Workflow Management for AI Coding Agents

If you're researching Workflow Management for AI Coding Agents, AI DevKit gives your AI coding agents one control plane for setup, console supervision, local-first memory, communication, workflow skills, and verification. AI DevKit includes a repeatable engineering workflow inside the larger control plane. Instead of letting an agent jump straight into code, you create durable context at each phase so it can plan, implement, verify, and review with a clear source of truth.

This is the reliability layer of AI DevKit: humans keep ownership of requirements and quality, while AI coding agents follow the same SDLC checkpoints when the work needs more rigor.

Why a Repeatable Workflow?#

When you work with AI coding agents without structure, you often:

• Lose context between sessions
• Repeat yourself explaining requirements
• End up with inconsistent code and documentation

AI DevKit solves this by creating workflow docs as you go. Each document becomes context for the next phase, so your coding agent knows what you're building, why it matters, and what evidence is required before claiming completion.

The Development Lifecycle#

1. Start with the Dev Lifecycle Skill#

The dev-lifecycle skill is your entry point for any new feature. Ask your agent to use it and describe what you want to build:

Use the dev-lifecycle skill to build <feature>

What happens:

Documentation created:

AI DevKit creates these under your configured docs directory. The default is docs/ai/.

docs/ai/
├── requirements/feature-{name}.md   # What and why
├── design/feature-{name}.md         # Architecture
├── planning/feature-{name}.md       # Task breakdown
├── implementation/feature-{name}.md # Implementation notes
└── testing/feature-{name}.md        # Test strategy

Tip: You don't have to complete all phases in one session. AI DevKit saves your documentation, so you can pick up where you left off.

Using the Dev Lifecycle Skill#

Use the dev-lifecycle skill to guide you through the entire workflow. The skill validates that the phase skills are installed, proposes the next phase plan, and routes execution to the focused skill for that phase.

If you want the full setup, dependencies, and usage guide, see Dev Lifecycle Skill.

Installing the skills#

The normal first-run path installs built-in skills globally when you connect detected agents:

ai-devkit setup

With npx only:

npx ai-devkit@latest setup

Launch your agent once before setup so AI DevKit can detect its home directory. For manual skill installation and recovery options, see Skills. For the complete machine and project sequence, see Getting Started.

How to use it#

Tell your coding agent to use the skill and describe what you want to build. For example:

"Use dev-lifecycle skill to build an authentication feature with Google OAuth and email login"

The orchestrator takes over from there:

1. It walks you through requirements gathering — asking clarifying questions about your feature
2. It proposes a phase plan before making changes
3. Once requirements are clear, it routes to requirements review
4. Then proceeds through design review, planning, implementation, testing, and code review

You do not need separate workflow prompt files — the skill handles phase transitions for you.

Automatic phase progression#

The key difference from running individual commands: the skill suggests and triggers the next phase after each one completes. If a review phase finds issues, it loops back to the right phase automatically.

Resuming work#

If you started a feature in a previous session, the skill can pick up where you left off. It checks your existing documentation to determine which phase you're in and continues from there.

When to use lifecycle vs. focused skills:

• Use the dev-lifecycle skill when starting a new feature or continuing end-to-end work — it handles the flow for you
• Use focused phase skills such as dev-worktree, dev-requirements, dev-design, or dev-review when you only need one lifecycle phase
• Use focused support skills such as tdd, verify, or structured-debug when you only need one specific behavior

Focused Workflow Requests#

If you're using the dev-lifecycle skill, these phases are covered as part of the workflow. You can also ask for a specific phase directly:

Requirements Review#

Review the requirements for feature-authentication

When to use: Before starting design, to catch gaps in your requirements.

What it does:

• Validates completeness of requirements
• Identifies missing acceptance criteria
• Suggests clarifying questions

Design Review#

Review the design for feature-authentication

When to use: After design, before implementation, to ensure your architecture is solid.

What it does:

• Ensures architecture clarity
• Generates Mermaid diagrams for visualization
• Checks alignment with requirements

Execute Plan#

Execute the implementation plan for feature-authentication

When to use: During implementation, to work through tasks systematically.

What it does:

• Reads your planning document
• Presents tasks in logical order
• Tracks progress and captures notes
• Prompts documentation updates as you go

Update Planning#

Update the planning docs with current implementation progress

When to use: When implementation drifts from the original plan.

What it does:

• Syncs planning documentation with actual progress
• Updates task status and notes
• Keeps documentation current

Quality Requests#

Code Review#

Review the current diff before I commit

When to use: Before committing, to catch issues early.

What it checks:

• Alignment with design documents
• Logic, security, and performance issues
• Code redundancy and duplication
• Missing tests and documentation

Testing#

Use TDD to add tests for this change

When to use: After implementation, to ensure coverage.

What it does:

• Generates unit and integration tests
• Targets high coverage of your new code
• Follows your project's testing conventions

Tips for Success#

1. Don't skip phases — Each phase builds context for the next. The dev-lifecycle skill handles this automatically
2. Keep docs updated — Ask the agent to update planning when things change
3. Review before committing — Ask for code review before you push
4. Be specific — The more detail you give, the better your coding agent can help

Next Steps#

• Debug effectively — See Debugging with AI DevKit
• Understand existing code — See Understanding Existing Code
• Give your AI memory — See Memory

Workflow Management for AI Coding Agents with AI DevKit#

Use AI DevKit to keep Workflow Management for AI Coding Agents consistent across features and teams: one config, one local console, shared memory, and the same verification workflow across supported agents.