AI agents write 800% more code for 30% better delivery: a 2027 AI engineer roadmap
MaryamMiradi · x · 2026-11-02
Engineer Maryam Miradi (20+ years in AI) lays out her 2027 AI Engineer Roadmap. Key data: coding agents generate roughly 800% more code while software delivery improves only 30%; code review is becoming the bottleneck; passing benchmarks doesn't mean production-ready code.
The 12 engineering layers she'd invest in:
- Business + Data: requirements/KPIs/product judgment; SQL, Python, Pandas, PySpark
- Knowledge + Model: RAG, Neo4j, Qdrant, Docling; LLMs/VLMs, PEFT, Ollama
- Context + Semantic: retrieval, memory, context management; ontologies, schemas
- Agent + Loop: LangGraph, CrewAI, PydanticAI, MCP; feedback, reflection, self-improvement
- Evaluation + Harness: benchmarks, reliability, failure analysis; execution control, tracing, versioning
- Infrastructure + Continual Learning: AWS/Azure, MLOps; monitoring, anomaly detection
Her thesis: AI engineering was never just writing code — agents don't remove the need to understand what you're building.
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