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.
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.
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.
Frequently asked questions
What did this team achieve with this AI workflow?
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…
What tools did this team use?
Zendesk, Slack, GPT-4 Turbo, Google Doc, YouTube, RAG, Google Workspace.
What results were reported?
meeting time replaced by async Q&A: instead of going to like a 60-minute meeting, I have like a five-minute chat; User prompts that are unintelligible: 60 or 70%; agent reliability after Lindy 2.0: way more reliable, way easier to set up; new use cases from Lindy 2.0: a ton of new use cases pop up (source-reported, not independently verified).
What failed first in this deployment?
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.
How is this customer support AI workflow structured?
Meeting begins: Lindy records → AI generates coaching notes → Summary and coaching notes emailed → Notes disseminated to Slack → User reply restores meeting context.