Platform · AI Agents · EU-hosted by architecture
AI agents the way regulators and boards accept them.
Graph-of-agents on a brand knowledge graph. Multi-level reasoning. Cross-agent verification at 99.7% accuracy on governance questions. Every decision scored on TRACE, logged on a Merkle-chain audit trail, served from EU-central-1 by architecture.
99.7%
Accuracy
Multi-agent verification on governance questions — open-source benchmark in GraQle
50-800×
Cost advantage
Lower than single-model incumbents on identical workloads
2,009
Tests passing
GraQle open-source SDK on PyPI, 201 skills, verifiable
EP 26162901.8
Patent
Brand knowledge graph reasoning method, Quantamix Solutions B.V.
What we build
Generative AI got fast. The teams shipping agentic systems at scale also need them defensible. Generic agents guess; governed agents cite. The difference is the layer that sits between the model and the business decision: a brand knowledge graph, a scoring function, and an audit trail.
Our position is the opposite of most agent stacks. Where Copilot and similar runtimes optimise for speed of a single agent answering a single question, we optimise for the reliability of a multi-agent graph answering a compounded business question — one a board, a regulator, or a procurement committee will ask about six months later.
Below are the eight capabilities that separate a governed graph-of-agents from a single-model chain.
Eight capabilities that make agents defensible
Graph-of-agents, not chains
Single-model chains hallucinate and cascade errors. A graph-of-agents puts every agent as a node in a reasoning graph — each node reads from the brand knowledge graph, writes to a shared scored state, and is cross-checked by sibling agents before its output is committed. Errors surface early; they do not compound.
Multi-level reasoning
Reasoning flows up levels rather than down chains. A first-level agent answers; a second-level agent scores the answer against the brand knowledge graph; a third-level agent decides whether to publish, revise, or escalate to human review. Every level is inspectable and every decision carries a score.
Cross-agent verification
Agents verify each other. The same question routed through multiple agents produces multiple answers; a verification agent compares them, scores agreement, and surfaces disagreement as a signal rather than a failure. This is the pattern that gets the system to 99.7% accuracy on governance questions.
Grounded in the brand knowledge graph
Every agent answers from the customer's own foundation documents — persona research, voice rules, positioning, case studies, interview transcripts — held as an interconnected graph. Generic agents answer from the public internet. Governed agents cite the brand's own source. The difference reads in every output.
TRACE scoring on every decision
Transparency, Reasoning, Auditability, Compliance, Explainability — each scored 0-100 for every agent decision. Low scores block publication; high scores publish with a full audit trail. TRACE is the scoring function that makes agentic AI defensible to legal, procurement, and the board.
50-800x lower cost than single-model incumbents
Grounding + scoring + routing means the expensive model is called only when cheaper models cannot meet the TRACE threshold. Most agent decisions resolve on routed cheaper models with the knowledge graph doing the heavy lifting. The aggregate cost advantage over single-model stacks is typically 50-800x on identical workloads.
Merkle-chain audit trail
Every agent decision writes to a cryptographic audit trail — model, prompt, grounding documents, score, compliance tier. The trail is tamper-evident and exportable to a regulator on demand. This is what turns agentic AI from an operational capability into a governance asset.
EU-hosted by architecture, not by policy
The entire stack runs on AWS eu-central-1 (Frankfurt). Data residency, processing jurisdiction, and audit logs all stay in the European Union. For regulated EU procurement this is a gate — platforms that cannot demonstrate EU-native architecture are excluded before the technical evaluation begins.
Where governed agents ship first
Four use cases that carry the governance model naturally. Each links to the pillar where the pattern is documented in detail.
Market research agents
Agents grounded in customer interviews, competitor reviews, and community discussion produce research deliverables a board will accept. The flagship at /blog/market-research-using-generative-ai walks the full loop.
Brand-safe content generation
Multi-model routing plus SCORCH visual audit plus BRAND Score text scoring means every piece of content that ships is grounded, scored, and publishable. Text governance and visual governance run in the same pipeline.
