How to Build Multi-Agent AI Systems That Actually Work in Production | Tyler Fisk
🎯 Summary
Comprehensive Summary: How to Build Multi-Agent AI Systems That Actually Work in Production | Tyler Fisk
This 101-minute podcast episode features Tyler Fisk, an experienced AI agent builder, demonstrating the process of moving from conceptual “vibe coding” to building and productionizing functional, multi-agent AI workflows, using the creation of an expert customer service agent for Apple as a live case study. The core narrative emphasizes the mentality of an AI practitioner—similar to a Forward Deployed Engineer—which prioritizes deep problem understanding, rigorous research, and structured workflow design over simple prompt engineering.
1. Focus Area
The primary focus is the practical construction and productionization of multi-agent AI systems for real-world business applications, specifically customer service automation. Key technologies discussed include LLM playgrounds (Typing Mind), specialized agents (Gigawatt, Clear), research tools (Perplexity), and knowledge retrieval systems (RAG).
2. Key Technical Insights
- Agent Specialization and Hierarchy: Effective production systems require specialized agents. The example builds an Expert Agent (“Core”) whose sole job is deep research and information synthesis, feeding verified data to a second Customer Service Agent (“You’ve Got Mail”) responsible for final customer communication.
- Structured Information Flow (JSON Output): For robust inter-agent communication in production, agents should output structured data (like JSON) rather than natural language. This makes parsing information between LLMs significantly easier and more reliable.
- Layered Knowledge Retrieval: The expert agent’s intelligence relies on a strict hierarchy: 1) Primary: RAG database (scraped official documentation), 2) Secondary: Built-in system instructions, 3) Tertiary: Verified web search with confidence scoring.
3. Business/Investment Angle
- Productionization is the Next Frontier: The market is moving past basic prompting toward deploying complex, reliable, multi-agent workflows that solve specific business problems (like customer service).
- Skillset Demand: The required skillset mirrors that of a Forward Deployed Engineer, focusing on understanding the business context before building, suggesting high value for practitioners who master this structured approach.
- Tooling Development: Tyler is actively working to productize his internal agent-building infrastructure (Gigawatt) to automate the entire setup process, indicating a market opportunity for platforms that streamline agent creation and deployment.
4. Notable Companies/People
- Tyler Fisk: The expert guest, known for teaching thousands of students and implementing agents for hundreds of businesses.
- Gigawatt: Tyler’s proprietary AI prompt engineering/AI engineering agent used as the initial architect and brainstorming partner.
- Clear Agent: A specialized agent focused on writing high-quality, deep research prompts for tools like Perplexity.
- Cassidy AI: Mentioned as a no-code platform used for efficient web scraping to populate the RAG knowledge base.
- Meta: Referenced for the Chain of Verification methodology, which is incorporated to reduce hallucinations.
5. Future Implications
The industry is rapidly evolving toward autonomous, multi-agent collaboration where complex tasks are broken down and delegated to specialized AI workers. The future involves agents that can self-improve (meta-prompting) and operate with minimal human oversight once the initial architecture and system instructions are established. The emphasis shifts from what the LLM knows to how the system orchestrates the LLMs to find, verify, and synthesize information.
6. Target Audience
This episode is highly valuable for AI Engineers, ML Practitioners, Prompt Engineers transitioning to production roles, CTOs, and technical founders interested in deploying reliable, scalable, and complex AI workflows rather than simple chatbot interfaces. A basic understanding of LLMs and system design is assumed.
Detailed Narrative Arc: The episode begins with Tyler Fisk immediately diving into a live build session within the Typing Mind playground, utilizing his Gigawatt agent connected to an LLM (Sonnet). The goal is to architect a two-agent system to handle Apple customer service emails.
The initial phase focuses on alignment and requirements gathering. Tyler uses conversational language with Gigawatt, which responds by asking clarifying questions about the scope (consumer products vs. enterprise), the types of customer scenarios (troubleshooting, billing, etc.), and the required information sources. This conversational interaction is made possible by Gigawatt’s extensive, pre-trained system instructions (written in XML for Claude compatibility).
The build then transitions into data acquisition and research orchestration. Tyler demonstrates parallel processing:
- Setting up a domain scrape of
apple.comusing Cassidy AI to feed the RAG database. - Tasking Gigawatt to identify three areas requiring deep research.
- Calling in the Clear Agent to translate Gigawatt’s requests into optimized deep research prompts, which are then executed simultaneously across Perplexity and Claude.
Finally, while the research runs, Tyler instructs Gigawatt to generate a Product Requirements Document (PRD) for the primary research agent, which he names “Core.” This step emphasizes the importance of formal planning before execution. The discussion concludes by highlighting the necessity of separating roles (research vs. communication) to prevent role confusion and ensure the research agent focuses strictly on intelligence gathering, not customer interaction. The efficiency gained through this conversational, multi-agent orchestration is noted as exponential compared to manual prompting.
🏢 Companies Mentioned
đź’¬ Key Insights
"The first thing I would say is we would never put it into production without some sort of a human-in-the-loop checkpoint. That's very irresponsible."
"Like these are all data points that we can then come back in and make adjustments on to Dallas and further, but really quickly and under what has it been now, like an hour, hour and a half, we've been able to build two agents that after some testing here, we could put them into a workflow and connect them to their tech stack and then have them in a production type environment very quickly."
"If you're productionizing, it's crazy cheap. Oh my god. People should go and look into how much it costs to process documents like what I said. Basically do what you would pay for OCR, but now you can use Flash for that even. Holy smokes. It's so much cheaper."
"Our RAG database is connecting into Graph RAG, which holds much more relational information in there and it updates it as well."
"The Professor might not bring back the best information because the more you add into a RAG system, it's powerful, but it can also degrade the retrieval of it, the quality of the retrieval, because there's just so much information for it to be looking through."
"especially with things like PDFs or documents that might have things in it that are also not text, that might be visual, this is where something like Gemini Flash is really good at that. It's extremely inexpensive to basically do OCR now and not only extract the text that's on the page... but it can look at the visuals and you can have a flow set of word actually extraction describes that and turns it into the vector store data as well."