Sales outreach · Production

Shipping an AI SDR Chatbot with LangSmith: What Actually Helped in Production

The problem

The AI SDR chatbot had to qualify leads, answer grounded product questions, route conversations, and write structured outcomes to CRM — but quality drifted on real traffic once users asked messy multi-part questions, tool call failures were opaque, and duplicate CRM writes from retry logic evaded manual QA.

First attempt

Without trace-level visibility the team could not identify which prompt version caused failures, what context retrieval actually returned, or which tool call failed silently — leaving them patching prompts reactively based on anecdotes.

Workflow diagram · grounded in source
1
Inbound lead message
trigger
“qualify inbound leads, answer product questions, and route the right conversations to the right humans”
2
Lead intent qualification
ai_action
“qualify intent (is this a partner, a brand, a merchant, an agency?)”
3
Product QA routing decision
routing
“Determine if product QA is needed based on conversation state.”
4
Knowledge base retrieval
ai_action
“Retrieve knowledge base context with error handling and validation from Qdrant vector store.”
5
CRM write with idempotency
integration
“write structured outcomes to downstream systems (e.g., lead notes, tags, and a handoff summary)”
6
LangSmith feedback and eval loop
feedback_loop
“mark good/bad outcomes on real production runs, create a prioritized queue of "fix these" examples, feed the best examples back into your evaluation dataset”
Reported outcome

LangSmith made the system debuggable, testable, and shippable through end-to-end tracing, structured feedback loops, and regression evaluations.
The duplicate CRM write bug was identified and fixed via trace inspection, and quality drift was halted by running evaluations on a curated conversation dataset.

Reported metrics
System shippabilitydebuggable, testable, and shippable
Quality drift between deploymentsstopped quality from drifting silently between deployments
duplicate CRM writesoperational noise from duplicate actions
Reported stack
LangSmithLangGraphQdrantPostgresSaverCRM
Source
https://www.lubulabs.com/ai-blog/langsmith-production-ai-sdr-loyalty-chatbot
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

LangSmith made the system debuggable, testable, and shippable through end-to-end tracing, structured feedback loops, and regression evaluations.

What tools did this team use?

LangSmith, LangGraph, Qdrant, PostgresSaver, CRM.

What results were reported?

System shippability: debuggable, testable, and shippable; Quality drift between deployments: stopped quality from drifting silently between deployments; duplicate CRM writes: operational noise from duplicate actions (source-reported, not independently verified).

What failed first in this deployment?

Without trace-level visibility the team could not identify which prompt version caused failures, what context retrieval actually returned, or which tool call failed silently — leaving them patching prompts reactively…

How is this sales outreach AI workflow structured?

Inbound lead message → Lead intent qualification → Product QA routing decision → Knowledge base retrieval → CRM write with idempotency → LangSmith feedback and eval loop.

Related sales outreach cases