# Enterprise Readiness Scorer

> |

- **Type:** Skill
- **Install:** `agentstack add skill-anthonyalcaraz-agentic-graph-rag-skills-enterprise-readiness-scorer`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [AnthonyAlcaraz](https://agentstack.voostack.com/s/anthonyalcaraz)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [AnthonyAlcaraz](https://github.com/AnthonyAlcaraz)
- **Source:** https://github.com/AnthonyAlcaraz/agentic-graph-rag-skills/tree/main/skills/crisis/enterprise-readiness-scorer
- **Website:** https://www.oreilly.com/library/view/agentic-graphrag/9798341623163/

## Install

```sh
agentstack add skill-anthonyalcaraz-agentic-graph-rag-skills-enterprise-readiness-scorer
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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".

## When NOT to Use

- Ranking LLMs by quality — Ch1 is explicit that the flaws are
  architectural, not model-quality ("cannot be addressed by expanding
  context windows or refining embedding techniques")
- Consumer FAQ / support bots where queries map to a text snippet — Ch1
  names these as a great fit for plain vector RAG
- Single-turn Q&A with no actions, state, or temporal evolution

## Process

| Step | Input | Action | Output | Verification |
|------|-------|--------|--------|--------------|
| 1 | profile JSON (graph_capabilities map) | `lib.score_flaws(caps)` | (points, cured, open_flaws) | each flaw cured only by its mapped capability |
| 2 | profile JSON (agency map) | `lib.score_agency(agency)` | (points, missing dims) | scores coverage/calibration, not magnitude |
| 3 | profile JSON (capabilities map) | `lib.score_capabilities(caps)` | (points, missing) | proportional to capabilities present |
| 4 | captures_rejected_alternatives bool | `lib.decision_trace_test(b)` | (15 or 0, note) | binary test per Marple |
| 5 | full profile | `lib.assess(profile)` | score + band + recommendations | score bounded 0-100; band matches thresholds |

## Rationalizations

| Agent rationalization | Documented rebuttal |
|------------------------|--------------------|
| "Our LLM is frontier-grade, so we are production-ready." | Ch1: the five flaws "aren't bugs. They represent an architectural failure" and "cannot be addressed by optimizing search or tweaking embedding models." Model quality does not cure an architecture gap. |
| "We have a vector store, that covers retrieval." | A vector store cures none of the five flaws by itself. Score it: all five stay open, band is NAIVE-VECTOR. The flaws are cured by graph capabilities, not by a vector index. |
| "We logged everything, so we have a context graph." | Marple's test (Ch1, The Context Graph): can it tell you what alternatives were *rejected*? Logging final states is read-time data. The decision-trace test is worth 15 points precisely to catch relabeled search indexes. |
| "Max out autonomy and authority for a powerful agent." | Ch1: agency dimensions are sliding scales and must be *calibrated*, not maximized. The real-estate agent has high autonomy, near-zero pricing authority. This scorer rewards calibration coverage, not magnitude. |

## Red Flags

- **Score is high but decision_trace is 0.** The agent may pass the
  capability checklist while recording only outcomes; it will become "a
  high-fidelity log of failure" if it ever hallucinates (Ch1 counter-thesis).
- **All five flaws open but band is not NAIVE-VECTOR.** Scoring bug —
  open flaws should dominate; recheck the FLAW_CURE mapping.
- **Agency magnitude drives the score.** Misreads Ch1: a deliberately
  low-authority agent is correct design, not a deficiency.

## Non-Negotiable Verification

1. **Run the benchmark battery.** `python cli.py benchmark` must report:
   - all-open flaws score 0; all-cured score the full 40
   - each flaw cured only by its mapped graph capability
   - decision-trace test is binary 15/0
   - a perfect profile is exactly 100 and PRODUCTION-READY; empty is NAIVE-VECTOR
   - agency scores calibration coverage, not magnitude
2. **Verify CLI help.** `python cli.py --help` exits 0 and prints the SKILL.md description.

## Security Posture

- **Prompt injection.** The profile JSON is untrusted input - a vendor can
  self-report flattering capability booleans to inflate the score. The scorer
  only reads fixed keys against a fixed rubric; adversarial field values can
  skew the score but never execute, so verify claimed capabilities (especially
  `captures_rejected_alternatives`) against evidence before trusting a band.
- **Data exfiltration.** No network calls, no file writes. Architecture profiles
  may describe internal systems; they stay in-process and surface only in the
  stdout report the caller owns.
- **Privilege escalation.** No shell invocation, no eval, no dynamic import. A
  PRODUCTION-READY band is advisory - it authorizes nothing; the deployment
  gate that consumes the score owns the actual go/no-go decision.

## Source Attribution

Distilled from *Agentic GraphRAG* (O'Reilly) by Anthony Alcaraz and Sam Julien
— Ch1: The Crisis of Agentic AI, specifically the five-fatal-flaws opening
(the naive-vector failure), the "Defining Agentic AI" agency dimensions and
capabilities, and the "Enterprise Context Graphs" decision-trace test.
Supporting references: Singhal 2012 (strings to things), Lilian Weng 2023
(LLM-as-brain), Arvind Jain / Glean context-data-platform, Kirk Marple /
Graphlit rejected-alternatives test.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [AnthonyAlcaraz](https://github.com/AnthonyAlcaraz)
- **Source:** [AnthonyAlcaraz/agentic-graph-rag-skills](https://github.com/AnthonyAlcaraz/agentic-graph-rag-skills)
- **License:** MIT
- **Homepage:** https://www.oreilly.com/library/view/agentic-graphrag/9798341623163/

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-anthonyalcaraz-agentic-graph-rag-skills-enterprise-readiness-scorer
- Seller: https://agentstack.voostack.com/s/anthonyalcaraz
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.