Show Notes
Parker breaks down the $25K AI agent play, why he believes high-margin, multi-agent projects beat generic automation, and how it ties into his MAP and Vibe with AI initiatives. Practical, no-fluff takeaways ahead.
The 25K Agent Play
- Package a custom AI agent around a real business problem with a clear, bounded scope.
- Focus on leverage and repeatability: aim for high repeatable value per transaction, not quick spammy outputs.
- Build around a defined tech stack and objective: what tools, what outputs, and what success looks like.
- Deliver a “done-for-you” or “done-with-you” model, with a clear path to repeatable templates to scale.
- Expect 75%+ reusability once a project is finished; reuse is the real multiplier.
Takeaways:
- Start with a concrete problem and a measurable outcome.
- Use a templated, reusable approach to maximize future transactions.
Real-World Build: Real Estate Agent Workflow Example
- Problem space: real estate agents juggle follow-up, nurturing, contracts, and outreach—text, SMS, MMS, email, and phone all at once.
- The agent acts as the middleware, coordinating inbound/outbound channels and data from multiple sources.
- Key learnings: define SOPs early; lock the required tech stack (e.g., a modern JS/TS stack, specific APIs) to avoid custom-shotgun builds.
- Outcome focus: structure the engagement so the agent adds value across the funnel, from first impression to close.
Takeaways:
- Start with data compression and multimodal inputs (text, voice, etc.) to improve throughput.
- Lock in a stack and delivery standards from day one to avoid scope creep.
Build Architecture & Tech Stack
- Vision: a turbo monorepo with clear module separation: frontend UI, and a backend where the agents live as modular scripts/functions.
- Use the Agents SDK to manage swarms (dozens of agents) more efficiently.
- Core patterns:
- Modular folders for each integration (calendar, CRM, messaging, etc.).
- A single top-level service for tokens/access control (singleton pattern).
- Clear, small, well-documented tool interfaces (e.g., calendar APIs, email, chat).
- Frontend and UI goals: turn the backend logic into a usable dashboard that’s easy to iterate on.
- Practical note: be prepared to gut and refactor when major versions land; validate with small, fast iterations.
Takeaways:
- Structure code as a turbo mono repo with clean boundaries and a single source of truth for tokens.
- Rely on the Agents SDK for scalable multi-agent orchestration.
MAP & Vibe with AI: Product Vision
- MAP: AI health tech platform that helps you live smarter and plan for your “future self” using data from wearables and health apps (Whoop, Aura, Apple Health).
- Architecture: health dashboard + calendar + goal planning + coaching feedback; iOS companion app with Swift-based components.
- Rationale: health data is objective and amenable to automation; it supports long-term habit formation and performance tracking.
- Status: initial prototype built and iterating; the bigger plan is to integrate AI agents to automate health coaching and habit formation at scale.
Takeaways:
- Ground product decisions in objective data and clear goals.
- Plan for an automation-augmented coaching experience, not just dashboards.
Content Generation Engine & Automation
- Long-form content is the starting point to fuel short-form clips and cross-platform posts.
- Core idea: a pipeline that turns long-form output into snippets, clips, and text content across platforms, plus SEO-friendly blog content.
- Within Vibe with AI, the plan is to automate the end-to-end content loop: drafting, clipping, posting, and repurposing at scale.
- Practical benefit: reduce manual content churn while increasing distribution reach and quality.
Takeaways:
- Build a robust automation pipeline that converts long-form content into multi-platform assets.
- Use data to inform what clips and formats perform best; iterate quickly.
Community, Automation, and Next Steps
- Automations inside the community: brainstorms, triggers, and nurture flows to keep members engaged.
- Possible workflow: admin accounts or browser agents to monitor activity and trigger actions (e.g., posting updates, notifying members).
- The plan includes vibewithai.com to host and scale the content distribution and SEO pipelines, leveraging proven blog templates and parasite tactics.
- Ongoing work: refining the product roadmap for MAP and Vibe with AI, plus sharing code snippets on request (comment “map” to get deeper dives).
Takeaways:
- Build community automations that keep members consistently engaged.
- Stand up a site to centralize distribution and SEO workflows; reuse proven templates.
Q&A Highlights (Key Points)
- You don’t need a “YouTube strategist” upfront; automate your own discovery and learning from a curated set of sources (Twitter, newsletters, etc.).
- The value is in systematizing: create SOPs, define the stack, and map the customer journey from proposal to delivery.
Takeaways:
- Create a repeatable process for learning and content strategy; automate as much as possible.
actionables you can apply now
- Define a 25K agent project in one page: problem, stack, deliverables, success metrics, and a reusable template.
- Pick one real-world problem in your domain and draft an SOP-focused plan (including tech stack and delivery dates).
- Start a small mono-repo experiment: separate frontend and agent logic, ensure a single token service, and pilot a 2-3 agent swarm.
- List the data sources you’ll need for an automation project (e.g., SMS, email, calendar, CRM) and map how they’ll flow through your agents.
Links
- Vercel templates (for learning AI agent patterns)
- Next.js, Turborepo (architecture references)
- SEMrush blog templates (SEO content guidance)
- Whoop, Oura, Apple Health (data sources)
- Garry Tan (reference for product-market fit and iteration)
If you want code snippets or deeper dives into any part of the project (map, MAP, or Vibe with AI), comment “map” and I’ll share the relevant sections.