Multi-Agent AI App: Current

Current came to Grassurn with an app built by a previous development team that had no real system architecture and no agentic AI expertise behind it. It looked 'finished' without actually ready to release for users.

What Wasn't Working

• No system architecture: the app was assembled without service boundaries or a scalable data layer, so every new feature risked destabilizing the whole product.
• Lack of agentic AI expertise: the flagship "Ask Current" feature was scoped as a single prompt bolted with Gemini API with. No memory, no tools, no safety layer which meant there was no path past a demo.
• No development discipline: No migration safety net or test coverage. Development environment was termed 'Production ready'. The only discipline followed here was shipping anything within hours of a request which essentially meant shipping blind.
• Far from production readiness: the app could survive a walkthrough but not real users, load or edge cases. Business team spent over $70K for just a product demo.

The Real Cost here was that every week spent building on top of an unstable foundation pushed Current further from a defensible AI product and closer to a costly rebuild.

Grassurn's investigation went past the surface ask of "build the chat feature" and found the initial scoping, both technical and product, was misaligned and never initiated to be ironed out. Ask Current didn't need a smarter chatbot, it needed to NOT be a chatbot at all.

Key Issues Identified

• Product Scoping: the original brief assumed a Q&A chat interface bolted onto a map was the right shape for the feature, when what users actually needed was a fast conversational path from "I want to go out" straight to a finalized venue.
• Grounding & Freshness: without grounding data and a regular venue pipeline behind it, any chat layer would be answering from stale, static listings instead of what venues actually looked like tonight.
• Voice & Intent: users didn't need a bot that asked clarifying questions. Users needed a "Current" persona, a buddy that reads intent from one casual message and gets someone to a venue decision within seconds.
• Data as the Strategy: Grassurn identified the deeper business opportunity here. Current shouldn't run on web-only listings at all. It should be a user-first data submission system, where what users report about a venue becomes the ground truth that shapes the answers and suggestions the next user gets.

The Verdict : Ask Current didn't need a better chatbot. It needed to stop being a chatbot altogether: a grounded, always-user first, user-voiced buddy running on the community's own words as Current's real data engine.

Rebuilding the AI layer wasn't about reaching for the newest agent framework. It was about giving Current an AI system its business could actually run on, built by a team that acted as a partner in the product, not a vendor taking orders.

What We Built

• Multi-Agent Architecture: replaced the single prompt with a governed agentic system comprising of Supervisor Orchestrator, Match Agent, Venue Intelligence Agent, Content Guard, Nudge & Engagement Agent, and Owner Matcher — each scoped, designed, developed and rolled out by priority.
• Grounded, Memory-Aware Chat: Vertex AI/Gemini with RAG grounding over vector-embedded venue data, live tool-calling for venue retrieval, and multi-turn session memory, so Ask Current answers from real venues with near-realtime data instead of guessing and hallucinating while running.
• Eval-Driven Safety: 184 eval scenarios across 21 personas, adversarial red-team pairs, and a 9-layer production-readiness framework run against every change before it ships.
• AI Product Strategy: Grassurn set the agent roadmap against Current's actual value proposition and proposed the production-readiness and go-to-market sequencing, not just the code to get there

The Partnership Difference Grassurn ran design-sync reviews, exec-alignment sessions directly with the client's team and kept the engagement lean, so Current got senior AI strategy and hands-on build without agency overhead or budget bloat.