Aakash GuptaMasterclass: How to Turn an AI Agent into a Real Product (No Code)
CHAPTERS
Why this episode: from “vibe coding” to production AI agents
Aakash sets the stage for a live build with Tyler Fisk, positioning him as a seasoned practitioner who takes agent building from prototypes to production workflows. Tyler previews the plan: go from idea to a working multi-agent system in real time, without requiring a CS background.
Tooling overview: TypingMind playground + the “Gigawatt” agent that builds agents
Tyler introduces TypingMind as a flexible LLM playground and brings in Gigawatt—his prompt/AI engineering agent—connected to models and tools (search, sequential thinking). He explains the “AI practitioner / deployed engineer” mentality: research first, requirements next, then build.
Defining the Apple customer support use case and agent roles
They scope the workflow: handle inbound Apple customer-service emails using two agents—an internal expert and an outward-facing email responder. Tyler has Gigawatt ask clarifying questions to align on scope (consumer products, all support categories) and information sources (RAG + web with verification).
How to replicate Gigawatt: meta-prompting, research frameworks, and chain-of-verification
Aakash probes how others can recreate Gigawatt if they don’t have access. Tyler explains it’s an accumulation of best practices and research techniques, including Meta’s chain-of-verification approach to reduce hallucinations and improve factuality.
Building the knowledge base fast: Cassidy scraping + parallel Deep Research agents
Tyler switches to Cassidy (no-code) to scrape apple.com into a RAG knowledge base while also launching Deep Research in parallel. He introduces a second helper agent (“Clear”) specialized in writing high-quality Deep Research prompts for tools like Perplexity and Claude.
Creating a PRD for the expert agent (“Core”) and why multi-agent beats single-agent
Gigawatt produces a PRD for the Apple expert agent, “Core,” to lock alignment on inputs/outputs and avoid role confusion. Tyler explains why separating expert reasoning from customer communication mirrors real organizations and improves controllability, tone, and reliability.
Temperature explained with the “icy peak” claw-machine analogy
Tyler breaks down temperature as a control over randomness/creativity by altering the probability distribution of token selection. He connects it to agent design: experts often benefit from low temperature, while customer-facing writing may benefit from higher temperature.
System instructions V1 for Core: XML structure, meta-examples, and self-review (meta-prompting loop)
Tyler has Gigawatt draft Core’s system instructions in XML with defined sections (role, context, instructions, criteria, examples). He avoids early “shot prompts” and instead uses scenario-based meta-examples, then runs a structured self-evaluation to iterate from a ‘B’ to an ‘A’ version.
Deploying Core in Cassidy: model choice tradeoffs, RAG + tools, and first test query
Tyler moves Core into Cassidy, enabling RAG, web search, and data analysis, and discusses model selection (speed, reasoning, latency, cost, context window, redundancy). They test Core with a device-purchase question; Core returns a structured JSON answer with citations, then converts it to readable markdown for review.
Building the email agent (“Echo”): Apple tone/brand alignment and cross-model quirks (XML tags)
Using example system instructions from a different email agent (Hattie B’s), Tyler has Gigawatt adapt the voice to Apple and generate a new email-focused agent. He deploys the agent on Gemini, notes how newer models emulate XML tag patterns (think/scratchpad/answer), and runs observational evals by having Echo turn Core’s research into a customer-ready email.
From agents to production workflow: human-in-the-loop, Slack approvals, and branching logic
Tyler shows what productionization looks like in Cassidy: triggers from email, sentiment analysis, expert research, drafting, QA loops (“toast method”), and a Slack-based approval step. A generative filter routes decisions (ship/revise/confirm), enabling safe autonomy while maintaining oversight and audit trails.
Scaling considerations: RAG for enterprise docs, GraphRAG/agentic RAG, evals, and cost framing
They discuss ingesting large enterprise document sets (OCR, chunking, embeddings) and the limitations of naive RAG as corpora grow or become outdated. Tyler emphasizes agentic RAG/GraphRAG for relational, updatable knowledge, and frames costs in terms of labor saved and improving model economics over time; then they close with Tyler’s course-business results and product ambitions for “Hey Gigawatt.”
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