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You've heard of Infrastructure as Code- but agent evals can now ride your existing Terraform setup! I've been using the new LangSmith Terraform provider to auto-provision online evals + monitoring alerts for my agents, and think you should too. Here's how 🧵
Together these three resources close the online evaluation and monitoring loop, with the added flexibility and benefits that infrastructure as code provides. If you want to set this up for your own agents, the provider is live on the Terraform registry and documentation can be checked out here: docs.langchain.com/langsmith/mana… registry.terraform.io/providers/lang…
But, as always, scores are only useful if someone sees them. The alert resource lets you set a threshold and target for your online eval, and send messages to common services like PagerDuty or a Slack channel. For example, I set up an alert that pings me directly if my PII code evaluator detects even one mention of highly sensitive information, something worth investigating!
No one wants to talk about how setting up agentic „loops” has almost nothing to do with the code being generated- rather effective tests, CI, scans, codebase rules, formatting/linters, documentation, historical references, QA tools, hooks, templates, tickets, reviews etc
A man cannot prompt the same Claude twice, for it is not the same Claude, and he is not the same man
@sydneyrunkle @hwchase17 All my homies use dcode
8 hrs of sleep really do change your life
i love infrastructure as code. instead of opening the GCP console 20 times I now only have to open it 19 times
Actually a critical concept to understand for anyone seriously building agents. I work on one that can spawn up to 50 subagents at once- the only way to do that reliably and effectively is with this feature
Memory is what turns an ok general agent into YOUR excelling agent- but requires constant reflection and updating Engine does this 24/7 for you
🧠LangSmith Engine as Sleep Time Compute Memory for agents is often described as “sleep time compute” or “dreaming” This involves running a background process to analyze agent trajectories and update a memory store Today, we show how to do that in LangSmith: 1. Trace all
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