses external knowledge over multiple steps – something basic observability might miss if it only logs final answers.
In short, use multi-turn simulation when success depends on how the assistant behaves over time, not just what it says once. Single-turn checks are a bit like quizzing a student on single questions; multi-turn is like observing them in a full interview or debate.
Why Traditional Testing Falls Short
Relying solely on logs and manual testing is risky. Observability (monitoring real chats) might eventually reveal a problem, but only after a user encounters it – too late! And spotting issues in long chat transcripts is like finding needles in a haystack. On the other hand, single-turn eval scripts (where you feed one prompt and check the answer) are easy to automate but narrow. They can tell you if the bot knows a fact or refuses a blatantly disallowed request, but they won’t catch slow failures. By “slow failures” we mean policy or performance breakdowns that happen gradually as a conversation progresses.
Consider our earlier examples: an upset customer who keeps pressing. Turn by turn, the AI’s patience or compliance might wear down until it says something inappropriate. Or a savvy user who tests boundaries – initially denied a coupon code, they keep chatting pleasantly and eventually the agent gives in with a freebie. These outcomes depend on the sequence of interactions. No single-turn test would trigger that behavior because it emerges from the interplay of multiple turns.
The industry’s workaround has been to have humans stress-test the AI with conversation role-play. That’s what we meant by “vibe-checking” – you literally chat with your bot pretending to be different user personas. It’s slow, inconsistent, and not comprehensive. You can’t possibly cover all the corner cases by hand, and each time your model or prompt is updated, the process repeats.
Meet Multi-Turn Simulation
Multi-turn simulation automates this conversational testing. The idea is to create a synthetic user persona (with a defined background, goals, and behavior style) and let it converse with your AI agent in a controlled environment. The synthetic user is powered by a language model as well – essentially it’s an LLM Driver that role-plays as the user. You program the Driver with instructions like a persona (“Angry customer who feels wronged”), goals (“wants a refund and an apology”), and tactics (“if agent resists, escalate politely but firmly”). This Driver will generate user messages turn by turn, driving the conversation with your AI (which we call the Target).
Meanwhile, the Target can be whatever AI you want to test – a hosted foundation model (e.g. OpenAI GPT-4 or Google Gemini), or even your own API/service if you have a custom-built conversational agent. The simulation platform (e.g. Okareo) mediates between the Driver and Target: it feeds the Driver’s messages to the Target and the Target’s responses back to the Driver, alternating turns just like a real chat. The conversation continues until a stop condition is met – perhaps a fixed number of turns or a goal achieved/failure detected.
Crucially, the entire interaction is evaluated automatically. You define or enable Checks – essentially assertions or metrics to apply on the dialogue. Some checks are built-in and very handy for conversational AI: for example, a Behavior Adherence check monitors if the assistant stayed in character and followed your guidelines, a Model Refusal check verifies the assistant correctly refused any disallowed requests, and a Task Completion check sees if the assistant ultimately achieved the user’s main goal. You can also write custom checks specific to your domain (maybe a check that no discount was given, or that the agent didn’t reveal confidential info). After the simulation, you get a report of which checks passed or failed, and even turn-by-turn annotations of issues. This makes it easy to pinpoint where in the conversation things went off track.
A New Testing Superpower
By running these multi-turn simulations, you essentially stress-test your AI agent in advance with a variety of scenarios – happy customers, angry customers, curious but rule-bending customers, you name it. It’s like a flight simulator for your chatbot: better to crash in the simulator than with real passengers! And unlike manual testing, you can run dozens or hundreds of these simulated conversations, covering lots of edge cases, whenever you need. Got a new model update or a tweaked prompt? Just rerun your suite of simulation scenarios and see if any regressions pop up.
Multi-turn simulation is thus a powerful complement to other evals: it gives you observability into conversation dynamics that static logs or one-shot tests would miss. Instead of waiting for a user to stumble on a flaw, you proactively discover how your agent behaves in complex dialogues. This leads to more robust, trustworthy AI systems.
In the upcoming posts, we’ll dive deeper. Next, in Part 2, we’ll show how to set up a multi-turn simulation step by step – defining personas, goals, and checks, and even give a peek at code and tools that make it easy. Then in Part 3, we’ll explore advanced tips (like crafting effective adversarial user prompts and integrating simulation into your development cycle). By the end of this series, you’ll be ready to leave single-turn “pop quizzes” behind and start grinding your agent through full conversational workouts. Your users (and your peace of mind) will thank you!
