SaaS ideas your next customers are already looking for
100 SaaS startup ideas for the people who said they'd pay, from agency owners to dentists. Micro SaaS ideas, AI SaaS ideas and B2B SaaS ideas: find yours, build it, sign them up.
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Pain points in business: what people struggle with
Search the problems to solve, as people described them on Reddit and other platforms, in their own words.
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AI Agent Regression Testing
Teams lack reliable ways to catch AI agent regressions and failures before and after production releases.
In their words
the incident that broke us wasn't a hallucination, weirdly. it was a tool-call regression.
three tools, real budget, still seeing prod failures none catch
Has anyone built a similar unattended coding and UI-testing loop?
Where does AI agent evaluation fit in your MLOps pipeline? (Asking because ours doesn't, and it's becoming a problem)
agent eval latency added 18 minutes to our CI. how are you running this without killing dev velocity?
Anyone maintaining a real agent regression suite, not just eval prompts in a spreadsheet?
our "eval" right now is me checking outputs in the playground
the benchmark had stopped describing production
How are you scoring valid path variation in your eval pipeline without giving genuinely unsafe shortcuts a pass?
It’ll make 1 change and run a full suite that takes 30x longer than the change it made.
What's missing is a reliable middle layer. Something that tests interactions without requiring a full system run.
Comparing final outputs misses the second case. Comparing full trajectories flags the first case as a failure when it isn't one.
We blocked out ~2 months to build out a testing system for our internal agentic business processes, and I just found out there's a startup with $50 million in seed funding to answer this exact question.
pre-prod agent debugging is exactly where I lose the most time.
AI agent developers
Where they talk
- r/AI_Agents
- r/LLMDevs
- r/LangChain
- r/aiagents
- r/ClaudeCode
- r/vibecoding
- r/mlops
- r/n8n
Tools they use today
- Braintrust
- LangSmith
- Claude Code
- Langfuse
- Claude
- testmu
- ChatGPT
- MCP
Why those fall short
- tracing tools light on adversarial eval; prompt tools weak on multi-turn; text grading misses tool-call trajectory
- existing plugins are much more complicated than required
- promptfoo couldn't do multi-turn behavioral scenarios; evals only measure what you anticipate
- judge calls dominate; no tiering, caching or parallelism
- manual playground checks don't scale or catch low-probability failures
Map of ideas
Drawing the map…