From Plan to Ship
Your AI agent workflow — from requirements to production code, with full context at every step.
The Workflow Loop
Agiflow provides MCP skills (prompts) for each phase of your development workflow. Your AI agent uses these to manage projects, refine tasks, and execute work systematically.
Planning
Skill: project_plan
Break high-level requirements into concrete tasks.
Key Actions
- - Create 5-15 focused tasks (15 min – 2 hr each)
- - Define 2-5 acceptance criteria per task
- - Group 3-8 related tasks into Work Units
- - Assign priorities and dependencies
Example Prompt
“Plan the shopping cart feature for our e-commerce app”
describe_capabilities to discover project tools, then creates tasks with acceptance criteria via create_task.Task Refinement
Skill: refine_task
Add detail and context to tasks before execution.
Key Actions
- - Add detailed acceptance criteria (2-5 per task)
- - Document technical context and constraints
- - Link related tasks and set dependencies
- - Assign to team members or agent profiles
Backlog Grooming
Skill: backlog_grooming
Prioritize and organize your backlog.
Key Actions
- - Review and reorder tasks by priority
- - Move tasks between work units
- - Identify blockers and dependencies
- - Archive or close stale tasks
Execution
Skills: run_work (work unit) and run_task (single task)
Agent executes tasks with full context.
Load Task Context
Agent loads the task via MCP — reads acceptance criteria, devInfo, and related context.
Understand Conventions
Before coding, calls get-file-design-pattern (Architect MCP) to understand project conventions.
Implement
Implements the task following acceptance criteria and discovered patterns.
Validate
After coding, calls review-code-change (Architect MCP) to validate against project rules.
Update & Commit
Updates task status and devInfo, then commits changes.
Triage & Review
Skills: triage, review_work, daily_standup
Review completed work, provide feedback, and iterate.
Key Actions
- - Review agent output and artifacts
- - Add comments for feedback (agent reads these on next session)
- - Move tasks through status columns: Todo → In Progress → Review → Done
- - Track progress via devInfo metadata
Agent reads previous comments and devInfo when resuming — no context loss.
Session Continuity (devInfo)
Work Units maintain structured devInfo metadata that agents access via MCP. This means agents resume with full context — files changed, test results, progress.
{
"devInfo": {
"executionPlan": "Backend → Frontend → Tests",
"progress": { "completedTasks": 2, "totalTasks": 6 },
"filesChanged": [
"backend/migrations/003_cart.sql",
"backend/src/repos/CartRepo.ts"
],
"testResults": { "passed": 12, "coverage": "85%" }
}
}Related Documentation
Ready to Organize Your Development Workflow?
Plan, execute, and review — all through your AI agent.