Quick Change vs Full SDD: Choosing the Right ZenFlow Workflow

You’ve got a coding task. Maybe it’s a quick button addition, maybe it’s a full payment integration. The way you approach these two tasks should be radically different—but most developers treat every AI request the same way.

This guide shows you how to pick the right workflow in ZenFlow so you stop wasting time on over-engineered simple tasks and stop debugging under-specified complex ones.

What is ZenFlow?

ZenFlow is Zencoder’s desktop application for orchestrating AI coding agents. Instead of the typical chat-based approach where you ping-pong with an AI (“add this”, “no wait, I meant that”, “also handle this edge case…”), ZenFlow runs structured workflows.
Here’s what that means in practice:

• You create a task with a description of what you want built

• You pick a workflow that defines how the agent should approach the work

• The agent follows that process, producing documentation, code, and verification

• Everything runs in isolation—each task gets its own Git branch and workspace, so parallel work never collides
  Think of it as the difference between texting instructions to a friend versus handing them a project brief. The brief takes longer to write, but the results are more predictable.

The Four Workflows

When you create a task in ZenFlow, you choose from four workflows. Each is designed for a different level of complexity:

1. Quick Change
The “just do it” option. You describe what you want, and the agent starts coding immediately. No planning documents, no approval gates, no back-and-forth. Best for small, obvious changes where you trust the agent to fill in details correctly.

2. Fix Bug
A structured debugging workflow. The agent investigates the issue, documents the root cause, designs a targeted fix, then implements with verification. Creates an audit trail of what broke and why.

3. Spec and Build
The middle ground. Before coding, the agent writes a technical specification describing the approach. You review and approve (or request changes), then implementation proceeds step-by-step. Great for medium features where you want to validate the plan before committing.

4. Full SDD Workflow
The most comprehensive option. SDD stands for Spec-Driven Development. The agent produces a full Product Requirements Document (PRD), technical specification, and implementation plan—with AI code review at every stage. Built for large, complex, or compliance-sensitive work.

This guide focuses on the two extremes: Quick Change for speed and Full SDD for thoroughness. Understanding when to use each will save you hours.

Quick Change: Ship in Minutes

Quick Change is for tasks where the requirements are obvious and you trust the agent to execute without oversight.

Think of it like this: You’re asking a skilled colleague to make a quick fix while you grab coffee. You trust them to figure out the details. You don’t need a meeting about it.

When to Use Quick Change

• Cosmetic fixes: “Change the primary button color from blue to green”

• Simple additions: “Add a ‘Copy to clipboard’ button next to the API key”

• Config updates: “Increase the session timeout from 30 to 60 minutes”

• Small refactors: “Extract the validation logic into a separate function”

• Quick experiments: “Add a console.log to trace the checkout flow”

The common thread: you can fully describe the task in one or two sentences, and there’s no ambiguity about what “done” looks like.

What Happens When You Use Quick Change

1. You write a task description

2. ZenFlow creates a minimal plan with a single step

3. The agent immediately starts implementation

4. Code appears in your task’s isolated workspace

5. You review, merge, and move on
   Total time: Usually 2–10 minutes.

Example
Task description:

Add a "Copy to clipboard" button next to the API key display in the settings page. Use the existing IconButton component and show a toast on success.

What the agent does:

• Finds the settings page component\
• Locates the API key display section\
• Adds an IconButton with clipboard functionality\
• Wires up the success toast\
• Commits the changes

No documentation. No architecture discussions. Just working code.
The Risk

Quick Change trusts you to provide complete requirements. If your description is vague, the agent fills gaps with assumptions—and those assumptions might be wrong.

Warning signs you picked the wrong workflow:

• You’re sending follow-up messages like “No, I meant…” or “Actually, also handle…”\
• The agent asks multiple clarifying questions before starting\
• You realize mid-implementation there are edge cases you forgot to mention\
• The task touches more files than you expected

If any of these happen, you’re not saving time—you’re creating rework. Consider switching to a more structured workflow.

Full SDD Workflow: Build It Right

Full SDD is for tasks where getting it wrong is expensive—complex integrations, breaking changes, compliance requirements, or anything that multiple stakeholders need to review.

Think of it like this: You’re hiring a contractor to renovate your kitchen. You want blueprints, material lists, and a project plan before anyone picks up a hammer. Yes, it takes longer upfront. But you’re not going to discover halfway through that the new island blocks the refrigerator door.

When to Use Full SDD

• Multi-component features: A notification system spanning email, SMS, and in-app delivery

• Breaking changes: API migrations, database schema updates, major refactors

• Cross-team work: Features that require buy-in from product, design, or other engineering teams

• Compliance-sensitive work: Anything with audit, security, or regulatory implications

• Long-running projects: Work spanning multiple days where context loss is a risk

What Are Artifacts?

