Simple chat-based AI assistance is table stakes. Advanced workflows deliver measurable results: GitHub Spec Kit achieves 95%+ first-attempt accuracy with detailed specs, Rakuten reduced development timelines from 24 to 5 days (79% reduction) using structured agent workflows, and 7 hours of sustained autonomous coding is now achievable on complex projects. These patterns enable longer-horizon, more complex tasks with dramatically better outcomes.
Spec-Driven Development with AI: Get Started with a New Open Source Toolkit
GitHub
GitHub Spec Kit formalizes the spec-driven development approach where detailed specifications precede AI code generation. The four-phase workflow (Specify → Plan → Tasks → Implement) ensures human oversight at each checkpoint. This is the antidote to 'vibe coding' - structured, auditable AI development. Key for assessing advanced workflow maturity.
Claude Code's Slack integration represents the 'ambient AI' pattern: AI agents triggered from natural team conversations, not dedicated coding interfaces. The $1B revenue milestone and enterprise customers (Netflix, Spotify) validate the market. Rakuten's 79% timeline reduction is a standout case study.
Key Findings:
Claude Code in Slack launched December 8, 2025 as research preview
@Claude tag routes coding tasks to Claude Code on web automatically
Analyzes Slack context (bug reports, feature requests) for repository detection
BMAD-METHOD: Breakthrough Method for Agile AI Driven Development
BMad Code
BMAD represents the multi-agent orchestration approach to AI development. Unlike simple chat-based AI assistance, BMAD uses specialized agents (Analyst, Architect, Developer, QA) coordinated by an orchestrator. Key innovation: zero context loss between tasks. Represents advanced maturity in agentic workflows.
Key Findings:
19+ specialized AI agents with distinct roles (Analyst, Architect, Developer, QA)
50+ workflows covering development scenarios
Scale-adaptive intelligence adjusts to task complexity
Beads solves the 'context loss' problem in multi-session AI development. Rather than storing tasks in unstructured markdown, Beads uses Git-backed JSONL files that agents can query for 'ready' work. Key for long-horizon tasks spanning multiple days or sessions. Represents the frontier of AI workflow tooling for persistent memory.
Key Findings:
Git-backed issue tracker designed for AI coding agents
Real-world results validate advanced workflows: Rakuten achieved 79% timeline reduction (24→5 days) with structured agents, GitHub Spec Kit delivers 95%+ first-attempt accuracy, and 7 hours of sustained autonomous coding is now achievable. These represent the post-agentic maturity frontier—moving from reactive chat to proactive, structured development workflows.
Assessment Questions (6)
Maximum possible score: 26 points
○Q1single choice4 pts
Are you familiar with GitHub Spec Kit for spec-driven development?
[0]Never heard of it
[1]Heard of it, haven't tried
[2]Tried it experimentally
[3]Use it for some projects
[4]Standard part of my workflow
Note: Spec Kit represents GitHub's recommended approach for production-quality AI-assisted development
Spec-Driven Development with AI: Get Started with a New Open Source Toolkit
GitHub
GitHub Spec Kit formalizes the spec-driven development approach where detailed specifications precede AI code generation. The four-phase workflow (Specify → Plan → Tasks → Implement) ensures human oversight at each checkpoint. This is the antidote to 'vibe coding' - structured, auditable AI development. Key for assessing advanced workflow maturity.
When using spec-driven development, which phases do you follow?
[1]Specify - Write detailed specifications first
[1]Plan - Create implementation plan from spec
[1]Tasks - Decompose into ordered tasks
[1]Implement - Execute tasks with checkpoints
[0]I don't use spec-driven development
Note: Full four-phase workflow (Specify → Plan → Tasks → Implement) ensures quality and traceability
Spec-Driven Development with AI: Get Started with a New Open Source Toolkit
GitHub
GitHub Spec Kit formalizes the spec-driven development approach where detailed specifications precede AI code generation. The four-phase workflow (Specify → Plan → Tasks → Implement) ensures human oversight at each checkpoint. This is the antidote to 'vibe coding' - structured, auditable AI development. Key for assessing advanced workflow maturity.
[2]Sometimes - different models for different tasks
[3]Yes - I have specialized agents for roles (review, code, test)
[4]Yes - I use orchestration frameworks (BMAD or similar)
Note: BMAD and similar frameworks use specialized agents (Analyst, Architect, Developer, QA) coordinated by an orchestrator
BMAD-METHOD: Breakthrough Method for Agile AI Driven Development
BMad Code
BMAD represents the multi-agent orchestration approach to AI development. Unlike simple chat-based AI assistance, BMAD uses specialized agents (Analyst, Architect, Developer, QA) coordinated by an orchestrator. Key innovation: zero context loss between tasks. Represents advanced maturity in agentic workflows.
Key Findings:
19+ specialized AI agents with distinct roles (Analyst, Architect, Developer, QA)
50+ workflows covering development scenarios
Scale-adaptive intelligence adjusts to task complexity
How do you handle long-horizon tasks that span multiple days or sessions?
[0]I avoid tasks that span multiple sessions
[1]I complete tasks in single sessions when possible
[2]Manual context restoration each session
[3]Project context files updated between sessions
[4]Persistent memory tools (Beads, etc.)
[5]Structured task tracking with dependency management
Note: Beads provides Git-backed, dependency-aware task tracking that surfaces 'ready' work automatically
Introducing Beads: A Coding Agent Memory System
Beads solves the 'context loss' problem in multi-session AI development. Rather than storing tasks in unstructured markdown, Beads uses Git-backed JSONL files that agents can query for 'ready' work. Key for long-horizon tasks spanning multiple days or sessions. Represents the frontier of AI workflow tooling for persistent memory.
Key Findings:
Git-backed issue tracker designed for AI coding agents
Do you use AI-native task tracking tools designed for agent workflows?
[1]No - I use traditional issue trackers (Jira, GitHub Issues)
[1]Aware of AI-native tools (Beads, Linear AI, etc.) but haven't tried
[2]Experimenting with AI-native task tracking
[3]Regularly use AI-native tools for agent-assisted projects
[4]AI-native tracking integrated into team workflow with dependency management
Note: AI-native task trackers (Beads, Linear AI, etc.) are designed for agent workflows with features like dependency-aware task graphs and automatic context surfacing
Introducing Beads: A Coding Agent Memory System
Beads solves the 'context loss' problem in multi-session AI development. Rather than storing tasks in unstructured markdown, Beads uses Git-backed JSONL files that agents can query for 'ready' work. Key for long-horizon tasks spanning multiple days or sessions. Represents the frontier of AI workflow tooling for persistent memory.
Key Findings:
Git-backed issue tracker designed for AI coding agents
[1]Ad-hoc - I just start coding with AI when needed
[2]Informal - I have some patterns but they're flexible
[3]Semi-structured - I follow a general process
[4]Structured - I follow defined workflows with checkpoints
[5]Rigorous - I use formal methodologies (Spec Kit, BMAD) with explicit phases
Note: Rigorous workflows with explicit phases and checkpoints correlate with higher code quality and fewer rework cycles
Spec-Driven Development with AI: Get Started with a New Open Source Toolkit
GitHub
GitHub Spec Kit formalizes the spec-driven development approach where detailed specifications precede AI code generation. The four-phase workflow (Specify → Plan → Tasks → Implement) ensures human oversight at each checkpoint. This is the antidote to 'vibe coding' - structured, auditable AI development. Key for assessing advanced workflow maturity.