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CASE STUDY

Prognos Labs

An enterprise-grade orchestration platform designed to route, test, and analyze autonomous LLM agent networks with structured audit logs.

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Prognos Labs

Overview

SCALING AGENTIC WORKFLOWS FOR THE ENTERPRISE

Prognos Labs serves as a command center for LLM agents. Companies deploy multi-agent loops that verify each other's outputs, routing queries based on system latency and model capabilities.

Node.jsLangChainPinecone DBOpenAI

The Problem

Debugging autonomous agent networks is complex. Outputs drift, token costs spike, and tracing agent pathways in production requires heavy logging overhead.

My Solution

Created a trace-flow diagram showing agent actions. Built optimized vector databases using LangChain and Pinecone to anchor chat threads, and streaming telemetry dashboards to monitor live token consumption.

Impact & Results

Measurable outcomes that drive business growth and user engagement.

94%Goal Completion Success
35%Token Cost Reduction
28Interconnected Agent Nodes
5Active Routing Paths