Map Your Team’s AI-Native Lifecycle
As an individual contributor, you want to map your team’s position in the AI-native product development lifecycle so that you know which build process practices to adopt next and can avoid skipping critical stages.What the lifecycle assessment measures
Unlike the URL score (which evaluates the product), the lifecycle assessment evaluates your team’s build process. It tracks progress through 6 stages and 36 tasks that represent the modern AI-native development lifecycle.The 6 lifecycle stages
Specify & Constrain
Define the problem with AI-aware constraints. What can AI actually solve? What are the guardrails? What does “good enough” look like when outputs are probabilistic?
Build System of Context
Assemble the knowledge layer AI needs to perform: data pipelines, embeddings, retrieval systems, prompt engineering, and context window management.
Orchestrate & Generate
Wire up models, agents, and pipelines. Model selection, chaining, tool use, and orchestration architecture.
Validate, Eval & Craft
Test quality at AI speed. Traditional QA does not work for probabilistic outputs. This stage requires eval frameworks, human review loops, and quality benchmarks.
Ship & Manage Economics
Deploy and manage inference costs. AI products have variable cost structures (tokens, GPU time) that need active management.
Complete your assessment
Work through each stage
For each of the 36 lifecycle tasks, mark your team’s status: not started, in progress, or completed.
Review your operations stack
The assessment also evaluates your tooling across 8 categories: Context & Knowledge, Model & Inference, Orchestration, Eval & Quality, Deployment, Economics, Observability, and Feedback.
Three cross-cutting concerns
These forces span all 6 stages and affect every decision your team makes:Token Economics
The cost of every AI interaction. Unlike traditional software (where marginal cost approaches zero), AI products have real per-request costs. Managing token economics is a continuous discipline, not a one-time optimization.Role Fluidity
AI-native teams blur traditional role boundaries. Engineers write prompts. Designers evaluate model outputs. PMs manage token budgets. The lifecycle tracks how well your team has adapted to this reality.Cognitive Debt
The AI equivalent of technical debt. Shipping AI features without proper evaluation, monitoring, or feedback loops accumulates cognitive debt: models that drift, prompts that break, outputs that degrade silently.Using your lifecycle results
Your lifecycle report shows progress through each stage:- Complete stages have mature practices. Maintain them.
- Partial stages have some practices but gaps remain. These are your highest-leverage investments.
- Not started stages represent areas your team has not addressed. Prioritize these if they block downstream stages.
Common patterns
| Pattern | What it means | What to do |
|---|---|---|
| Strong Specify, weak Eval | Your team defines problems well but ships without quality gates | Invest in eval frameworks and human review loops |
| Strong Build, weak Compound | You build fast but do not learn from usage | Close the feedback loop with analytics and model retraining |
| Gaps in Context stage | AI features lack the knowledge layer to perform well | Prioritize RAG, embeddings, or context management |
| Weak Economics | Inference costs are unmanaged | Establish token budgets and cost tracking before scaling |
How lifecycle complements maturity
The URL score (F1) measures your product’s maturity. The lifecycle assessment (F2) measures your team’s build process. Together they reveal the full picture:- High F1, low F2: Your product is ahead of your process. Sustainability risk.
- Low F1, high F2: Your process is mature but has not yet produced results. Patience and execution needed.
- Both high: Compound readiness. Team, process, and product all aligned.
Next steps
Get coaching on gaps
Ask DAC to build an improvement plan for your weakest lifecycle stages.
Run a quick assessment
Complement your lifecycle with a URL-based maturity score.
Run a full diagnostic
For leaders who want the complete cross-framework view.
Set up integrations
Connected tools provide ground-truth signals that improve both scores.