The AECM Framework
The Augmented Engineer Competency Model defines 8 skill domains that capture what it means to be an effective AI-augmented software engineer. Each domain is independently assessed, tracked, and developed.
Why a new competency model?
AI tools have changed what it means to be productive as a software engineer. Writing code is no longer the bottleneck — the bottleneck is specifying what to build, verifying the output, and orchestrating the human-AI workflow.
Traditional engineering skill frameworks focus on language proficiency, system design, and algorithmic thinking. These skills remain important, but they no longer tell the full story. AECM fills the gap by defining the skills that determine how effectively an engineer collaborates with AI tools.
The 8 Domains
Each domain represents a distinct category of skills. Together they form a complete picture of augmented engineering competency.
Decomposition & Specification
The ability to break down complex problems into well-specified, AI-executable tasks with clear constraints and acceptance criteria.
Breaking work into appropriately-sized units that an AI tool can execute effectively.
Clearly defining edge cases, error conditions, and non-functional requirements.
Determining what context an AI tool needs and scoping information appropriately.
Defining measurable success criteria for each task that enable verification.
Context Orchestration
The ability to create and maintain the contextual environment that enables effective AI collaboration.
Providing AI tools with the right information at the right time.
Structuring project documentation for effective AI consumption.
Setting up AI tool configurations (CLAUDE.md, MCP servers, etc.) for optimal collaboration.
Verification & Quality Assurance
The ability to critically evaluate AI-generated output for correctness, security, performance, and maintainability.
Identifying bugs, logic errors, and quality issues in AI-generated code.
Detecting security vulnerabilities in AI-generated output.
Identifying performance issues and optimization opportunities.
Evaluating structural quality and maintainability of generated code.
Iterative Refinement
The ability to efficiently guide AI tools toward desired outcomes through feedback and iteration.
Providing clear, actionable feedback that moves AI output toward the target.
Achieving desired outcomes in fewer rounds of interaction.
Recognizing when to change strategy vs. continue refining the current approach.
Domain Translation
The ability to translate business domain knowledge into technical specifications that preserve domain constraints.
Ensuring domain-specific rules and regulations are maintained in technical output.
Identifying incorrect assumptions an AI tool might make about the domain.
Articulating domain requirements in a way AI tools can use effectively.
Tool Ecosystem Management
The ability to select, configure, and orchestrate the right AI tools and workflows for each task.
Choosing the appropriate AI tools and configurations for specific tasks.
Designing efficient workflows that leverage multiple tools effectively.
Augmentation Prioritization
The ability to determine which tasks benefit from AI augmentation and design the optimal human-AI workflow.
Accurately classifying tasks as AI-first, manual-first, or hybrid.
Designing the optimal balance of human and AI effort for each task.
Knowledge Curation
The ability to build and maintain knowledge artifacts that enhance AI collaboration over time.
Creating comprehensive documentation that enables effective AI assistance.
Maintaining and updating knowledge artifacts as projects evolve.
Structuring documentation specifically for AI tool consumption.
Discover your skill profile
Take the free onboarding assessment and see where you stand across all 8 AECM domains.