AI agents don't just generate answers anymore. They take actions, call tools, and access real data. BotGauge helps engineering teams find where agents break before users do. Run adaptive red-team attacks and evals, catch security and behavioral failures, and replay every issue with full traces. Turn failures into regression tests, continuously retest as your agents evolve, and monitor their behavior in production. One reliability layer from development to deployment.
Your agent has a failure mode you haven’t found yet. Your users will find it first. 👋
I’m Pramin, co-founder and CEO of BotGauge. Today we’re launching BotGauge: the platform that finds your AI agent’s failures before your users do.
Agents don’t fail like normal software. A user comes at it sideways, and it calls tools it shouldn’t, leaks context, or skips approvals. Change the model, a prompt, or an MCP server, and it can take a completely different path. Last month’s tests won’t catch that.
So we built an attacker.
🧠 Custom attacks for your agent. BotGauge learns your agent’s purpose, its users, and what it’s allowed to do. Then our in-house red-teaming engine builds attack patterns specific to that agent, because a booking agent and a support bot break in very different ways.
🔍 See exactly how it broke. Every finding links to the prompts, tool calls, and execution path behind it, so you fix the root cause.
🔁 Every failure becomes a permanent test. Each break turns into an eval that runs on every release and feeds your guardrails and monitoring.
Example: we told a booking agent to ignore its instructions, rebook to a new destination, and skip approval. BotGauge caught it, blocked the action, and added the attack to the eval suite for good.
It works with OpenAI, Anthropic, LangChain, LangGraph, CrewAI, AutoGen, and plain SDK/API. We’re SOC 2 Type II.
How do I get my agent layer into the platform? Mine needs a macOS computer to run.
The part that stuck with me is turning each failure into a permanent eval that runs on every release. The quiet failures Pramin described — an agent retrying a failed tool call in a loop, or holding an approval for 117 role-play attempts and folding on the 118th — are exactly the ones that never show up as crashes. Replaying the same tool calls with the full trace feels like the only way to know a fix actually fixed it, rather than just changing the prompt and hoping. Curious how you handle the cases where the break comes from instructions hidden in a document the agent reads, rather than from the user directly.
Really love this. It's something we will definitely look into as we roll out AI chat in our app in the coming months, as it's exactly the type of testing and safeguards we need.
An agent passing yesterday's test is a low bar if today's PDF can talk it into a new career. Do your evals cover instructions hidden in documents and tool outputs, or mainly direct user prompts?
This looks really interesting. I've been hesitant to hook up some services to agents precisely because I didn't trust it not to take advantage of the permissions it was given. I'm definitely going to explore BotGauge further.
A bit of feedback on the post: your images are too low-resolution to really see what's on the screens you're sharing. I noticed the same thing on your website. I think some higher-resolution images would go a long way in helping people better understand what it is you're offering.
Curious, what's the mist surprising way you're seen an agent break during testing?
Replaying a failed run with full traces and then keeping it as a regression test is the loop most agent teams still do by hand in a notebook, so having it in one layer from dev to prod saves real time.
A friend of mine runs a booking agent. A user talked it into changing a trip without asking anyone. Scary how easy it was.
The trace is what makes a failed eval useful for me. Being able to replay the same tool calls should make it easier to check whether a fix holds.
To answer your question, back in February an OpenClaw agent I'd hooked up to WhatsApp started messaging a friend of mine and leaking its own internal notes into the chat. So I pulled its access. I'd never have thought to write a test for that one, which I guess is the point of having something attack the agent on purpose.
Curious to see how it works in real world scenarios, especially with agents getting more complex
Really curious to see what people come up with! Always interesting to see how someone pushes an agent in a way nobody expected.
Excited to try out red-teaming. Stress testing the AI agent against real attack patterns before attackers to find the gaps sounds interesting.
About BotGauge on Product Hunt
“The trust layer for your agents ”
BotGauge launched on Product Hunt on October 28th, 2026 and earned 209 upvotes and 34 comments, earning #3 Product of the Day. AI agents don't just generate answers anymore. They take actions, call tools, and access real data. BotGauge helps engineering teams find where agents break before users do. Run adaptive red-team attacks and evals, catch security and behavioral failures, and replay every issue with full traces. Turn failures into regression tests, continuously retest as your agents evolve, and monitor their behavior in production. One reliability layer from development to deployment.
BotGauge was featured in Developer Tools (522k followers), Artificial Intelligence (482.3k followers) and Security (3k followers) on Product Hunt. Together, these topics include over 230k products, making this a competitive space to launch in.
Who hunted BotGauge?
BotGauge was hunted by Ben Lang. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how BotGauge stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Ben Lang
Your agent has a failure mode you haven’t found yet. Your users will find it first. 👋
I’m Pramin, co-founder and CEO of BotGauge. Today we’re launching BotGauge: the platform that finds your AI agent’s failures before your users do.
Agents don’t fail like normal software. A user comes at it sideways, and it calls tools it shouldn’t, leaks context, or skips approvals. Change the model, a prompt, or an MCP server, and it can take a completely different path. Last month’s tests won’t catch that.
So we built an attacker.
🧠 Custom attacks for your agent. BotGauge learns your agent’s purpose, its users, and what it’s allowed to do. Then our in-house red-teaming engine builds attack patterns specific to that agent, because a booking agent and a support bot break in very different ways.
🔍 See exactly how it broke. Every finding links to the prompts, tool calls, and execution path behind it, so you fix the root cause.
🔁 Every failure becomes a permanent test. Each break turns into an eval that runs on every release and feeds your guardrails and monitoring.
Example: we told a booking agent to ignore its instructions, rebook to a new destination, and skip approval. BotGauge caught it, blocked the action, and added the attack to the eval suite for good.
It works with OpenAI, Anthropic, LangChain, LangGraph, CrewAI, AutoGen, and plain SDK/API.
We’re SOC 2 Type II.
👉 Try it free: Link
👉 Book a demo here: Link
What’s the weirdest thing your agent has done in production? The team and I are in the comments all day. 🚀