Compliance and governance agents
Agents that read GDPR Article 30 records, EU AI Act Article 10 data governance pipelines, and BCBS 239 lineage artefacts and score them against policy. The agent that catches a non-compliant change before it ships is the one that pays for the programme.
Salesforce agentic integration
Agentforce, Einstein, and Data Cloud wired into the brand knowledge graph. Sales responses are grounded in the brand's own case studies and positioning; every decision carries a TRACE score a customer and a compliance officer can inspect.
One intelligence layer, not another model
Content, compliance, and AI adoption are three problems that every enterprise is solving in parallel. We believe they are one problem wearing three coats — the problem of grounding, scoring, and auditing AI output so the business can defend it. One knowledge graph, one scoring function, one audit trail answer all three.
That position is documented across four pillars on this site — Brand governance for AI content, Brand intelligence for market research, Brand-safe content generation, and EU AI Act for marketing compliance. The same graph-of-agents pattern on this page powers all four. The pillars are the answer by buyer question; this page is the answer by platform primitive.
Evaluate the pattern on your stack
A scoping call with the team that shipped the pattern.
No sales intermediary. We confirm use case, regulatory scope, and existing AI stack on the call. From there the typical path is a pilot Campaign grounded in your foundation documents, running alongside your current system for a measurable comparison.
Frequently asked questions
What is a graph-of-agents?
A graph-of-agents is a reasoning topology where each AI agent is a node in a graph rather than a link in a chain. Agents read from a shared knowledge graph, write to a scored state, and cross-check each other before an answer is committed. The structural difference from chain-of-agents is that errors surface as disagreement between nodes rather than cascading silently down a chain. This is the pattern that gets multi-agent systems to 99.7% accuracy on governance questions.
What does multi-level reasoning mean in practice?
Multi-level reasoning means an agent's answer is itself the input to a scoring agent, whose verdict is itself the input to a publishing agent. Level 1 answers. Level 2 scores. Level 3 decides. Each level is inspectable, each decision carries a score, and the system can retract weak claims or escalate to human review when confidence drops. Single-level agents cannot reason about their own conclusions; multi-level agents can.
How do your AI agents differ from Agentforce, Copilot, or LangChain agents?
All three are fine runtimes for individual agents. None of them ships a brand knowledge graph, TRACE scoring, cross-agent verification, or a Merkle-chain audit trail by default. Our position is complementary to those runtimes — you can run our governance layer on top of Agentforce or LangChain. The difference is that governance is a first-class capability rather than something a team has to build from scratch.
Is this open source?
Yes. GraQle — the reasoning-network SDK underneath — is open-source on PyPI with 2,009 tests and 201 skills. The commercial layer is the brand-knowledge-graph grounding, TRACE scoring, and managed audit trail. The pattern is published; the data model, integration work, and regulatory alignment are what customers pay for.
Can I run these agents inside my existing AI stack?
Yes. The graph-of-agents pattern is runtime-agnostic. We ship adapters for the common runtimes (LangChain, LangGraph, AWS Bedrock Agents, Azure AI Foundry, Agentforce) and the scoring and audit layers sit above whichever runtime you have. Most customers integrate rather than migrate.
Where does the 99.7% accuracy number come from?
From the open-source benchmark suite inside GraQle: 2,009 tests across 201 skills, run across four regulatory domains (banking, pharma, public sector, critical infrastructure). The number is reproducible — you can clone the repo, run the benchmark, and verify. The accuracy comes from cross-agent verification rather than a larger model; a single-model baseline on the same benchmark scores meaningfully lower.
How do I evaluate this for my organisation?
Start with a scoping call on Calendly at https://calendly.com/crawlq-ai-demo/book-a-demo-call. We confirm use case, regulatory scope, and existing stack. From there the typical path is a pilot Campaign grounded in your foundation documents, running alongside your current system for a measurable comparison. Scoping call first; commercial discussion second.
Related reading
- Best agentic AI — governed by design — the comparison buyer question
- How it works — use cases end-to-end — the workflow view
- Corporate AI training — graph-of-agents and multi-level reasoning taught as a programme
- Brand governance for AI content — the scoring pillar this page operationalises
- Quantamix Solutions — the parent organisation and the full enterprise AI portfolio