Control and outcomesBusiness leaders start here

1. Layer 07

   Outcome managementHold every agent to a business result, a named owner, and a cost per outcome. Agent workforce management lives here: scale what earns its keep and retire the rest.

   Is every agent earning its place?

   • Outcome and ROI reportingCost per resolved case, completed task, or accepted outcome, reported in the terms finance uses.
   • Agent workforce managementAn owner, a target, and a keep, fix, or retire decision for every agent in production.
   • Agent service levelsQuality, latency, and cost targets per agent, with an alert when one drifts.
2. Layer 06

   Governance and securityControl what each agent can see, decide, and spend, with permissions and an audit trail down to the record.

   What can each agent see, decide, and do?

   • AI usage discoveryFinding the assistants in use that nobody put through procurement.
   • Agent and connector intakeA risk-tiered gate for every new agent, assistant, and connector, covering owner, access, and rollback.
   • Framework alignmentNIST AI RMF and similar frameworks, cut down to the risks your business actually carries.
   • Tool boundaries and least privilegeExplicit limits on what an agent can reach, sized to the cost of being wrong.
   • Human approval gatesA named person approves anything that sends, pays, signs, or files, and nothing else.
   • Trace and audit loggingA record of what the agent saw, chose, and called, built for audit rather than debugging.
   • Security review supportGetting an AI system through the review it has to pass to go anywhere.

Business context and agentsData and AI teams start here

1. Layer 05

   Agent build and operationsBuild agents and applications that finish the work, with tools, approvals, retries, and handoffs to people.

   Can the agent complete the work?

   • Document workflow automationDocuments into the system of record without a person keying the values, with exceptions routed to a queue.
   • Bespoke agentic applicationsSoftware built around your workflow, with agents inside, in place of SaaS seats bent to fit it.
   • Multi-agent orchestrationSplitting work across agents where one prompt asked to do all of it degrades at all of it.
   • Durable workflow stateState that outlasts a session, for processes measured in months rather than minutes.
   • Conversational and voice systemsLatency and hallucination budgets treated as engineering constraints rather than caveats.
   • Agent-readable standards and contextArchitecture and operational rules in a form coding agents consume directly.
   • Defect and anomaly detectionSurface and process-condition detection on continuous lines.
   • Proximity and safety monitoringVehicle and pedestrian conflict detection in active work areas.
   • Counting and throughput reconciliationPiece and unit counts in the places where the number is still manual.
2. Layer 04

   OntologyModel your products, customers, policies, and rules once, so every agent reasons from the same definitions.

   Do your agents share one model of the business?

   • Domain ontology designProducts, customers, policies, and the rules between them, modeled once and shared by every agent.
   • Knowledge graphs and graph retrievalAnswers that follow relationships across records, where plain retrieval stops improving.
   • Natural-language query over business dataPlain-English questions answered against the graph and the warehouse, with the source attached.

FoundationCIOs and platform teams start here

1. Layer 03

   DataOpsTurn documents and records into current, tested evidence an agent can trust.

   Is the right evidence ready?

   • Document intelligenceExtraction from evidence-heavy documents, including the scanned ones, with a citation on every field.
   • Retrieval and knowledge layersChunking, embedding, and indexing built so retrieval quality is a number you can check.
   • Lakehouse architectureGoverned lakehouse design on Databricks or native cloud services.
   • Data governance and catalogingOwnership, lineage, and access rules that hold up when an auditor pulls the thread.
   • Streaming and event pipelinesGetting data where a detection or an agent needs it in seconds rather than overnight.
   • Data migration and modernizationMoving warehouses and legacy stores without a freeze the business will not agree to.
   • Data quality and observabilityFinding out a pipeline broke before a model trained on the gap.
2. Layer 02

   Model operations and routingRoute every task to the smallest model that meets the bar, so a better model can drop in without a rebuild.

   Can you change models without rebuilding?

   • Model gateway and routingOne entry point for model calls, with policy deciding which model answers.
   • Evaluation harnessesScoring a model or agent against your real historical cases before it touches a live one.
   • MLOps and model deploymentThe path from a trained model to a versioned endpoint you can roll back.
   • Model and prompt release managementVersioned changes, and a way back when a newer model regresses on your cases.
3. Layer 01

   InfrastructureRun each workload where it belongs, from public cloud to fully private and air-gapped.

   Where should this workload run?

   • Cloud landing zonesAccount structure, identity, and network baseline a security team signs off once.
   • Multi-cloud architectureDesigns that hold across AWS, Google Cloud, and Azure rather than assuming one.
   • Private and sovereign deploymentModel workloads inside your boundary, for data that is not allowed to leave it.
   • Kubernetes and container platformsCluster design and workload isolation for inference that scales unevenly.
   • Edge inference deploymentRunning models on the plant floor, where bandwidth and latency rule out a round trip.
   • Camera and sensor integrationWorking with the coverage already installed before anyone proposes new hardware.
   • Site rollout and commissioningTaking one proven line or site to the rest without starting over each time.