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How connected
AI systems work.

Explore the components, data flow and human controls behind a proposed system. These architectures explain an approach; they are not client deployments.

Give intelligence a way to act.

Build agents around the work: the context they need, the tools they can use, and the decisions people should own.

Example architecture

Open the full architecture for technical detail. A readable walkthrough follows below.

agents example system architectureOriginal explanatory architecture, not an installed client system. Labeled connections describe the example; the surrounding page provides a readable explanation.REQUEST / INTERFACEAGENT EXECUTION BOUNDARYBUSINESS SYSTEMSA person has a taskAPPLICATION / VOICE / EVENTRequest + session IDA reviewable resultANSWER / STATE / TOOL EVENTSSOURCE + RUN TRACEContext assemblyPOLICY · MEMORY · EVIDENCESession & run stateHISTORY · LIMITS · IDENTITYMODEL + INSTRUCTIONSChoose the next stepAction gatewayACCESS + APPROVAL POLICYPERMITTED ACTIONSTOOL CALLRESULTCRM / ERPRECORDS + ACTIONSData & knowledgeREAD / RETRIEVEBusiness APIsSCOPED TOOLSEvaluate & observeERRORS · SOURCES · RUN HISTORYHuman reviewAPPROVE / CHANGE / DECLINEREVIEW IF REQUIREDAPPROVEDTHE MODEL PROPOSES A STEP. THE APPLICATION CONTROLS WHAT CAN EXECUTE.
Flow & relationshipsBoundaries & controlsIllustrative architecture
  1. 01

    Understand the request

    Combine the task with the current session and the rules for this workflow.

  2. 02

    Select the next step

    The model can answer, request a tool or ask for missing information.

  3. 03

    Execute with boundaries

    Validate tool inputs and permissions. Route consequential actions through an agreed review gate.

  4. 04

    Check the result

    Return tool results to the agent. Record the run and surface the outcome to the user.

A useful system connects a request to a verifiable next step.Discuss this type of system
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