The Future of Engineering Isn't Pure Human or Pure AI — It's Augmented
The debate raging across our industry feels tired and binary: either AI agents will replace programmers entirely, or they'll never match the quality of senior engineers who understand architecture and systems design. Both camps are missing the point.
The real future isn't about choosing between human and artificial intelligence — it's about augmented engineering, where developers learn to work with AI agents rather than competing against or ignoring them entirely. But this future requires a fundamental shift in how we think about engineering competency.
That's why I've spent the past year developing the Augmented Engineering Competency Model (AECM) and launching Skillarc, a platform designed to help engineers navigate this transition.
What Is the AECM?
The AECM breaks down augmented engineering into eight core domains, each representing a critical skill for working effectively with AI agents:
- DS: Decomposition and Specification — Breaking complex problems into clear, actionable components that AI can execute
- CO: Context Orchestration — Managing the information flow and context that AI agents need to produce quality output
- VQ: Verification and Quality Assurance — Systematically validating AI-generated code and catching errors before they ship
- IR: Iterative Refinement — Guiding AI through multiple rounds of improvement to reach production-quality results
- DT: Domain Translation — Converting business requirements and technical constraints into language AI agents understand
- TE: Tool Ecosystem Management — Coordinating multiple AI tools and integrating them into existing development workflows
- AP: Augmentation Prioritization — Deciding when to use AI assistance versus when human expertise is essential
- KC: Knowledge Curation — Building and maintaining the knowledge base that makes AI agents more effective over time
These aren't abstract concepts. They're practical skills that distinguish engineers who struggle with AI tools from those who use them to amplify their impact.
Why Traditional Competency Models Fall Short
Most engineering competency frameworks assume a purely human workflow: write code, review code, deploy code. They measure algorithm knowledge, system design skills, and coding proficiency — all important, but incomplete in an AI-augmented world.
The AECM recognizes that engineering is becoming a collaborative process between human and artificial intelligence. Success isn't just about what you know; it's about how effectively you can guide, verify, and refine AI output to meet production standards.
Consider Context Orchestration: a senior engineer might know exactly how to implement a feature, but if they can't provide the right context to an AI agent — relevant code snippets, architectural constraints, business logic — the AI will produce generic, unusable output. This is a learnable skill that traditional competency models don't address.
Introducing Skillarc
To make the AECM actionable, I built Skillarc, a platform with three core components:
The AECM Framework explains each domain in depth, with real-world examples and practical guidance on developing these skills.
The Assessment Tool evaluates your current competency across all eight domains using AI analysis of your actual prompts and interactions. Rather than multiple-choice questions, you demonstrate your skills through realistic scenarios, then receive detailed feedback on areas for improvement.
The Learning Platform provides personalized skill development based on your assessment results, complete with a skills radar showing your strengths and growth opportunities.
Building a Community of Practice
Skillarc isn't just about individual skill development. The platform includes a calibration system where experienced practitioners can help others refine their projects and approaches. My goal is to create a community where engineers learn from each other as we collectively figure out this new way of working.
This matters because augmented engineering is still evolving. We're all learning together, and the engineers who share knowledge and help others improve will shape the future of our profession.
The Path Forward
The transition to augmented engineering won't happen overnight, and it won't be uniform across all domains or companies. But engineers who develop these skills now will have a significant advantage as AI tools become more sophisticated and ubiquitous.
The choice isn't between human engineers and AI agents. It's between engineers who can effectively collaborate with AI and those who can't. The AECM provides a roadmap for that collaboration.
Check out Skillarc and take the assessment. I'm curious to see where you stand and how we can help each other navigate this transition. The future of engineering is augmented — let's build it together.
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