A problem I keep running into, and hear from others, is teams embedding agents directly into their backend because itβs easy. But in production, debugging becomes painful and scaling gets expensive. Running agents as separate services fixes this, but the infra work is heavy, so most teams delay it until they are forced to rebuild.
I built Dank Cloud to handle that deployment layer so agents run as separate services you can call from your backend, with per-agent logs, GitHub-based deploys, and optional hosted vector memory.
The cloud is in beta and currently supports agents built with our open-source JS framework, with LangChain and CrewAI support coming next.
Would really appreciate feedback from anyone who has deployed agents in production or hit similar issues.
I built Dank Cloud to handle that deployment layer so agents run as separate services you can call from your backend, with per-agent logs, GitHub-based deploys, and optional hosted vector memory.
The cloud is in beta and currently supports agents built with our open-source JS framework, with LangChain and CrewAI support coming next.
Would really appreciate feedback from anyone who has deployed agents in production or hit similar issues.
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