Aakash GuptaMasterclass: How to Turn an AI Agent into a Real Product (No Code)
Aakash Gupta and Tyler Fisk on build production multi-agent customer support products without code live demo.
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.
At a glance
WHAT IT’S REALLY ABOUT
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.
- The workflow emphasizes rigorous upfront discovery and documentation (PRD), then iterative system-instruction drafting using self-critique (“meta-prompting”) to raise quality before deployment.
- They set up a RAG knowledge base by scraping Apple’s site and importing deep-research reports, while enforcing an information hierarchy (RAG first, then system knowledge, then verified web).
- The episode explains practical agent-engineering concepts—temperature tradeoffs, tool/MCP connectors, and structured inter-agent handoffs (often JSON)—to reduce hallucinations and improve orchestration.
- For production, Tyler shows a no-code automation pattern with sentiment analysis, QA loops, Slack-based human approval, and logging/evals, plus guidance on model selection and cost justification via labor replacement value.
IDEAS WORTH REMEMBERING
7 ideasSplit agents by role to match real org structure and optimize outputs.
Tyler argues experts shouldn’t write customer emails directly; separating a deterministic expert agent from a higher-temperature brand-voice agent improves factuality, empathy, and instruction-following.
Use an explicit information hierarchy to curb hallucinations.
Core is instructed to consult the RAG first, then system knowledge, then carefully verified web search with “chain of verification” and confidence checks—reducing ungrounded claims.
Iterate system prompts with structured self-critique to reach “production-grade.”
Gigawatt drafts system instructions, grades them section-by-section (role/context/instructions/criteria/examples), and then rewrites additively; this replaces “ship the first prompt” behavior with repeatable improvement.
Prefer structured inter-agent handoffs (e.g., JSON) even if humans don’t like it.
Tyler notes JSON is easier for downstream agents to parse reliably; humans can request a markdown view for review while keeping machine-to-machine formatting stable.
Parallelize research and build steps with multiple specialized helper agents.
He runs web scraping, deep research (Perplexity/Claude), PRD drafting, and prompt iteration concurrently, dramatically reducing time-to-first-working-system.
Temperature is a control knob for creativity vs. determinism—use it differently per agent.
The email agent may benefit from higher temperature for natural tone, while the expert agent should be lower temperature for consistency and fewer hallucinations; bundling both into one agent forces compromises.
Productionization requires human-in-the-loop and evals, not just a clever demo.
Tyler warns against auto-sending without approval; he demonstrates a Slack-based approval loop, sentiment analysis, QA steps, logging, and “goldens” to build observability and safe autonomy over time.
WORDS WORTH SAVING
5 quotesI joke when I tell people what I do for a living now is I talk funny to robots.
— Tyler Fisk
We’re spinning up multiple agents here just to kind of get this process done and get all the context that we need as quickly as possible.
— Tyler Fisk
The real-life experts typically are not the same people that you want answering the customer service emails.
— Tyler Fisk
Temperature is like this icy peak inside of a claw machine… you’re changing the shape of the probability distribution curve.
— Tyler Fisk
We would never put it into production without some sort of a human-in-the-loop checkpoint. That’s very irresponsible.
— Tyler Fisk
QUESTIONS ANSWERED IN THIS EPISODE
5 questionsFor the Core agent’s “chain of verification,” what exact steps and confidence thresholds would you implement before allowing web-sourced claims into the answer?
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.
When you say RAG quality can degrade as you add more documents, what concrete chunking, metadata, and retrieval strategies (or “Cairns method” details) have worked best in practice?
The workflow emphasizes rigorous upfront discovery and documentation (PRD), then iterative system-instruction drafting using self-critique (“meta-prompting”) to raise quality before deployment.
How do you decide when system instructions are ‘long enough’ versus when you risk instruction overload/context rot, and what signals show it’s time to refactor?
They set up a RAG knowledge base by scraping Apple’s site and importing deep-research reports, while enforcing an information hierarchy (RAG first, then system knowledge, then verified web).
In your Slack human-in-the-loop workflow, what are the top escalation triggers you’d hard-code (legal, safety, PR), and how would you test them?
The episode explains practical agent-engineering concepts—temperature tradeoffs, tool/MCP connectors, and structured inter-agent handoffs (often JSON)—to reduce hallucinations and improve orchestration.
You mentioned ‘toast method’ QA loops (draft → grade → rewrite). What scoring rubric do you use, and how do you prevent the model from inflating its own scores?
For production, Tyler shows a no-code automation pattern with sentiment analysis, QA loops, Slack-based human approval, and logging/evals, plus guidance on model selection and cost justification via labor replacement value.
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