A Better Way to Test Conversational Agents
Imagine you’ve built a customer support chatbot. In isolated tests it answers correctly – great! But what happens when an unhappy customer pushes it over several back-and-forth turns? Will your bot stay polite and on-policy, or could it slip up and say something it shouldn’t? Conversely, what if a very friendly customer sweet-talks the bot – might it bend rules and give an unauthorized discount just to please them?
These scenarios reveal a hard truth: single-turn evaluations and basic observability aren’t enough to vet a conversational AI. Many failure modes only surface in multi-turn dialogues. The common (and inefficient) approach today is to have people manually “vibe-check” the agent – team members chatting with the bot and hoping to catch issues. This doesn’t scale and often misses subtler problems. There’s a better solution: multi-turn simulation of conversations.
From One-Shot to Full Conversations
In single-turn evaluation, we only see how the AI responds to one input at a time - even when a memory or conversation history is provided. It’s fundamentally a spot check on isolated responses. Real user interactions, however, are conversations: context builds up, each person will follow a unique path to a similar goal, people can reference past turns (or not), the AI must remember and stay consistent. Multi-turn simulation means testing the AI over an entire dialogue, not just a single exchange. By simulating multi-step conversations, we gain several advantages:
Conversation Dynamics: Multi-turn tests capture context and memory effects. Does the agent recall the user’s name or earlier details later on? Does it maintain its persona and follow instructions consistently over time? What if the user seeking the same goal approaches the conversation differently? Single-turn evals can’t assess these, but multi-turn simulation explicitly exercises them.
Robustness to Edge Cases: With simulation, we can inject adversarial or off-“happy path” turns mid-conversation to probe the agent’s robustness. For example, the simulated user might suddenly ask something off-topic or attempt a prompt injection. A single-turn test might try one malicious prompt in isolation, but a multi-turn scenario can see if the agent gradually gets tricked after some rapport is built.
Tool and Memory Usage: If your AI agent uses tools or external calls (e.g. functions, web search, database lookups), a multi-turn conversation is the only way to observe that in action. Simulations can follow and score those API calls or function invocations throughout the dialogue. This gives visibility into how the agent maintains session state or uses external knowledge over multiple steps – something basic observability might miss if it only logs final answers.
In short, use multi-turn simulation when success depends on how the assistant behaves over time, not just what it says once. Single-turn checks are a bit like quizzing a student on single questions; multi-turn is like observing them in a full interview or debate.
Why Traditional Testing Falls Short
Relying solely on logs and manual testing is risky. Observability (monitoring real chats) might eventually reveal a problem, but only after a user encounters it – too late! And spotting issues in long chat transcripts is like finding needles in a haystack. On the other hand, single-turn eval scripts (where you feed one prompt and check the answer) are easy to automate but narrow. They can tell you if the bot knows a fact or refuses a blatantly disallowed request, but they won’t catch slow failures. By “slow failures” we mean policy or performance breakdowns that happen gradually as a conversation progresses.
Consider our earlier examples: an upset customer who keeps pressing. Turn by turn, the AI’s patience or compliance might wear down until it says something inappropriate. Or a savvy user who tests boundaries – initially denied a coupon code, they keep chatting pleasantly and eventually the agent gives in with a freebie. These outcomes depend on the sequence of interactions. No single-turn test would trigger that behavior because it emerges from the interplay of multiple turns.
The industry’s workaround has been to have humans stress-test the AI with conversation role-play. That’s what we meant by “vibe-checking” – you literally chat with your bot pretending to be different user personas. It’s slow, inconsistent, and not comprehensive. You can’t possibly cover all the corner cases by hand, and each time your model or prompt is updated, the process repeats.
Meet Multi-Turn Simulation
Multi-turn simulation automates this conversational testing. The idea is to create a synthetic user persona (with a defined background, goals, and behavior style) and let it converse with your AI agent in a controlled environment. The synthetic user is powered by a language model as well – essentially it’s an LLM Driver that role-plays as the user. You program the Driver with instructions like a persona (“Angry customer who feels wronged”), goals (“wants a refund and an apology”), and tactics (“if agent resists, escalate politely but firmly”). This Driver will generate user messages turn by turn, driving the conversation with your AI (which we call the Target).
