A problem I keep running into, and hear from others, is teams embedding agents directly into their backend because it’s 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.
A problem I keep running into, and hear from others, is teams embedding agents directly into their backend because it’s 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.
This is interesting.
Has anyone here run agents as separate services rather than embedding them directly in their app? Curious what that experience was like.
I have a lot of DevOps exp so I used to do this for all my agents. But it was so time consuming and tedious lol I wanted to automate it.