enterprise-readiness-scorer

Enterprise Agentic-Readiness Scorer

Overview

Ch1 opens with a promise and a trap. The promise: an agent that pursues goals instead of answering questions. The trap: "you fire up your favorite LLM, add it to your agentic framework of choice, and connect it to a vector-based RAG system. Should be easy, right? Wrong." A naive vector-only approach creates five fatal flaws that are not bugs but an architectural failure preventing the system from becoming truly agentic.

This skill turns that diagnosis into a score. It checks four things the chapter argues are required for enterprise agency:

1. The five fatal flaws are cured. Each flaw is cured only by a specific graph capability (context amnesia by evolving memory, relationship blindness by entity relationships, temporal ignorance by temporal evolution, reasoning paralysis by multi-hop reasoning, tool chaos by tool orchestration).
2. The three agency dimensions are calibrated. Autonomy, action, and authority are sliding scales, not binary — and Ch1's point is calibration, not maximization (a real-estate agent has high autonomy but deliberately low pricing authority).
3. The four emergent capabilities are present. Autonomous decision-making, contextual understanding, strategic tool utilization, memory persistence.
4. The decision-trace test passes. Per Marple's test in the Enterprise Context Graphs section: can the system tell you not just what happened, but what alternatives were considered and rejected?

When to Use

• Before greenlighting an enterprise agent for production deployment
• Reviewing a vendor's "context graph" claim against the rejected-alternatives test
• Comparing a naive-vector prototype to a graph-augmented redesign
• Architecture review where someone proposes "just add a bigger vector store"

Phrases: "is this agent production-ready", "enterprise agentic readiness", "score my RAG architecture", "are we naive vector RAG", "context graph vs search index".