Meanwhile, the Target can be whatever AI you want to test – a hosted foundation model (e.g. OpenAI GPT-4 or Google Gemini), or even your own API/service if you have a custom-built conversational agent. The simulation platform (e.g. Okareo) mediates between the Driver and Target: it feeds the Driver’s messages to the Target and the Target’s responses back to the Driver, alternating turns just like a real chat. The conversation continues until a stop condition is met – perhaps a fixed number of turns or a goal achieved/failure detected.
Crucially, the entire interaction is evaluated automatically. You define or enable Checks – essentially assertions or metrics to apply on the dialogue. Some checks are built-in and very handy for conversational AI: for example, a Behavior Adherence check monitors if the assistant stayed in character and followed your guidelines, a Model Refusal check verifies the assistant correctly refused any disallowed requests, and a Task Completion check sees if the assistant ultimately achieved the user’s main goal. You can also write custom checks specific to your domain (maybe a check that no discount was given, or that the agent didn’t reveal confidential info). After the simulation, you get a report of which checks passed or failed, and even turn-by-turn annotations of issues. This makes it easy to pinpoint where in the conversation things went off track.
A New Testing Superpower
By running these multi-turn simulations, you essentially stress-test your AI agent in advance with a variety of scenarios – happy customers, angry customers, curious but rule-bending customers, you name it. It’s like a flight simulator for your chatbot: better to crash in the simulator than with real passengers! And unlike manual testing, you can run dozens or hundreds of these simulated conversations, covering lots of edge cases, whenever you need. Got a new model update or a tweaked prompt? Just rerun your suite of simulation scenarios and see if any regressions pop up.
Multi-turn simulation is thus a powerful complement to other evals: it gives you observability into conversation dynamics that static logs or one-shot tests would miss. Instead of waiting for a user to stumble on a flaw, you proactively discover how your agent behaves in complex dialogues. This leads to more robust, trustworthy AI systems.
In the upcoming posts, we’ll dive deeper. Next, in Part 2, we’ll show how to set up a multi-turn simulation step by step – defining personas, goals, and checks, and even give a peek at code and tools that make it easy. Then in Part 3, we’ll explore advanced tips (like crafting effective adversarial user prompts and integrating simulation into your development cycle). By the end of this series, you’ll be ready to leave single-turn “pop quizzes” behind and start grinding your agent through full conversational workouts. Your users (and your peace of mind) will thank you!
A Better Way to Test Conversational Agents
Imagine you’ve built a customer support chatbot. In isolated tests it answers correctly – great! But what happens when an unhappy customer pushes it over several back-and-forth turns? Will your bot stay polite and on-policy, or could it slip up and say something it shouldn’t? Conversely, what if a very friendly customer sweet-talks the bot – might it bend rules and give an unauthorized discount just to please them?
These scenarios reveal a hard truth: single-turn evaluations and basic observability aren’t enough to vet a conversational AI. Many failure modes only surface in multi-turn dialogues. The common (and inefficient) approach today is to have people manually “vibe-check” the agent – team members chatting with the bot and hoping to catch issues. This doesn’t scale and often misses subtler problems. There’s a better solution: multi-turn simulation of conversations.
From One-Shot to Full Conversations
In single-turn evaluation, we only see how the AI responds to one input at a time - even when a memory or conversation history is provided. It’s fundamentally a spot check on isolated responses. Real user interactions, however, are conversations: context builds up, each person will follow a unique path to a similar goal, people can reference past turns (or not), the AI must remember and stay consistent. Multi-turn simulation means testing the AI over an entire dialogue, not just a single exchange. By simulating multi-step conversations, we gain several advantages:
Conversation Dynamics: Multi-turn tests capture context and memory effects. Does the agent recall the user’s name or earlier details later on? Does it maintain its persona and follow instructions consistently over time? What if the user seeking the same goal approaches the conversation differently? Single-turn evals can’t assess these, but multi-turn simulation explicitly exercises them.
Robustness to Edge Cases: With simulation, we can inject adversarial or off-“happy path” turns mid-conversation to probe the agent’s robustness. For example, the simulated user might suddenly ask something off-topic or attempt a prompt injection. A single-turn test might try one malicious prompt in isolation, but a multi-turn scenario can see if the agent gradually gets tricked after some rapport is built.
Tool and Memory Usage: If your AI agent uses tools or external calls (e.g. functions, web search, database lookups), a multi-turn conversation is the only way to observe that in action. Simulations can follow and score those API calls or function invocations throughout the dialogue. This gives visibility into how the agent maintains session state or uses external knowledge over multiple steps – something basic observability might miss if it only logs final answers.
