AI Agents are executing APIs and moving money. Who is actually holding the kill-switch?
Hey Product Hunt! 👋
We’ve all seen the shift over the last year. We went from building simple, sandboxed chatbots to unleashing autonomous AI agents that can read internal drives, call external APIs, and even trigger payment workflows.
But let’s be honest for a second: Building agents feels a lot like handing a loaded credit card to a brilliant, highly-distractable intern. 😅
We've talked to dozens of teams who are terrified of three things shipping to production:
Goal Hijacking / Prompt Injection: A rogue email or document tricking an agent into bypassing its original instructions.
Shadow Agents: Developers spinning up untracked, over-permissioned integrations that security teams don’t even know exist.
The "Slack Incident" Nightmare: An agent makes a massive mistake, and the only "audit trail" you have is a panicked message in Slack with zero context on why the agent made that decision.
We built Lineation because we believe enterprise teams shouldn't have to choose between automation and absolute chaos. We wanted to build a literal "black box flight recorder" for AI agents—giving you a policy plane that actually travels with the workflow and lets you replay an agent's reasoning step-by-step.
I’d love to hear from the builders and security folks here:
What is the "scariest" thing you’ve seen an AI agent try to do in development or production?
How are you currently handling permission gates when your agents need to call sensitive APIs?
Do you trust your current LLM gateways to stop prompt injection, or are you building custom wrappers?
Let’s swap some agent horror stories (and solutions) in the comments! 👇
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