The AI Development Delivery Playbook (Agentic Lane Delivery)

I run focused AI development delivery lanes. They’re not endless projects. They’re not vague “innovation workshops.” They’re scoped, technical, and built to ship cleanly.

This playbook is the system I use to deliver AI products with public setup medians of A$1,000 / A$3,000 / A$5,000 / A$6,000, plus required monthly retainers of A$500–A$1,500 (minimum 3 months). For the terminology shift and positioning update, see Renaming Sprints to Agentic Development.

Who This Is For

• Agencies transitioning into AI delivery
• Founders building MVPs fast
• Teams who need clarity, not chaos

If you want vague scope, this isn’t for you. If you want a clean build, clear boundaries, and accountable delivery, it is.

The Delivery Cycle Philosophy

I treat delivery cycles like product experiments. The goal is speed + clarity, not perfection.

• Ship fast
• Learn fast
• Improve later

That’s the entire mindset.

Phase 1: Pre‑Delivery Cycle (2–3 Days)

The delivery cycle starts before the delivery cycle.

1. Discovery Call (60–90 minutes)

I ask three questions:

• What is the problem?
• Who is the user?
• What does success look like in 14–21 days?

If they can’t answer those, the delivery cycle isn’t ready.

2. Define the “Delivery Cycle Outcome”

I write one sentence:

“At the end of this delivery cycle, the user can ______.”

Everything else is secondary.

3. Scope Lock

I keep the scope ruthless:

• Core workflow only
• No edge-case fantasies
• No unvalidated features

4. Pricing by Lane (Setup + Retainer)

Here’s the public median model I use:

Setup is priced from complexity and risk, not hours. Every project carries a 3-month retainer minimum so post-launch quality stays stable.

Phase 2: Delivery Cycle Build (Days 1–14/21)

This is where the work happens. I run a daily cadence.

Day 1–2: Architecture + Setup

• Repo setup on GitHub
• Stack decisions (typically Next.js + Convex)
• Data models
• Environment config on Vercel

Day 3–7: Core Workflow Build

• The one thing the user must be able to do
• UI + backend integrated
• No polish until it works end‑to‑end

Day 8–12: UX + Reliability

• Error handling
• Edge cases
• Basic analytics
• Visual polish

Day 13–14/21: Launch Readiness

• Deploy
• Docs / onboarding
• Handoff summary

The Agent Advantage (How I Ship Faster)

I use AI agents to compress the delivery cycle timeline.

• Build agents (powered by Claude from Anthropic and OpenAI's Codex) handle feature implementation
• QA agents scan for bugs
• Writer agents draft docs and onboarding

This means I spend my time directing and reviewing, not grinding. The real-world results of this approach are in I Built 3 AI Apps in 5 Days.

Client Management (How to Avoid Chaos)

1. One Point of Contact

I keep communication tight. One channel. One decision maker.

2. Daily Updates (Asynchronous)

I send a short end‑of‑day update:

• What shipped today
• What’s next
• Any blockers

3. Mid‑Delivery Cycle Review

At the halfway mark, I show real progress and confirm scope. This prevents last‑minute surprises.

4. No “Maybe” Features

If a feature isn’t in scope, it goes to phase two. Scope creep is the delivery cycle killer.

Deliverables (What Clients Actually Get)

By the end of the delivery cycle, clients receive:

• Working product flow
• Live deployment
• Documentation
• Handoff video or summary

Scaling and maintenance continue through the retainer lane (minimum 3 months) instead of an ad-hoc afterthought.

Why the Delivery Cycle Model Works

• Fast feedback loops
• Clear boundaries
• Predictable pricing
• High‑trust delivery

Clients don’t want endless projects. They want outcomes.

Common Mistakes (And How I Avoid Them)

Mistake 1: Vague scope

Fix: write one sentence outcome.

Mistake 2: Over‑promising

Fix: ship one workflow, not five.

Mistake 3: No review checkpoints

Fix: schedule mid‑delivery cycle demo.

Mistake 4: Under‑pricing

Fix: price based on impact, not hours. See the real cost of building AI products for a full breakdown.

Sample Lean Lane Timeline (2 Weeks)

• Day 1: Discovery recap + repo setup
• Day 2: Architecture + data model
• Day 3–5: Core workflow build
• Day 6–7: UX + error handling
• Day 8: Demo + scope confirmation
• Day 9–11: Refinement + polish
• Day 12–13: Docs + deployment
• Day 14: Final handoff

Final Reflection

The delivery cycle model is simple: short timelines, clear outcomes, and disciplined delivery.

In a world where AI is accelerating everything, clients want someone who can turn ambiguity into a shipped product fast.

This playbook is how I do it. The Agentic Development page goes deeper into lane design and scope framing. You can adapt it, adjust it, and run it in your own way. Just keep the core rule: scope tight, ship fast, and learn relentlessly. For the full stack I recommend, see Next.js + Convex: The AI App Stack for 2026, and for cost planning, check out AI Agent Cost Breakdown: Real Numbers.