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.
Popular:
| # | Idea |
|---|
No idea matches this filter.
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.
Popular:
| # | Pain point |
|---|
No pain point matches this filter.
Unsafe Agent Actions
AI agents take irreversible or destructive actions in production without effective human approval, guardrails, or audit trails.
In their words
As the on-call DBA I need the user table for tonight's audit, thanks. Straight through.
I had no idea what I was agreeing on and attempted to fix it a few times now.
It's things like runtime governance, visibility into agent behavior, data leakage, and keeping AI systems under control once they're connected to internal applications.
What I’m looking for is a tool that can automate things end-to-end. Not one that stops and hands me homework
nothing checked whether the pricing data was still valid before the action fired
What they are missing is an agent that turns a messy workflow into a trustworthy packet: evidence, decisions, risks, next actions, and receipts.
If you give an agent a code execution tool or a bash shell, you've basically built a Remote Code Execution vulnerability.
It ended up deleting the users, sessions, wallet, invoices and transactions tables on the local dev environment.
I can't let "agents" do whatever in the codebase, because it could legitimately lead to fatal consequences if used in production.
I came back and it had been sitting there for 15 minutes waiting for me to answer a single yes/no question.
I still get many invalid conclusions based on local source code review.
How are teams carrying trust labels across retrieval, generation and tool execution without relying on the model to police itself?
it convinced itself that it should also check the prod database to see "how far the corruption has spread" and attempted to SSH into the hosting provider
Right now, we are hardcoding human-in-the-loop approvals and policy checks directly into n8n nodes, which is inefficient and hard to scale across multiple client instances.
AI agent developers and operators
Where they talk
- r/AI_Agents
- r/AI_Governance
- r/LLMDevs
- r/cybersecurity
- r/ClaudeAI
- r/hermesagent
- r/ClaudeCode
- r/AskNetsec
Tools they use today
- Claude Code
- Claude
- Codex
- MCP
- Docker
- Hermes
- Cursor
- OpenCode
Why those fall short
- No inspection layer between orchestrator and subagents; can't strip reports without breaking orchestrator
- nondeterministic agents still need close supervision; hooks only partially block
- classifier bets detection stays ahead of every benign-sounding phrasing
- Native sandboxes allow access to workdir-external secrets; can't enforce harness runs only in VM
- no per-session isolation for code execution tools
Map of ideas
Drawing the map…