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Coding · Intermediate

# Automated testing for conversational agent workflows

Run multi-turn simulated user dialogues to catch prompt regressions before release

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Opens BotBento. Paste the prompt into a bot; nothing is preconfigured.

- When something happensAgent code change pushed
- ThenRun simulated conversation suites
- ResultPublish test evaluation report

Run simulated test conversations against custom agent configurations to verify tool usage and goal completion. The bot executes end-to-end conversation suites, identifies broken dialogue paths or tool failures, and summarizes regression risks across multiple user scenarios.

## Prompt to start with

BotBentoRecommended

```
When a test run is initiated, execute multi-turn dialogue scenarios against the target agent configuration using terminal test runners. Compare conversational outcomes against expected goals and flag failed tool calls or incomplete dialogues. Prepare an evaluation report for the team, and always wait for user approval before updating repository files or merging code.
```

Copy

## Tools it uses

GitHubTerminal

## Works with

BotBento

## Tags

- testing
- evaluation
- agents
- python

## Source

[AutoChain: Build lightweight, extensible, and testable LLM Agents !\[GitHub Repo stars\](https://img.shields.io/github/stars/Forethought-Techn](https://github.com/Forethought-Technologies/AutoChain)
GitHub · checked 2026-10-16

Summarized in our own words. Any results described there are the authors’ own, not measured by BotBento.

By BotBento Editorial · Added 2026-10-16 · Updated 2026-10-16 · [Suggest a correction](/contact/)

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