Agent6 Intelligence Stack

The private control layer between
AI models and your operation.

Agent6 organises specialised agents, model selection, organisational memory, human approval, verification and audit into one controlled system on your infrastructure. AI becomes reliable executable work without dependence on a single model provider.

Interactive architecture

Six layers. One closed accountability loop.

Select a layer to see how signals become controlled execution. Governance, model routing and assurance run through the entire stack; approved corrections inform the next task.

LAYER 01ACTIVE

Purpose

Goal, authority & boundaries

Defines the purpose, rules, allowed actions, blocked actions and accountable owner before an agent starts.

  • Agent Passport
  • Policy & authority
  • Data and tool boundary

Why a stack?

A model generates output. A workforce must control work.

Operational work needs identity, current context, task allocation, model choice, verification, escalation, logging and recovery. Agent6 makes that private execution layer explicit and governable.

Orchestration

Work is distributed as typed steps to the right agent, tool, model or person.

Verification

Output is checked against schema, sources, uncertainty and agreed quality rules.

Audit

Every material step records time, actor, input, outcome and approval.

Organisational memory

Process knowledge and approved corrections become versioned context for later tasks.

Private access boundaries

Each agent sees only the sources and tools required for its role inside your environment.

Model freedom & fallback

Models can be selected per task, replaced or run locally without rebuilding the workflow.

From experiment to AI-first operation

Your systems remain. Agent6 builds the private process layer between them.

The first implementation runs as an Edge Twin beside the current workflow. People and AI process the same cases; more execution shifts only after quality and control have been proven.

Separate AI toolsAgent6 private workforce
Everyone prompts in a different wayFixed process role, authority and quality criteria
Send data to a separate modelOn-premises or private deployment inside your boundary
Dependent on one AI modelModel selection per task and replaceable providers
Output disappears into chatsTasks, decisions, sources and corrections remain traceable
A pilot stays a pilotMeasurable path from parallel twin to production