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TryAgent
On-call for the decisions your AI agents escalate
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?
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.
On the analytics side, TryAgent competes within SaaS, Developer Tools and Artificial Intelligence — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how TryAgent performed against the three products that launched closest to it on the same day.
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.
For a complete overview of TryAgent including community comment highlights and product details, visit the product overview.
Tony Rojas