№ 00 AI WORKFLOW READINESS SCORECARD

How production-ready are your team's AI coding workflows?

Most teams are using AI coding tools. Few have the workflows to make them reliable. Assess your team in about 5 minutes.

Based on 29 AI development patterns. Built by Paul Duvall, author of the Jolt Award-winning book Continuous Integration (Addison-Wesley).

Drawn from the open-source ai-development-patterns framework (400+ stars on GitHub).

15

Questions

Across 3 pillars

5 min

To Complete

Quick assessment

30-day

Action Plan

Personalized to your score

What You'll Assess

1

Context Persistence

How well your team maintains context for AI tools across sessions, contributors, and projects.

2

Multi-Agent Orchestration

How effectively you decompose work and coordinate multiple AI agents for complex tasks.

3

CI/CD Integration

How your pipeline handles AI-generated code with quality gates, testing, and deployment guardrails.

What You'll Get in Your Inbox

Your score, per-pillar analysis with specific patterns to apply, and a 30-day action plan — emailed to you as soon as you finish.

Scoring is weighted 0–100 and fully deterministic — same inputs, same score, letter grades A–F. No AI in the scoring logic.

C

48/100

Assisted

Context Persistence

C50/100

Multi-Agent Orchestration

C46/100

CI/CD Integration

C49/100

AI helps, but you're still doing the heavy lifting. Standardizing your approach will turn individual wins into team-wide leverage.

Detailed Analysis & Action Items

C Context Persistence Building Foundation — 50/100

Working: rules files (“Standardized across repos”); rule adherence (“Hooks or CI checks catch specific rule violations”). Gaps: context loading (“Manual copy-paste of relevant files”); standards across tools (“Each tool configured separately”).

Review rules changes like code (see Codified Rules). Done when: the last rules change went through a pull request citing the AI output it corrects.
Measure adherence and report drift. Run a recurring probe that tests agents against your rules files. Done when: a dated adherence report exists for each of the last two months.

C Multi-Agent Orchestration Building Foundation — 46/100

Free. No credit card. Results delivered to your inbox.

What Best Describes Your Role?

This helps us tailor your 30-day action plan to the decisions you actually make.

Select the role closest to yours:

Individual Contributor — I write code and use AI tools directly Team Lead / Engineering Manager — I guide a team's AI adoption Executive / CTO / VP — I set AI strategy for the organization

Pillar 1: Context Persistence

How well does your team maintain context for AI coding tools across sessions, contributors, and projects?

0 of 5 answered

1. Does your team use persistent context files (e.g., CLAUDE.md, rules files) to encode coding standards for AI tools?

0No context files 1Ad-hoc or minimal 2Standardized across repos 3Comprehensive and version-controlled ?Not sure

2. How does your team handle progressive disclosure for large codebases when working with AI tools?

0No strategy 1Manual copy-paste of relevant files 2Structured agent docs with selective loading 3Token-budgeted context hierarchy with relevance scoring ?Not sure

3. How do you know your AI agents actually follow the rules files you wrote?

0We don't check 1We notice when a reviewer catches a violation 2Hooks or CI checks catch specific rule violations 3Rule adherence is measured on a schedule and drift is reported ?Not sure

4. How are your AI coding standards shared across the AI tools you use (e.g., Claude, Cursor, Codex)?

0No written standards for AI tools 1Each tool configured separately 2One shared standards file that every tool reads or adapts 3Single-source rules synced to every tool automatically ?Not sure

5. Does your team encode domain expertise as reusable AI commands or prompt templates?

0No reusable commands 1Individuals save personal prompt snippets 2Team-shared command templates for common tasks 3Versioned command library with domain-specific workflows ?Not sure

Pillar 2: Multi-Agent Orchestration

How effectively does your team decompose work and coordinate multiple AI agents for complex tasks?

0 of 5 answered

6. Does your team use spec-driven development to guide AI code generation?

0No specs for AI 1Informal requirements in prompts 2Structured specs for major features 3Machine-readable specs (text, visual, or both) drive all AI generation ?Not sure

7. How does your team handle task decomposition for AI-assisted work?

0Single-prompt approach 1Manual breakdown, sequential execution 2Structured task decomposition with dependency tracking 3Automated decomposition with parallel agent execution ?Not sure

8. Can your team run multiple AI agents in parallel on independent tasks?

0One agent at a time 1Occasionally run 2 agents manually 2Regularly parallelize with worktrees or agent teams 3Systematic parallel orchestration with merge coordination ?Not sure

9. Does your team use event automation (e.g., hooks, triggers) to enforce quality when AI generates code?

0No automation around AI actions 1Manual checks after AI generates code 2Pre-commit hooks for linting and secret scanning 3Lifecycle hooks trigger validation, tests, and security checks on every AI action ?Not sure

10. Does your team have patterns for handling AI agent failures or stuck states?

0Start over from scratch 1Ad-hoc retry or manual intervention 2Documented recovery patterns with a clear retry limit 3Automated failure detection with approach pivot and structured handoff ?Not sure

Pillar 3: CI/CD Integration

How well does your pipeline handle AI-generated code with appropriate quality gates, testing, and deployment guardrails?

0 of 5 answered

11. Does your CI pipeline run automated tests on AI-generated code before merge?

0No CI pipeline 1Basic tests, often skipped for AI PRs 2Same pipeline as human code 3Enhanced pipeline with AI-specific quality gates ?Not sure

12. How does your team test AI-generated code?

0Manual testing only 1AI generates code, humans write tests 2TDD with AI: tests first, AI implements 3Comprehensive TDD with property-based and integration testing ?Not sure

13. How does your team manage security for AI-generated code and AI tool access?

0No security practices for AI 1Manual security review of AI output 2Automated scanning (SAST, dependency audit) in pipeline 3Agents sandboxed from production secrets, plus required automated scanning in CI ?Not sure

14. How does your team handle code review for AI-generated PRs?

0No review process 1Same review as human code, often rubber-stamped 2Structured review with AI-specific checklist 3Multi-layer review: automated checks, human review, traceability ?Not sure

15. Can your team measure the impact of AI coding tools on delivery speed and code quality?

0No metrics 1Anecdotal feedback 2Basic metrics (PRs merged, time to merge) 3DORA metrics plus AI-specific quality signals, reviewed on a schedule ?Not sure

Your score is ready. One step to unlock it.

You've assessed all 3 pillars. We'll email you a personalized scorecard with your score, per-pillar breakdown, and a 30-day action plan.

Your overall readiness score and letter grade
Per-pillar breakdown with specific patterns to apply
A sequenced 30-day priority action plan

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This score is what your team believes. The probe measures what is true.

The harness probe runs in your own CI, on every repo you point it at, and checks whether agents actually follow the rules, hooks, and gates your team wrote. Each month: a drift report with six measures against your week-0 baseline. The advisory retainer is $25,000/month per harness, unlimited repos, month to month.

See the advisory retainer →

Or book a conversation to discuss your results →