TryAgent
On-call for the decisions your AI agents escalate
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TryAgent - On-call for the decisions your AI agents escalate

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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.

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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?