In short, use multi-turn simulation when success depends on how the assistant behaves over time, not just what it says once. Single-turn checks are a bit like quizzing a student on single questions; multi-turn is like observing them in a full interview or debate.
Why Traditional Testing Falls Short
Relying solely on logs and manual testing is risky. Observability (monitoring real chats) might eventually reveal a problem, but only after a user encounters it – too late! And spotting issues in long chat transcripts is like finding needles in a haystack. On the other hand, single-turn eval scripts (where you feed one prompt and check the answer) are easy to automate but narrow. They can tell you if the bot knows a fact or refuses a blatantly disallowed request, but they won’t catch slow failures. By “slow failures” we mean policy or performance breakdowns that happen gradually as a conversation progresses.
Consider our earlier examples: an upset customer who keeps pressing. Turn by turn, the AI’s patience or compliance might wear down until it says something inappropriate. Or a savvy user who tests boundaries – initially denied a coupon code, they keep chatting pleasantly and eventually the agent gives in with a freebie. These outcomes depend on the sequence of interactions. No single-turn test would trigger that behavior because it emerges from the interplay of multiple turns.
The industry’s workaround has been to have humans stress-test the AI with conversation role-play. That’s what we meant by “vibe-checking” – you literally chat with your bot pretending to be different user personas. It’s slow, inconsistent, and not comprehensive. You can’t possibly cover all the corner cases by hand, and each time your model or prompt is updated, the process repeats.
Meet Multi-Turn Simulation
Multi-turn simulation automates this conversational testing. The idea is to create a synthetic user persona (with a defined background, goals, and behavior style) and let it converse with your AI agent in a controlled environment. The synthetic user is powered by a language model as well – essentially it’s an LLM Driver that role-plays as the user. You program the Driver with instructions like a persona (“Angry customer who feels wronged”), goals (“wants a refund and an apology”), and tactics (“if agent resists, escalate politely but firmly”). This Driver will generate user messages turn by turn, driving the conversation with your AI (which we call the Target).
Meanwhile, the Target can be whatever AI you want to test – a hosted foundation model (e.g. OpenAI GPT-4 or Google Gemini), or even your own API/service if you have a custom-built conversational agent. The simulation platform (e.g. Okareo) mediates between the Driver and Target: it feeds the Driver’s messages to the Target and the Target’s responses back to the Driver, alternating turns just like a real chat. The conversation continues until a stop condition is met – perhaps a fixed number of turns or a goal achieved/failure detected.
Crucially, the entire interaction is evaluated automatically. You define or enable Checks – essentially assertions or metrics to apply on the dialogue. Some checks are built-in and very handy for conversational AI: for example, a Behavior Adherence check monitors if the assistant stayed in character and followed your guidelines, a Model Refusal check verifies the assistant correctly refused any disallowed requests, and a Task Completion check sees if the assistant ultimately achieved the user’s main goal. You can also write custom checks specific to your domain (maybe a check that no discount was given, or that the agent didn’t reveal confidential info). After the simulation, you get a report of which checks passed or failed, and even turn-by-turn annotations of issues. This makes it easy to pinpoint where in the conversation things went off track.
A New Testing Superpower
By running these multi-turn simulations, you essentially stress-test your AI agent in advance with a variety of scenarios – happy customers, angry customers, curious but rule-bending customers, you name it. It’s like a flight simulator for your chatbot: better to crash in the simulator than with real passengers! And unlike manual testing, you can run dozens or hundreds of these simulated conversations, covering lots of edge cases, whenever you need. Got a new model update or a tweaked prompt? Just rerun your suite of simulation scenarios and see if any regressions pop up.
Multi-turn simulation is thus a powerful complement to other evals: it gives you observability into conversation dynamics that static logs or one-shot tests would miss. Instead of waiting for a user to stumble on a flaw, you proactively discover how your agent behaves in complex dialogues. This leads to more robust, trustworthy AI systems.
In the upcoming posts, we’ll dive deeper. Next, in Part 2, we’ll show how to set up a multi-turn simulation step by step – defining personas, goals, and checks, and even give a peek at code and tools that make it easy. Then in Part 3, we’ll explore advanced tips (like crafting effective adversarial user prompts and integrating simulation into your development cycle). By the end of this series, you’ll be ready to leave single-turn “pop quizzes” behind and start grinding your agent through full conversational workouts. Your users (and your peace of mind) will thank you!
