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AI agents now handle real work like refunds, writes, and approvals, but sometimes they hit a decision they shouldn't make alone. LangGraph can pause the run; the hard part is everything after. TryAgent is the incident-response layer for those moments. Your agent sends one escalation. TryAgent routes it by policy to the right human, runs an SLA with a safe-default fallback, captures a structured decision, logs an audit trail, and resumes your run via signed webhook. TS + Python SDKs.
Hey Product Hunt 👋
I built TryAgent after wiring the same flow into a couple of agents: the agent
hits something it shouldn't decide alone, and you need a human in the loop — fast,
to the right person, with a record.
Pausing the agent turned out to be the easy 10%. The other 90% was the operations
around the pause: routing to the right reviewer, an SLA so it doesn't hang
forever, a safe default when no one answers, capturing a clean structured decision
instead of a freeform reply, and an audit trail compliance would actually accept.
Then handing the decision back to the workflow so the run continues.
So TryAgent is that layer — think incident response / on-call, but for the
decisions AI agents escalate. It drops into a LangGraph interrupt, and ships TS +
Python SDKs.
Would genuinely love feedback on the routing + timeout model. What does
"escalate to a human" look like in your agents today?
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About TryAgent on Product Hunt
“On-call for the decisions your AI agents escalate”
TryAgent was submitted on Product Hunt and earned 6 upvotes and 1 comments, placing #87 on the daily leaderboard. AI agents now handle real work like refunds, writes, and approvals, but sometimes they hit a decision they shouldn't make alone. LangGraph can pause the run; the hard part is everything after. TryAgent is the incident-response layer for those moments. Your agent sends one escalation. TryAgent routes it by policy to the right human, runs an SLA with a safe-default fallback, captures a structured decision, logs an audit trail, and resumes your run via signed webhook. TS + Python SDKs.
TryAgent was featured in SaaS (43k followers), Developer Tools (515.4k followers) and Artificial Intelligence (473.1k followers) on Product Hunt. Together, these topics include over 229.2k products, making this a competitive space to launch in.
Who hunted TryAgent?
TryAgent was hunted by Tony Rojas. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Tony Rojas