AI Workflow Readiness Scorecard

Find out how production-ready your team's AI coding workflows really are.

15

Questions

Across 3 pillars

3 min

To Complete

Quick self-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.

Free. No credit card. Results are immediate.

Start

Pillar 1

Pillar 2

Pillar 3

Results

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

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 3Automated context hierarchy with progressive detail

3. Do new team members get productive with AI coding tools within their first week?

0No onboarding for AI tools 1Informal guidance from peers 2Documented setup but no workflow training 3Structured onboarding with context files and examples

4. How consistently do AI tools produce code that matches your team's conventions?

0Rarely matches conventions 1Sometimes, with heavy editing 2Usually, with minor adjustments 3Consistently matches conventions

5. Does your team share and reuse AI context across projects and repositories?

0No sharing 1Occasionally share prompts informally 2Shared templates across some projects 3Centralized context library with cross-repo reuse

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 drive all AI generation

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

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 branches 3Systematic parallel orchestration with merge coordination

9. How does your team maintain consistency across multi-file AI-generated changes?

0Frequent inconsistencies 1Manual review catches most issues 2Linting and type checking catch drift 3Shared context ensures consistency, automated checks verify

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 3Automated failure detection with handoff protocols

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

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

13. Does your team run security and dependency analysis on AI-generated code?

0No security scanning 1Manual security review 2Automated SAST/DAST in pipeline 3Layered security: SAST, dependency audit, secrets scanning, SBOM

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

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) 3Comprehensive dashboard: velocity, quality, coverage trends

Get Your Scorecard Results

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Your AI Workflow Readiness Score

Pillar Breakdown

Context Persistence

Multi-Agent Orchestration

CI/CD Integration

Detailed Analysis & Action Items

Your 30-Day Priority Action Plan

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