Managed Deep Agents v0.9
Agents that schedule their own follow-ups
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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.
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PH · launched Oct 19, 2026 · 3 upvotes (top 10%)
- Horizontal
- Agent / copilot
- Developer
- AI-native
- Commercial product
- Artificial Intelligence
Similar launches
See all 614 similar launches →Could you build this?
Partial A basic agent that schedules its own follow-ups can be vibe-coded on top of existing LLM APIs, but a production managed platform needs durable execution, permission models, multi-model routing, and reliability guarantees that are hard to build well.
What it would actually take: The stack would be an agent runtime (LangGraph or similar) with a durable job queue and scheduler (e.g., Temporal or a Postgres-backed queue), per-user permission scoping, and connectors to Slack or similar channels. The hard parts are reliable long-running execution, safe tool access with the requester's credentials, and observability. These require distributed-systems and security expertise.
Competitors
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Attention rank: #99 of 615 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 349 days after the earliest competitor.
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