Masterclass: How to Turn an AI Agent into a Real Product (No Code)

Tyler Fisk reveals how to build AI agents that actually work in production. He breaks down the meta-prompting system, shows how to build multi-agent architectures live, and demonstrates why most teams fail at production agents. --- Full Writeup: https://www.news.aakashg.com/p/tyler-fisk-podcast Transcript: https://www.aakashg.com/how-to-build-production-ai-agents-complete-masterclass-with-tyler-fisk/ --- Timestamps: 00:00:00 - Intro 00:01:34 - Tyler's Background: AI Agent Expert 00:03:00 - Live Demo: Building Apple Customer Service 00:04:12 - Gigawatt: The Agent That Builds Agents 00:16:08 - Ads 00:17:00 - Deep Research & Knowledge Base Setup 00:26:02 - Why Multi-Agent Systems Beat Single Agents 00:30:00 - Temperature Settings Explained (Ice Peak) 00:34:05 - Ads 00:39:47 - System Instructions & Meta-Prompting 00:51:26 - Testing Core & Echo Agents Together 01:05:50 - RAG Databases & Enterprise Documents 01:24:11 - Production Workflow with Human-in-Loop 01:35:10 - $1.6M Course Business Results 01:39:53 - Outro --- 🏆 Thanks to our sponsors: 1. Maven: Get $135 off Tyler’s course with my code AAKASHxMAVEN - https://maven.com/sara-davison/scale-with-aiworkflows-foundations?promoCode=AAKASHxMAVEN 2. Vanta: Get $1,000 off AI security & compliance at vanta.com/acos - http://vanta.com/aakash 3. Testkube: Leading test orchestration platform - http://testkube.io/ 4. Kameleoon: Leading AI experimentation platform - http://www.kameleoon.com/ 5. The AI Evals Course for PMs: Get $1155 off with code ‘ag-evals’ - https://maven.com/parlance-labs/evals?promoCode=ag-evlas --- Key takeaways: 1. Stop Vibe Coding: Most teams write one prompt, test twice, ship to production, and hope for the best. Tyler's rule: "We would never put it into production without a human-in-the-loop checkpoint. 2. Use Meta-Prompting to Build Agents: Tyler built Gigawatt—an agent with 72,000 characters of system instructions that builds other agents. It researches the domain, writes V1 instructions, evaluates itself (scores out of 100), identifies gaps, and rewrites to V2. Goes from 77% to 86%+ quality. 3. Build Multi-Agent Architectures: Don't build one agent that does everything. Separate concerns like you'd separate teams. For Apple: Core (expert agent, temp=0, finds facts) + Echo (email agent, temp=0.7, writes responses). 4. System Instructions Need 7K-9K Tokens: Structure includes Role (job description), Context (business details), Instructions (step-by-step process), Criteria (guardrails), Examples (meta reasoning). 5. Temperature Is Your Secret Weapon: Tyler's Toy Story analogy: Imagine an icy peak in a claw machine. Temp=0 (frozen): claw picks from top only—deterministic, precise. Temp=1 (melted): claw grabs anywhere—creative, varied. 6. Information Hierarchy Prevents Hallucinations: Priority order: RAG database first (scraped company docs), System instructions second (built-in expertise), Web search third (with chain-of-verification). When agents search without verification, they hallucinate. 7. Build Complete Workflows: Tyler's 9-step production workflow with 5+ agents: Email arrives → Sentiment analysis (Cinnamon) → Expert research (Core) → Email writing (Echo) → QA loop → Human checkpoint (Slack) → Generative filter → Send → Log to memory. 8. Observational Evals Come First: Test 20+ different scenarios manually. Include edge cases and adversarial inputs. Document every failure. Save golden examples. Only after building confidence do you add systematic evals in production. 9. Calculate ROI as Labor Cost Reduction: Traditional cost: $460/day (expert time + customer service rep + manager review) = $138K/year. AI cost: $153/day (platform fees + API credits + human review) = $45.9K/year. Savings: $92K annual (67% reduction). 10. Emotion Prompting Actually Works: Tyler ends every prompt with "Go get 'em slugger." Based on research: positive reinforcement improves LLM outputs by ~15%. The same psychology that works on humans works on LLMs. "Be nice to your AI. They're gonna have robot bodies soon." --- 👨‍💻 Where to find Tyler: Instagram: https://www.instagram.com/tyfisk/ LinkedIn: https://www.linkedin.com/in/tyfisk/ AI Build Labs: https://www.linkedin.com/company/ai-build-lab/ --- 👨‍💻 Where to find Aakash: Twitter: twitter.com/aakashg0 LinkedIn: linkedin.com/in/aagupta/ Newsletter: news.aakashg.com #aiagents #productmanagement 🧠 About Product Growth: The world's largest podcast focused solely on product + growth, with over 187K listeners. Hosted by Aakash Gupta, who spent 16 years in PM, rising to VP of product, this 2x/week show covers product and growth topics in depth. 🔔 Subscribe and turn on notifications to get more vidoes like this.

