Gemini Enterprise
Prove Gemini Enterprise agents before they touch your data.
Gemini Enterprise brings agents that plan, call tools and act across your systems. That capability is exactly why it belongs in containment first. NayaOne is the air-gapped environment where you evaluate Gemini’s agentic behaviour on synthetic data and real APIs, then move to production with the evidence risk and security require.
We wanted to see what the agents would actually do before we let them near a customer system. In the sandbox we watched every tool call, on synthetic data, and only then signed off.
Head of AI
Tier-1 bank
0
real customer records exposed during evaluation
Days
to a governed agent evaluation, not quarters
Why agents belong in a sandbox first
An agent that answers is a chatbot. An agent that acts is a risk decision.
Gemini Enterprise agents plan, call tools and act across systems. Before granting that reach into production, see exactly what the agent does, and prove it.
Agents take actions
Gemini Enterprise agents plan, call tools, query connectors and chain multiple steps. Before you grant that reach into live systems, you need to see exactly what the agent does, and prove it.
Contain the blast radius
Inside the air-gap, an agent’s tool calls hit synthetic data and mocked APIs, with independent entitlements per project. A mistake stays inside the boundary, not in production.
Evidence for governance
Every agent run is logged, what it planned, which tools it called, on what data, with what result. Risk and security interrogate the trace, not a vendor questionnaire.
The path to production
The same environment from first agent run to production handoff.
1
Stand up Gemini Enterprise in the air-gap
Provision an isolated workspace and bring Gemini Enterprise in through governed ingestion, no corporate identity wired to production, no data leaving the boundary.
2
Point it at synthetic data and pre-integrated APIs
Wire connectors and tools to representative synthetic data and 1,500+ pre-integrated APIs, so the agent behaves as it would in production without touching a real record.
3
Test the agentic loop
Exercise planning, retrieval, tool calls and multi-step tasks. Measure accuracy, latency, cost and guardrail behaviour, and probe how the agent handles ambiguity and failure.
4
Generate the evidence pack
Export a documented record of what was tested, on what data, with what result, and the full agent trace, so governance happens early, not as a last-minute blocker.
5
Launch to production
Hand the proven agent off with the evidence to back it. The evaluation that would have taken a quarter to even scope is done in days.
In practice
The agentic use cases teams are proving with Gemini.
Onboarding & KYC agents
Agents that read documents, cross-check watchlists and assemble a case file, evaluated on labelled synthetic identities before they see a real applicant.
Fraud & financial-crime copilots
Investigation agents that triage alerts, gather context across systems and draft a rationale, tested against representative fraud patterns with a full audit trail.
Claims & document processing
Multi-step agents that extract, decision and route, proven on synthetic claims and statements so accuracy is measured, not assumed.
Internal knowledge & operations agents
Retrieval agents wired to synthetic corpora and mocked connectors, so tool permissions and data reach are validated before any live integration.
The headline
See what the agent does before it does it in production.