From Single Tool to Full-Link Intelligence
The bottleneck in ERP delivery has shifted. It's no longer about typing code faster; it's about understanding complex business logic, modeling massive data structures, and navigating legacy systems. Our vision for 2026 builds a "Second Brain" for the R&D organization.
The Current Bottleneck
Why purely coding assistants (Trae/Copilot) aren't enough.
The "Non-Coding" Time Trap
40% of our R&D cycle is consumed by analysis and design, not implementation. This is the prime target for AI optimization.
The Knowledge Chasm
Complex ERP logic creates a massive disparity in efficiency between new hires and veterans. We need structured asset precipitation.
The AI-Native R&D Brain
Redefining the Software Development Lifecycle (SDLC)
We are moving from a linear process to a circular, agent-driven workflow. "Coding" becomes "Logic Definition," and AI handles the assembly.
1. Intent Capture
Requirement Agent reads PRD/Audio
2. Logic Modeling
Data Schema MCP & Domain DSL
3. Auto Assembly
Code Gen & TDD Agents
4. Evolution
RAG Feedback Loop
Tooling Implementation
Deploying Agents (The Doers) and MCPs (The Sensors).
Model Context Protocol (MCP) Services
We will build standard interfaces connecting LLMs to our private ERP assets. The chart below prioritizes MCP development based on projected utility.
Agent Capabilities Matrix
Comparing the functional maturity of our planned AI Agents. "Legacy Sensei" is critical for managing 10+ years of code debt.
Efficiency Gains
By deploying the "Migration Wizard" and "Schema Explorer," we drastically reduce the manual overhead in ERP project delivery.
Path to 2026
Phase 1: Deep Tooling
Standardize `.trae` context files. Deploy Local Ollama models. Prompt Library v1.0.
Phase 2: Asset Precipitation
Deploy "Doc-Hub MCP". Clean private corpus. Launch team-specific RAG knowledge base.
Phase 3: Scenario Landing
Agentic Workflows live. "Legacy Sensei" analyzing old code. Automated TDD loops active.