Aakash GuptahostTyler Fiskguest

Oct 16, 20251h 40mWatch on YouTube ↗

EPISODE INFO

Released: October 16, 2025
Duration: 1h 40m
Channel: Aakash Gupta
Watch on YouTube: ▶ Open ↗

EPISODE DESCRIPTION

Tyler Fisk reveals how to build AI agents that actually work in production. He breaks down the meta-prompting system, shows how to build multi-agent architectures live, and demonstrates why most teams fail at production agents. --- Full Writeup: https://www.news.aakashg.com/p/tyler-fisk-podcast Transcript: https://www.aakashg.com/how-to-build-production-ai-agents-complete-masterclass-with-tyler-fisk/ --- Timestamps: 00:00:00 - Intro 00:01:34 - Tyler's Background: AI Agent Expert 00:03:00 - Live Demo: Building Apple Customer Service 00:04:12 - Gigawatt: The Agent That Builds Agents 00:16:08 - Ads 00:17:00 - Deep Research & Knowledge Base Setup 00:26:02 - Why Multi-Agent Systems Beat Single Agents 00:30:00 - Temperature Settings Explained (Ice Peak) 00:34:05 - Ads 00:39:47 - System Instructions & Meta-Prompting 00:51:26 - Testing Core & Echo Agents Together 01:05:50 - RAG Databases & Enterprise Documents 01:24:11 - Production Workflow with Human-in-Loop 01:35:10 - $1.6M Course Business Results 01:39:53 - Outro --- 🏆 Thanks to our sponsors:
1. Maven: Get $135 off Tyler’s course with my code AAKASHxMAVEN - https://maven.com/sara-davison/scale-with-aiworkflows-foundations?promoCode=AAKASHxMAVEN
1. Vanta: Get $1,000 off AI security & compliance at vanta.com/acos - http://vanta.com/aakash
1. Testkube: Leading test orchestration platform - http://testkube.io/
1. Kameleoon: Leading AI experimentation platform - http://www.kameleoon.com/
1. The AI Evals Course for PMs: Get $1155 off with code ‘ag-evals’ - https://maven.com/parlance-labs/evals?promoCode=ag-evlas --- Key takeaways:
1. Stop Vibe Coding: Most teams write one prompt, test twice, ship to production, and hope for the best. Tyler's rule: "We would never put it into production without a human-in-the-loop checkpoint.
1. Use Meta-Prompting to Build Agents: Tyler built Gigawatt—an agent with 72,000 characters of system instructions that builds other agents. It researches the domain, writes V1 instructions, evaluates itself (scores out of 100), identifies gaps, and rewrites to V2. Goes from 77% to 86%+ quality.
1. Build Multi-Agent Architectures: Don't build one agent that does everything. Separate concerns like you'd separate teams. For Apple: Core (expert agent, temp=0, finds facts) + Echo (email agent, temp=0.7, writes responses).
1. System Instructions Need 7K-9K Tokens: Structure includes Role (job description), Context (business details), Instructions (step-by-step process), Criteria (guardrails), Examples (meta reasoning).
1. Temperature Is Your Secret Weapon: Tyler's Toy Story analogy: Imagine an icy peak in a claw machine. Temp=0 (frozen): claw picks from top only—deterministic, precise. Temp=1 (melted): claw grabs anywhere—creative, varied.
1. Information Hierarchy Prevents Hallucinations: Priority order: RAG database first (scraped company docs), System instructions second (built-in expertise), Web search third (with chain-of-verification). When agents search without verification, they hallucinate.
1. Build Complete Workflows: Tyler's 9-step production workflow with 5+ agents: Email arrives → Sentiment analysis (Cinnamon) → Expert research (Core) → Email writing (Echo) → QA loop → Human checkpoint (Slack) → Generative filter → Send → Log to memory.
1. Observational Evals Come First: Test 20+ different scenarios manually. Include edge cases and adversarial inputs. Document every failure. Save golden examples. Only after building confidence do you add systematic evals in production.
1. Calculate ROI as Labor Cost Reduction: Traditional cost: $460/day (expert time + customer service rep + manager review) = $138K/year. AI cost: $153/day (platform fees + API credits + human review) = $45.9K/year. Savings: $92K annual (67% reduction).
1. Emotion Prompting Actually Works: Tyler ends every prompt with "Go get 'em slugger." Based on research: positive reinforcement improves LLM outputs by ~15%. The same psychology that works on humans works on LLMs. "Be nice to your AI. They're gonna have robot bodies soon." --- 👨‍💻 Where to find Tyler: Instagram: https://www.instagram.com/tyfisk/ LinkedIn: https://www.linkedin.com/in/tyfisk/ AI Build Labs: https://www.linkedin.com/company/ai-build-lab/ --- 👨‍💻 Where to find Aakash: Twitter: twitter.com/aakashg0 LinkedIn: linkedin.com/in/aagupta/ Newsletter: news.aakashg.com #aiagents #productmanagement 🧠 About Product Growth: The world's largest podcast focused solely on product + growth, with over 187K listeners. Hosted by Aakash Gupta, who spent 16 years in PM, rising to VP of product, this 2x/week show covers product and growth topics in depth. 🔔 Subscribe and turn on notifications to get more vidoes like this.

SPEAKERS

Aakash Gupta
host
Host of the Aakash Gupta podcast/channel, interviewing builders about AI and product.
Tyler Fisk
guest
AI agent builder and instructor (Maven/AI Build Lab), focused on turning agent workflows into production products.

EPISODE SUMMARY

In this episode of Aakash Gupta, featuring Aakash Gupta and Tyler Fisk, Masterclass: How to Turn an AI Agent into a Real Product (No Code) explores build production multi-agent customer support products without code live demo Tyler builds a two-agent team—an expert “Core” agent plus an email-writing “Echo” agent—to mirror real company roles and improve reliability, tone, and task focus.

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