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Vigilator – human-in-the-loop layer for AI agents

Hacker News

Vigilator – human-in-the-loop layer for AI agents

Hi - I'm Noah, a student working out of the UK & I've been building Vigilator for the past couple of months, Vigilator provides an agnostic solution for handling interrupts and observing agents as they work. I was inspired to build Vigilator after I realised as teams roll out AI agents across organisations there's no consistent way to interrupt an agent mid-task, get a human decision, and resume - especially when multiple people can be responsible for weighing in on the decisions. Vigilator provides a dashboard, where you and your teammates can join a virtual organisation - from here you can use our Py/TS SDK or simply make API calls yourself - then from here tool calls can be approved/reviewed/denied and agents can be monitored as they work. We provide a workload management system meaning that you can have multiple team members at once handling interrupts and watching agents as they work. Vigilator is still early, while Vigilator is framework agnostic I've primarily focused on adding support and examples for Mastra, Langchain Deep Agents and Langgraph - and I would love feedback, particularly from people running agents in production at scale. try it: https://vigilator.ai Docs: https://docs.vigilator.ai (conceptual overview: https://docs.vigilator.ai/concepts ) If you would like to get in contact feel free to shoot me an email at noah@vigilator.ai

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, email · Missing: mac, macos, cursor
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: month, way · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
27%27% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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