Opportunities › Testing and quality assurance for AI agents › Proposed app
Probebench
Adaptive test conversations that find where your AI agent breaks before users do
- Score
- 42
- confidence 44
- Evidence
- 5
- items on the page
- Rivals
- 1
- named in sources
- First version
- 30 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2023
- Emerging market · AI estimate
ScreensConcept
Desktop app: Developers write rules, monitor multi-turn test flows, compare test runs and review transcripts—sustained keyboard work done at a desk. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
New test brief
Ready to run- AgentRefund assistant
About this appAI proposal
Probebench is a testing harness for teams building AI agents. Point it at your agent's endpoint or chat URL, write what the agent should and should not do, and it runs multi-turn conversations that adapt to each reply, looking for logic errors, ignored instructions and security gaps.
You do not write test cases first. Probebench proposes scenarios, follows weak spots deeper, and reports each failure as a replayable transcript that marks the turn where things went wrong. Confirmed failures can be saved as regression checks to rerun after every prompt or model change.
It is for developers and small teams shipping agents to real users, who today write static evals by hand and learn about the rest from complaints. It is meant to complement your evals by finding cases you did not think of.
- Tagline
- Find the failures your evals never imagined.
- Voice
- DirectPreciseCalm
- Why this name
- Probe says what it does: poking an agent until it shows weaknesses. Bench suggests a workbench for repeatable testing. Two plain words joined.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Adaptive multi-turn probing
A tester agent reads each reply and changes tack: rephrasing, pushing back, switching topic, to reach failures a fixed script would miss.
No member yet
No special access needed
Test through a public endpoint, web chat or API the way a user would. No model weights, logs or code changes required.
No member yet
Rules in plain language
Write what your agent must and must not do in a short brief. Probebench turns it into checks and flags every conversation that breaks them.
No member yet
Replayable failure transcripts
Each finding shows the full conversation, the failing turn, the rule broken and a severity, so engineers can reproduce it fast.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#14201FDeep Ink · text and icon background#F6F2E9Paper · surface#1F6F66Pine · primary#E8A317Signal Amber · accent#C8412BFault Red · data: failures
Typography
Aa
Probebench
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
Find the failures your evals never imagined.
PricingAI proposal
The material gives no competitor prices. Tiers follow the suggested per-agent, run-volume and freemium models and are a starting point to test, not a market-based figure.
Free
Free
- 1 agent
- Limited probe runs per month
- Plain-language rule checks
- Transcript replay
Team
$99 a month
- Up to 5 agents
- Larger monthly run volume
- Security and red-team runs
- Regression sets and reruns
- Shared findings
Scale
Contact us
- Many agents and seats
- Custom run volume
- Priority support
Would you pay Team · $99 a month?
Your answer tells PWAS which prices people accept. It changes no score.
No answers yet.
What it could be worthEstimate
What a software business making this much sells for today, from real deals. Pick a monthly revenue.
If it makes
Worth about
$190,000
Sale range $132,000 to $263,000
$120,000 a year in revenue
From 87 software businesses listed and sold on Empire Flippers in the last year, revenue vetted by the broker: they sell for about 19× their monthly revenue, most between 13× and 26×.
Measured on profit, the middle deal is 32× monthly net profit: about 2.7 years of profit.
An estimate, not an offer or financial advice. A real price depends on profit, growth, churn, how much of the work is the owner's, and the buyer.
How it would growAI proposal
How the product would reach new customers through its own use, and how the first ones would be found. A plan to test, not a result.
- Built-in loop
- Shared outputs. Users share finding reports and transcript links with teammates, clients or security reviewers, and a badge on a public report page links back to Probebench.
- Do they become customers?
- Recipients are often engineers or reviewers at similar teams and could become buyers, but a report link opened once may not convert. Test whether shared reports lead to signups.
- First customers
- Small startups of 2 to 15 people shipping a customer-facing support or sales AI agent, where one engineer owns quality and writes evals by hand.
- Where to reach them
- Hacker News and dev forums: Post a teardown of failures found in a public demo agent, with transcripts, and invite readers to try their own.
- AI engineer communities: Offer free run credits in Discord and Slack groups for agent builders in exchange for feedback.
- First milestone
- Five teams run Probebench on a production agent weekly for a month, and three of them pay for the Team tier.
Why people already pay for this
- VerifiedHacker News post received 20 points and 8 comments
- Public claimTeams spend significant time writing evaluations for failure modes of AI agents
- Public claimAutonomous testing discovers issues much faster than manual audits
- Public claimAI agents fail in ways that manual testing and static benchmarks do not catch
- Public claim
Tech stack
Every proposal builds on the same PWA base, installable from the browser on any phone or computer. Dashed boxes are what this app adds.
- AppNext.js and TypeScript
- PWAManifest and service worker
- UITailwind CSS
- Data and authSupabase Postgres
- PaymentsStripe
- EmailResend
- HostingVercel
- This app · Background jobsQueue with long-running workersTest runs span many multi-turn conversations and outlast serverless request limits.
- This app · LLM accessHosted LLM API for tester and judgeAdaptive probing and grading of transcripts need strong models; cost per run must be tracked.
RisksAI inference
- AIThe tester or judge model may report false failures or miss real ones, so trust depends on evidence in transcripts and on measured precision.
- CommercialTeams may treat testing as a feature of their existing eval or agent platform and not pay for a separate tool. Needs testing.
- TechnicalAgents behind auth, rate limits or bot protection are hard to probe without setup; connector effort may be large.
- DataTranscripts can hold customer or secret data returned by the tested agent, requiring careful retention and access controls.
- CommercialModel costs per run may squeeze margins at flat prices, especially on long adaptive sessions.
Everything marked AI proposal or AI inference was written by a model from the evidence on the opportunity page. The proposed name must be checked against trademark registers before it is used.