Enterprise demo

See Agent OS Enterprise in action.

Choose an enterprise scenario below. See how one governed agent gets a deployment contract, private context, approved tools, budget and approval policy, reviewable workflow steps, receipts, and audit trace before it acts.

This demo is intentionally buyer-facing. It shows the repeatable Agent OS Enterprise pattern across platform, support, engineering, security, and operations workflows without dumping an internal system diagram on the page. The private runtime layer sits underneath each flow.

Explore scenarios Start pilot intake Try enterprise pilot copilot
5 scenariosBounded contextOperator traceGoverned retrievalContext GraphArtifact handoffSandbox checkpoints
Choose a scenarioWatch the flow
Interactive enterprise demo composition showing scenario selection, the private runtime, and grounded outputs.
Use this walkthrough to show how Agent OS Enterprise moves from deployment contract to governed workflow, with the private runtime layer underneath.
Lifecycle

Start with the deployment contract, end with receipts and audit trace.

Use this five-step walkthrough to frame the product before you dive into the individual enterprise scenarios.

01

Define deployment contract

Choose the governed agent role, runtime lane, exposure mode, and policy envelope the workflow must stay inside.

02

Attach approved context and tools

Connect only the approved systems, private context, and tools the deployment is allowed to use.

03

Set wallet budget and approval thresholds

Define spend caps, approval lanes, and action gates before the agent touches real work.

04

Run the governed workflow

Execute the bounded workflow with traceable context, tool allowlists, and reviewable action paths.

05

Review receipts, audit trace, and output

Inspect the resulting output, receipts, citations, and operator-visible trace before promotion or repeat use.

Choose an enterprise scenario below.

One deployment model, five enterprise patterns.

Each scenario shows the same Agent OS Enterprise motion: approved systems and approved tools feed the runtime, the deployment scopes before retrieval and action, and downstream AI receives governed context, receipts, and reviewable handoffs with trace.

AI platform team builds governed context for every internal consumer.

Instead of each team shipping its own retrieval pipeline, the platform team deploys one governed Agent OS Enterprise runtime. Internal copilots and agents call the same enterprise boundary for tenant-safe, actor-aware context and reviewable execution.

Tenant scopingPolicy enforcementBounded contextOperator visibility

Approved systems

  • Confluence
  • SharePoint
  • Internal APIs
Governed runtime
  • Tenant scoping
  • Policy enforcement
  • Retrieval strategy
  • Context packet assembly

Outputs

  • Internal copilot
  • Agent workflows
  • Context packet
  • Grounded answers
  • Reviewable artifact
Operator trace preview
scopeplatform-team knowledge scope
callerinternal platform copilot
approved_sourcesengineering docs, handbook, internal APIs
policyidentity scope, domain boundary
citations3 approved sources, 7 excerpts
bounded_contextbudget tracked, history filtered
source_manifestsafe labels, raw paths omitted
artifact_statusreview_required → approved
answer_modegrounded answer

Buyer takeaway

The private runtime becomes the governed context layer every internal AI consumer can share, rather than letting each team invent its own retrieval surface.

Architecture

Approved systems -> private runtime -> grounded AI outputs.

Use this view in sales, discovery, and pilot conversations to show exactly where the private runtime sits inside Agent OS Enterprise without dumping an internal system diagram on the buyer.

Private runtime four-layer architecture: sources, connectors, runtime, and output consumers.

The architecture story stays simple.

  • Approved systems stay the system of record.
  • Connectors and sync establish the governed working set.
  • The private runtime applies scoping, retrieval policy, answer controls, and action gating before downstream model use.
  • Bounded context bundles package evidence, citations, and budget signals before the model receives anything.
  • Retrieval can combine multiple modes and bias toward the right sections in long materials.
  • Repository review and graph views stay source-safe and scoped to the approved work surface.
  • Reviewable artifact packets preserve citations, provenance, freshness, and risk state when the workflow needs a durable handoff.
  • Sandbox checkpoints, rollout controls, and recovery reviews keep private changes inspectable before promotion.
  • Internal apps, copilots, and agents receive grounded context instead of direct raw-system access.
  • Retrieved content stays evidence-only, and risky outbound or delegated actions can be blocked or reviewed.
Governance

Every request is traceable and reviewable.

The governance layer is the differentiator. The private runtime returns context plus the evidence required to operate enterprise AI responsibly, even when source content is noisy or adversarial.

Private runtime review console showing audit trace, grounded answer, and policy indicators.
  • Full request trace: which approved connectors, scoping rules, sources, and policies were applied.
  • Scoped access control: role, actor, group, action, and data-boundary rules can be applied before retrieval or action.
  • Separate access-audit trail: governed requests can be queried and exported without mixing the core audit history into general app data.
  • Grounded citations: every answer links back to the approved sources that contributed context.
  • Policy enforcement: identity, scope, and retrieval rules are applied before model use.
  • Isolation profile: the deployment contract can make shared logical, dedicated single-tenant, or customer-hosted boundaries explicit.
  • Bounded-context diagnostics: token budget used and remaining, included sources, omitted sources, and safe citation manifests.
  • Retrieval safety: suspicious hidden-content or prompt-injection patterns can be flagged before they steer the runtime.
  • Artifact review: compiled handoffs keep provenance, freshness, and review state attached to generated outputs.
  • Graph and sandbox review: related sources, review packages, checkpoints, and handoff materials can be inspected before rollout.
  • Private adapter review: live execution paths stay customer-approved instead of silently turning on.
  • Optimization review: private runtime changes stay inspectable before promotion.
  • Recovery review: bounded recovery actions require dry-run or operator approval before they proceed.
  • Operator readiness: health checks summarize source, retrieval, identity, and policy posture before rollout.
  • Memory review: low-trust memory stays reviewable or quarantined instead of silently becoming durable context.
  • Operator review: platform teams can inspect any request end-to-end before production use.
Ready?

See Agent OS Enterprise govern a real workflow.

Start with a structured intake or book a live pilot demo with the Agoragentic team. The private runtime powers the governed layer underneath the deployment.

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