As an engineer working on content generation, I wanted to share our experience building a fully autonomous system that researches, writes, and produces podcast content. This post covers the architecture decisions, technical challenges, and key learnings from building an AI agent that handles the entire content pipeline.
The Vision: Reliable Content Infrastructure
Our goal was to create a robust system that could reliably handle podcast content creation from research to final audio file. The approach we took demonstrates practical ways to leverage AI for scalable content operations.
System Architecture: The Technical Implementation
1. The Research Pipeline
The research pipeline is the foundation of our system. It continuously monitors e-mail newsletters and RSS feeds and determines which stories are worth covering. We designed it to be both thorough and cost-efficient, scaling to hundreds of potential stories per day while not spending the entire marketing budget on AI calls.
The research component has several main parts:
- A layer that reads newsletters and RSS feeds
- An LLM pipeline that filters and scores interesting stories
- A fact-checking deep research system that:
We use LLMs to understand both what a story means and why readers would care about it. This multi-step approach also helps to be factually accurate as errors in one source usually gets dissolved by the other sources.
2. Content Generation Architecture
Writing compelling content requires more than just stringing words together. Our generation system breaks down the writing process into distinct phases, each handling a specific aspect of content creation. This approach gives us fine-grained control over the output and makes the system easier to debug and improve.
The writing system works in stages:
- One part builds the story structure
- Another manages the AI models
- A third keeps writing style consistent
- A final part checks quality
Breaking this into smaller pieces works better than using one big model.
3. Quality Assurance Pipeline
Quality control is critical for autonomous content generation. We built a comprehensive verification system that catches issues before they reach production. Each piece of content goes through multiple validation steps to ensure it meets our standards for accuracy and engagement.
Our QA system checks:
- Facts match across sources
- Content follows our rules
- Writing style stays consistent
- Content is engaging
4. Audio Production Pipeline
The audio production pipeline is responsible for taking the written content and turning it into a podcast episode. Although we do use ElevenLabs for the voices, we did some custom work on prompt engineering and audio processing to make the output feel more natural.
The pipeline includes:
- Generating voice direction parameters to encode intentionality into segments
- Recording podcast segments separately to prevent voice degradation
- Joining the segments together
- Normalizing the volume and speeding it up slightly to keep listeners engaged
Engineering Decisions and Trade-offs
During development, we encountered several interesting technical challenges:
- Modular Architecture vs. End-to-End Models
This means we chose an agent composition approach to build a system that is easy to maintain and extend. We have a Producer agent that orchestrates the other Researcher, Writer and Mixer agents.
- We can test each agent in isolation
- We can swap out different models for each agent
- We can add new agents to the pipeline without breaking existing code
We had to be careful with the prompts to avoid hallucinations and ensure the agents were able to work together.
- Implemented a dynamic prompt template system (Open source @promptt/client)
- Tested various prompt templates to find the best ones for the agents
- We monitor listening minutes and plan to explore how prompt changes impact engagement
Building Your Own AI Agents
Building autonomous AI agents requires careful consideration of architecture, prompt engineering, and quality control. While this post outlines our approach to podcast content generation, every use case brings its own unique challenges. If you are interested in working on AI agents or autonomous systems and need help, reach out to us at BitComplete!
