I’m working on developing an automated support agent system for our company and I heard about using LangSmith and LangGraph together. We want to build something similar to what big tech companies use for their customer service.
Has anyone here successfully implemented these tools for enterprise-level support automation? I’m particularly interested in how to set up the workflow and what challenges you might have faced during development.
Our goal is to handle common technical issues automatically while still maintaining quality responses. Any tips on getting started with this kind of project would be really helpful. Thanks!
the biggest shock? user adoption. even when our AI nailed every response, customers still wanted to talk to humans. took months of slow rollout before they’d trust it. langsmith showed we hit 85%+ accuracy, but users didn’t care at first. we ended up adding confidence scores so the bot could say “i’m pretty sure” or “let me grab someone else.” also heads up - your human agents will push back hard. they’ll think you’re replacing them. we had to reframe it as “the bot handles the boring stuff so you can tackle the complex problems.”
Just deployed LangGraph for our support team last quarter. Workflow design matters, but here’s what nobody mentions - data quality makes or breaks everything.
Wasted 3 weeks figuring out conversation flow structure. LangGraph’s node system works great, but map out every customer journey first. I created detailed flowcharts for our top 20 support scenarios before touching any code.
LangSmith’s evaluation tools kept us from shipping terrible responses. Build proper test datasets early - we pulled real customer conversations from last year. The feedback loop between LangSmith monitoring and LangGraph improvements was a game changer.
This helped me grasp the full pipeline:
Biggest gotcha? Token costs explode with complex workflows. Had to optimize prompts aggressively because our initial setup burned through API credits. Also, version control your prompts like code - trust me.
Start with one simple use case, perfect it, then expand. We launched with billing questions only, added technical troubleshooting after nailing the foundation.
The integration complexity hit us hard when we rolled this out 10 months ago. LangGraph’s great for conversational flow, but connecting it to your CRM and knowledge systems? That needs real planning. We totally underestimated the custom middleware we’d have to build. LangSmith’s tracing became a lifesaver once we had multiple conversation branches going. Without proper logging, debugging failed interactions was a nightmare. The platform shows you exactly where things go wrong - saved us tons of time. Big lesson here: get dedicated resources for prompt engineering. We thought devs could handle it part-time, but they can’t. Creating enterprise prompts that keep your brand voice while solving technical problems is specialized work. Hire someone with NLP experience. Performance under load was brutal. Testing went fine, but we got crushed during peak hours with concurrent conversations. LangGraph’s async processing helped, but we still had to build proper queuing systems. Document everything. When your AI starts acting weird months later, you’ll need detailed records of prompt changes and workflow tweaks to figure out what broke.
Been running LangGraph for 8 months in financial services support. Architecture’s everything - design your agent workflow to match how your human agents actually think. Makes a massive difference. LangGraph’s state management rocks when you’re tracking conversation context across multiple customer exchanges. What blindsided us? Training on our internal knowledge base took forever. The AI has to deeply understand your products, policies, procedures - all of it. We spent weeks just cleaning and structuring docs so LangSmith could index properly. Integration with existing ticketing systems was brutal - plan for that early. LangSmith’s monitoring is solid for tracking response quality, but set clear metrics upfront. You need to know what’s a successful resolution vs when humans need to jump in.
We rolled out something similar 6 months back. Biggest takeaway? Start way smaller than you think. Don’t try tackling all tech issues at once - we kicked off with just password resets and basic account stuff. LangGraph worked great for building decision trees that knew when to escalate. The real pain point was keeping prompts consistent across different question types. LangSmith’s monitoring caught where things went sideways, but took us several rounds to nail down prompts that actually worked in production. We also totally underestimated testing time - edge cases and company-specific terms ate up way more hours than expected. Build solid fallback options right from the start. Your AI will get stumped, so you need smooth handoffs to real people. LangSmith’s dashboards saved us here - showed exactly where handoffs happened most so we could fix those workflows first.
