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Introducing LangSmith Fleet: an enterprise workspace for creating, using, and managing your fleet of agents. Fleet agents have their own memory, access to a collection of tools and skills, and can be exposed through the communication channels your team uses every day. Fleet includes: → Agent identity and credential... show more
149,147 views • 7 months ago •via X (Twitter)
15 Comments

The memory-per-agent model is the right call. Shared memory across a fleet is where multi-agent systems fall apart - you get state collisions, context bleed, agents stepping on each other's work. Isolation first, then controlled sharing.

Nice ship! We're focused on the other side of the "Fleet"- running autonomous agents locally via the CLI. Our v1.14.0 lets the terminal catch webhooks and fix bugs while the team is away. Local > Cloud for dev flow.

Do we have AI agent managers yet ? Just like we have AI product managers @grok

Agent Identities 🪪👌

Can we on-board agents from various platforms?

Mine

memory and tools per agent is the right abstraction. giving each agent a coherent scope has always been the hard part. fleet as a managed primitive makes that tractable at org scale

👁️👁️ Ai agent worksoace

Everyone's excited about agent fleet management. But the real bottleneck isn't identity/permissions—it's knowing when agents should NOT run. Most teams are automating ambiguity. Give your agents clearer specs before you give them Slack bots.

Fleet management is a symptom, not a solution. 15 agents with good rules outperform 150 with good dashboards. You don't scale by controlling more—you scale by designing systems that don't need you.

Isolation first, then controlled sharing is the right sequencing for fleet scale

exploring their use case in ai frens to ramp it up

Whoa 🔥

hmm i wonder if its actually good or claude code/codex with multiple agents is just better

Can I be real with you? I’ve been doing like hard core data science with agents for the last 2+ years. Even gone so far as invented my own workflow manager that exceeds airflow and dagster in core data passing (4M row csv handled w/ ease) and now I’m in the UI layer -use to be pure python. I say all this to say that I didn’t draw for langchain because I haven’t seen one actual real use case I could adapt. Not from your own shop or from any of your customers. Can you post a video of one of you agents doing real useful work? I’ll do the same

