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Buyer guide · Agentic AI · Eight criteria

Best agentic AI is the one your board can defend.

Eight buyer criteria separate defensible agentic AI from shiny-demo agentic AI. This page walks each criterion, names how CrawlQ and Quantamix score on it, and is honest about where neighbouring categories fit better. No leaderboard theatre — just the questions procurement actually asks.

Score your current stackSee the eight criteria

The eight criteria

Each criterion is a gate — a vendor that fails one is a vendor a regulated buyer cannot pick. Below each criterion we state how CrawlQ and Quantamix score on it. Verifiable evidence where possible.

1. Grounded in the customer's own data

The best agentic AI reads from a knowledge graph built from the customer's own foundation documents — positioning, personas, case studies, interview transcripts. Generic agents answer from the public internet and invent facts; grounded agents cite source. If the platform cannot produce the grounding document for any answer, it is not ready for regulated use.

How we score on this

Brand Memory is built from foundation documents on day one. Every generation carries a document-level citation trail.

2. Scored on a published standard

A platform that ships unscored output is asking the buyer to trust the vendor's judgement. A platform that scores every output on a published standard is asking the buyer to trust a repeatable measurement. Procurement prefers the second.

How we score on this

TRACE (Transparency, Reasoning, Auditability, Compliance, Explainability) plus BRAND Score (five dimensions, 0-100) run on every output. Below-threshold outputs do not ship.

3. Cross-agent verification, not single-model

Single-model agents cascade errors silently. A graph-of-agents where every decision is verified by a sibling agent catches hallucinations at the graph level. The accuracy delta between verified and unverified systems is typically 20+ points on governance questions.

How we score on this

99.7% accuracy on governance questions via multi-agent verification — open-source benchmark in GraQle, reproducible on public repos.

4. Cryptographic audit trail

An agent without an audit trail is a black box the regulator cannot inspect. Per-decision logging of model, prompt, grounding documents, score, and compliance tier — ideally with Merkle-chain tamper-evidence — is the minimum bar for EU AI Act Article 52 transparency.

How we score on this

Merkle-chain audit trail by default. Every decision exportable to a regulator on demand.

5. EU data residency by architecture

"GDPR compliant by policy" is a US vendor's claim. "EU AI Act compliant by architecture" requires the data and the processing both stay in the European Union. The distinction is a procurement gate in regulated EU industries.

How we score on this

AWS eu-central-1 (Frankfurt) by architecture. Data residency, processing jurisdiction, and audit logs all in the EU.

6. Cost structure that scales linearly, not quadratically

Single-model agents pay the frontier-model price on every query. Routed agents with knowledge-graph grounding pay frontier-model prices only when cheaper models cannot meet the score threshold. At scale the cost gap compounds.

How we score on this

50-800× lower cost than single-model incumbents on identical workloads, measured across banking, pharma, public sector, and critical infrastructure deployments.

7. Runtime-agnostic, not locked in

Vendors lock in. Governance layers integrate. The best agentic AI sits above the runtime — LangChain, LangGraph, Bedrock Agents, Azure AI Foundry, Agentforce — so a runtime migration does not require re-shipping the governance work.

How we score on this

Open-source SDK on PyPI — GraQle (2,009 tests, 201 skills). Adapters for the major runtimes. Governance layer is portable.

8. Built by practitioners who have shipped in regulated industries

Theory does not survive a supervisory review. The team that built the platform should be the team that can walk a regulator through an audit. Vendor demos with no live customer reference in your regulatory regime are a warning sign.

How we score on this

20+ years of enterprise AI across Philips, Amazon, ING, and Fortune 500. Philips reference architecture: 20h → 5h content cycle, €500K saved year one, 95% brand compliance.

Where neighbouring categories fit better

Not every use case needs a governance layer. Four categories where a different tool is the right answer.

General-purpose LLM chat (ChatGPT, Claude, Gemini)

Good for individual productivity. Not defensible as an enterprise agentic system — no grounding in customer data, no scoring, no audit trail.

Single-runtime agent frameworks (LangChain, LangGraph)

Good runtimes for building agents. They are not governance layers — grounding, scoring, and audit are what the customer builds on top. We ship that layer; we do not replace the runtime.

Vendor-owned agent platforms (Copilot, Agentforce, Einstein)

Excellent where the vendor's data graph matches the customer's needs. For brand-governance and regulated use cases, the grounding needs to be the customer's own knowledge graph, not the vendor's. We sit above these runtimes; we do not compete with them.

Single-model RAG products

A narrower slice of the grounded-generation pattern. Works for document Q&A. Insufficient for multi-step reasoning, cross-agent verification, and auditable publishing workflows.

Score your current agentic AI stack

A scoping call walks you through the eight criteria on your stack.

We run the criteria against whichever agentic AI setup you have today — your runtime, your grounding data, your audit posture — and give you an honest scorecard. If you pass on all eight we will tell you; if you have gaps we will show you where they are.

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Frequently asked questions

What makes an agentic AI system "best" for a regulated enterprise?

Defensibility. Speed and accuracy matter only after the board, the regulator, and procurement can all defend the output. Grounding in customer data, scoring on a published standard, cross-agent verification, a cryptographic audit trail, EU data residency, and a portable runtime-agnostic stack are the eight criteria that matter. Vendors that score on all eight survive procurement; vendors that score on three or four do not.

How is this different from generic AI agent rankings and reviews?

Most agent rankings optimise for benchmark accuracy and developer ergonomics. Those metrics matter, but they are necessary not sufficient. An agent that benchmarks well on MMLU and fails an EU AI Act Article 10 data-governance review ships zero value to a regulated enterprise. The eight criteria on this page weight the enterprise-critical attributes.

Where do you stand on Copilot, Agentforce, and Bedrock Agents?

Complementary, not competitive. Those platforms are excellent runtimes. Our governance layer — brand knowledge graph, TRACE scoring, cross-agent verification, Merkle-chain audit — sits above the runtime. A CrawlQ deployment can run on top of any major agent runtime, including all three.

Is the 99.7% accuracy number marketing or measured?

Measured. It comes from the open-source benchmark suite in GraQle: 2,009 tests across 201 skills, reproducible on PyPI. The benchmark spans four regulatory domains (banking, pharma, public sector, critical infrastructure). You can clone, run, and verify.

How do I evaluate this for my stack?

A 45-minute scoping call at https://calendly.com/crawlq-ai-demo/book-a-demo-call. We confirm the existing agent stack, the regulatory regime, and the use cases. From there the typical path is a pilot Campaign grounded in your foundation documents running alongside the current system for a measurable comparison. Scoping before commercial.

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