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Managed Deep Agents v0.9
Agents that schedule their own follow-ups
Managed Deep Agents v0.9 from LangChain adds three things for internal agents that work like teammates. Agents can now create their own reminders and recurring tasks mid-conversation, running with the requester's permissions and posting back to the same channel. One deployment can pick its model, skills, tools and sandbox on every run. And agents can react to Slack messages right away. Public beta.
Major update from @LangChain: Managed Deep Agents v0.9. It's their hosted runtime for long-running agents, and this release is aimed at internal agents that behave like teammates in Slack and other channels.
You now get:
Agents that schedule their own reminders, follow-ups and recurring tasks mid-conversation
One deployment that picks its model, skills, tools and sandbox fresh on every run
Slack reactions, so people know the agent saw their message before it replies
What's new?
Agent-created schedules: A new Schedules SDK lets agents handle requests like "remind me about this tomorrow" or "check on this deploy in an hour." The agent creates the schedule itself during the conversation, it runs as the person who asked using their permissions and connections, and results post back to the same channel. Recurring schedules use cron, and one-time ones reply in the original thread.
Per-run configuration: Define your agent as a function that receives the runtime at the start of each run and returns its setup: model, instructions, skills, MCP servers and sandbox. Instead of keeping near-copies of one agent for every team or repo, you run one deployment. The agent never sees skills or tools outside its configuration, which keeps its context small and doubles as access control. It also means the model isn't left to choose its own toolkit when you already know what it should have.
Slack reactions: The agent reacts to the message that starts a run while it's still reasoning and calling tools. You can turn it off, pick another emoji, or choose one per message with a function. You can even use a model to pick a more fitting emoji, like 🐛 for something broken.
About Managed Deep Agents v0.9 on Product Hunt
“Agents that schedule their own follow-ups”
Managed Deep Agents v0.9 was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #101 on the daily leaderboard. Managed Deep Agents v0.9 from LangChain adds three things for internal agents that work like teammates. Agents can now create their own reminders and recurring tasks mid-conversation, running with the requester's permissions and posting back to the same channel. One deployment can pick its model, skills, tools and sandbox on every run. And agents can react to Slack messages right away. Public beta.
On the analytics side, Managed Deep Agents v0.9 competes within Artificial Intelligence — topics that collectively have 481.6k followers on Product Hunt. The dashboard above tracks how Managed Deep Agents v0.9 performed against the three products that launched closest to it on the same day.
Who hunted Managed Deep Agents v0.9?
Managed Deep Agents v0.9 was hunted by Kumar Abhishek. 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 Managed Deep Agents v0.9 including community comment highlights and product details, visit the product overview.
Kumar Abhishek
Major update from @LangChain: Managed Deep Agents v0.9. It's their hosted runtime for long-running agents, and this release is aimed at internal agents that behave like teammates in Slack and other channels.
You now get:
Agents that schedule their own reminders, follow-ups and recurring tasks mid-conversation
One deployment that picks its model, skills, tools and sandbox fresh on every run
Slack reactions, so people know the agent saw their message before it replies
What's new?
Agent-created schedules: A new Schedules SDK lets agents handle requests like "remind me about this tomorrow" or "check on this deploy in an hour." The agent creates the schedule itself during the conversation, it runs as the person who asked using their permissions and connections, and results post back to the same channel. Recurring schedules use cron, and one-time ones reply in the original thread.
Per-run configuration: Define your agent as a function that receives the runtime at the start of each run and returns its setup: model, instructions, skills, MCP servers and sandbox. Instead of keeping near-copies of one agent for every team or repo, you run one deployment. The agent never sees skills or tools outside its configuration, which keeps its context small and doubles as access control. It also means the model isn't left to choose its own toolkit when you already know what it should have.
Slack reactions: The agent reacts to the message that starts a run while it's still reasoning and calling tools. You can turn it off, pick another emoji, or choose one per message with a function. You can even use a model to pick a more fitting emoji, like 🐛 for something broken.