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Parallel-agent AI prompt automation platform

Echo Prompter

An enterprise platform that runs prompts 10–100× faster with parallel agents, and regression-tests AI output for drift and hallucinations.

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  • Agents
  • .NET
  • Python
  • ChatGPT · Gemini · Claude
  • LLM testing

Overview

Echo Prompter is an enterprise-grade platform for data-driven AI execution, built for a client in the United States. I was architect and developer: a high-performance .NET core that manages parallel execution agents, Python workflows that orchestrate ChatGPT, Gemini and Claude, and testing tools that make probabilistic output measurable.

How it works

  1. 1Prompt dataset
  2. 2.NET scheduler
  3. 3Parallel agents
  4. 4ChatGPT / Gemini / Claude
  5. 5Regression checks
  6. 6Results + cost report

What I built

Parallel execution agents

A .NET backend schedules and manages many agents at once, taking prompt processing from sequential to 10–100× faster.

Multi-model orchestration

Python workflows route prompts across ChatGPT, Gemini and Claude through one REST API.

Regression testing for AI

Automated regression and load tests designed for probabilistic output, catching drift and hallucinations before they reach users.

Cost and observability

Real-time cost tracking and observability for large-scale runs, so every batch has a known price and a trace.

Engineering decisions

  • Split the work by strength: .NET for high-throughput concurrency and agent management, Python for the fast-moving LLM ecosystem.
  • Treat AI output like any other code under test: regression suites that tolerate probabilistic variation but flag real drift.

Stack

Backend
.NET (C#), REST API
AI workflows
Python
Models
ChatGPT, Gemini, Claude
Quality
Regression and load testing for LLM output, cost tracking, observability

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