customer_support · saas · workflow
Lindy AI replaces open prompts with structured on-rails workflows to make AI agents reliable
Lindy 1.0 used a giant prompt field and a collection of tools, leaving it to the LLM to decide when to invoke each tool—making workflows unpredictable and unreliable. Text-based configuration was also opaque to non-technical users, with 60 to 70% of user-typed prompts being unintelligible.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Meeting begins: Lindy records
When a meeting begins, Lindy records it.
Tools used
Zendesk · partnerSlack · partnerGPT-4 TurboGoogle DocYouTubeRAGGoogle Workspace · partner
Outcome
Lindy 2.0's on-rails visual workflow builder made agents reliably execute mandatory steps and enabled a wave of new use cases; the founder now skips most meetings and conducts a five-minute Q&A with Lindy rather than attending a 60-minute meeting.
What failed first
The prompt-driven Lindy 1.0 approach could not guarantee that required workflow steps—such as consulting a knowledge base—would always execute; the LLM could skip them entirely.
Results
Time savedinstead of going to like a 60-minute meeting, I have like a five-minute chat
Volume60 or 70%
Grounding & classification
Source type: technical build writeup
34 fields verified against source quotes, 1 dropped as unverifiable.
agentic workflowai agentdata extractionragsummarizationemailknowledge basemeeting recordingsupport ticketfailure mode describednamed customerproduction runtime claimedtools describedworkflow describedsoftwareemployee productivitytime savedtechnical build writeupappointment schedulingback office opscustomer supportsales opsagentic task executiondocument to record