u/Intrepid_Parking_225 — reddlx

TL;DR: Anthropic's Managed Agents are sick. LangGraph Deep Agents is solid but takes some tuning. Don't build your own Slack connector.

A customer saw LangChain's GTM agent post and asked if we could build it. We did, it works great, and so we open-sourced it. Has dependencies on our CLI but feel free to port to your own API integrations/another backend.

Repo: github.com/jptoor/deepline-gtm-agent

What it's good for?

Kicking off simple tasks we have skills for remotely via Slack.
Easy jobs with lots of potential edge cases.
Skills I want as "lambdas" (ex. /last30days) but didn't know how to do that easily. You can do anything you can do in CC via Slack, but it's slow and expensive relative to CC. (Screenshot attached)

First pass on deep agents about an hour total - 15min to get first agent up, 45min debugging slack :). To get it live and in a good spot on Managed Agents took ~30min.

What I learned:

Deep Agents did the heavy lifting — tool-calling, memory, subagent orchestration. Needed some iteration to get waterfall routing right but the framework was solid.
However, Managed Agents worked out of the box because all our skills were already designed for Claude Code.
90% of the work on this project was the Slack connector and formatting. Don't do do that. Friends said Vercel's Chat SDK and that is my only regret. Way easier, handles the OAuth/event subscription mess for you.
You need redis/db for chat thread memory or it is annoyingly bad at context management.

More detailed write up on technical implementation challenges that would have saved me time if I'd known (for your agents, I assume)