The Full SDD workflow produces artifacts—Markdown documents that live in your task’s workspace. These serve as living documentation and a contract between you and the AI:

requirements.md** (Product Requirements Document)**

Defines who needs this feature, what problem it solves, what success looks like, and critically—what’s explicitly out of scope. This prevents the agent from gold-plating or missing the point.

spec.md** (Technical Specification)**

The architectural blueprint. Database schemas, API contracts, component structures, integration points, security considerations. You review this before a single line of code is written.

plan.md** (Implementation Plan)**

An ordered list of coding steps, each one a reviewable checkpoint. “Step 1: Create the notification preferences table. Step 2: Build the email provider adapter.” If something goes wrong at step 5, you know exactly where to look.

The Four Stages
Stage 1: Requirements Gathering
The agent starts by asking questions. Who are the users? What’s the core problem? What’s in scope? What’s explicitly out of scope? What are the success metrics?
Your answers become the PRD. This isn’t busywork—ambiguity in requirements is the #1 cause of AI-generated code that “works” but doesn’t fit.

Stage 2: Technical Specification
With requirements locked, the agent designs the solution. Database schemas. API endpoints. Component architecture. Integration with existing systems.
You review the spec and approve it—or request changes. This is your last chance to catch architectural mistakes before code exists.

Stage 3: Implementation Planning
The spec breaks into ordered steps. Each step has clear deliverables and can be reviewed independently. This is where the agent maps dependencies: “We need the database migration before the API endpoints, and the API endpoints before the frontend integration.”

Stage 4: Implementation with AI Review
Now the agent codes—but after each step, an AI verifier reviews the diff. Issues surface during development, not after you’ve already merged three dependent features.
Example

Task description:

Build a real-time notification system supporting email, SMS, and in-app 

channels with user preferences, retry logic, and delivery analytics.`

What the agent produces before writing code:

1. PRD identifying stakeholders (customers, support team, operations), success metrics (95% of notifications delivered within 60 seconds, 40% reduction in support tickets), and constraints (no push notifications in v1, must use existing SendGrid account)\
2. Technical spec with database schema for preferences and notification logs, queue architecture using Redis, provider integrations for SendGrid and Twilio, and API contracts for the preference management endpoints\
3. Implementation plan with 8 ordered steps, from database migrations through frontend integration, each independently testable

Then implementation begins—with you able to pause, review, or redirect at any checkpoint.

The Tradeoff
Full SDD takes longer. A task that might take 10 minutes with Quick Change could take 30–60 minutes with Full SDD, including time for you to review specifications.
But for complex work, this investment prevents the 3-hour debugging sessions that happen when you discover halfway through implementation that the AI made a fundamental architectural mistake.

How to Choose: The Decision Framework

Here’s a quick comparison to guide your choice:

Factor	Quick Change	Full SDD
Time to complete	Minutes	Hours to days
Lines of code	Under 50	100+
Files touched	1–3	5+
Dependencies	None or obvious	Multiple services
Review needed	Just you	Stakeholder approval
Documentation	None	PRD + spec required
Rollback risk	Low	High

The 10-Minute Rule

Here’s a practical heuristic: If you can fully explain the task in under 2 minutes and expect implementation in under 10 minutes, use Quick Change.

The moment you catch yourself listing caveats (“but make sure it handles the case where…”) or integration concerns (“this needs to work with the existing payment flow…”), you’ve outgrown Quick Change. Move to Spec and Build or Full SDD.

Common Mistakes

Mistake 1: Using Quick Change for Complex Work

What happens: You create a Quick Change task for what seems like a simple feature. Thirty minutes later, you’re still in the chat correcting the agent’s assumptions. “No, the notification should go to the admin, not the user.” “Wait, we also need to handle the case where the email fails.” “Actually, log this to our analytics system too.”

Why it fails: Quick Change assumes your initial description is complete. Every clarification you add mid-task is fighting against the workflow’s design.

The fix: If you find yourself writing more than one or two follow-up messages, stop. Create a new task with Full SDD (or at least Spec and Build). The time you spend writing a proper spec is less than the time you’ll spend debugging misunderstood requirements.

Mistake 2: Using Full SDD for Simple Tasks

What happens: You create a Full SDD task to rename a CSS class. Twenty minutes later, you’re still waiting for a PRD about button styling best practices.

Why it fails: Full SDD adds overhead that only pays off for complex work. For simple changes, that overhead is pure waste.

The fix: If the entire requirement fits in one sentence with no ambiguity, use Quick Change. Save the heavyweight process for heavyweight problems.

Your AI agent is powerful, but it’s only as good as the structure you give it. Pick the right workflow, and the code almost writes itself. Pick the wrong one, and you’ll spend more time correcting the course than building features.