BotGauge is an agentic AI testing platform that uses adaptive red-teaming, execution tracing, and custom evaluators to detect failures and enforce behavioral guardrails before agents reach production.
What the product does and how it is positioned
BotGauge provides testing, tracing, and continuous evaluation tooling built specifically for AI agents, multi-agent systems, and transactional workflows.
The platform simulates real-world and adversarial interactions to uncover unintended execution paths, policy violations, or tool-misuse events, and allows engineering teams to convert those findings into automated test suites.
Source-supported ways to use the product
Engineering teams test AI agents against prompt injection and policy-bypass scenarios to uncover vulnerabilities prior to release.
The official customer story reports that Atlas runs red-team campaigns before release to find agent failures and converts each finding into a check that runs on future releases.
Developers trace tool calls, context passing, and multi-step execution flows across agents that execute approvals, refunds, and API actions.
The documented workflow, where available
Run baseline and adaptive red-teaming tests exploring inputs, contexts, tools, policies, and multi-turn interactions to identify unexpected agent behaviors.
Inspect prompts, model responses, tool calls, context, and execution paths behind each test result to understand how the agent arrived at its output.
Convert discovered edge cases and policy violations into repeatable code-based or LLM evaluations and safeguards that run with every future release.
BotGauge evaluates agent behavior by running adaptive red-teaming scenarios that simulate real-world pressure and adversarial inputs. By exploring how agents handle changes in prompts, context, and tool definitions across multi-turn exchanges, the platform identifies situations where an agent might bypass policy restrictions or skip required operational steps.
When unexpected behaviors occur, developers can inspect the complete execution trace. BotGauge records the sequence of prompts, model completions, contextual parameters, and tool invocations, allowing engineering teams to determine whether failure modes originate from prompt sensitivity, tool failure, or context drift.
Checks to run with your own material and workflow
What was checked and when
Answers based on the source-checked product record
BotGauge supports RAG assistants, customer support agents, voice agents, multi-agent systems, and transactional agents handling actions like approvals, refunds, and transfers.
BotGauge documents compatibility with OpenAI, Anthropic, and Hugging Face models, as well as LangChain, LlamaIndex, LangGraph, CrewAI, AutoGen, and AutoGPT frameworks.
BotGauge evaluates agents by running adaptive red-team tests and scoring goal completion, accuracy, and policy adherence through LLM evaluators and code-based checks.
BotGauge provides SOC 2 Type II validated security practices, SAML-compatible single sign-on (SSO), and fine-grained access control over projects and resources.
Yes, behaviors and policy violations uncovered during red teaming can be added to an evaluation suite to run as automated regression checks on subsequent releases.
