Managed scale and fast integration
Build with the appropriate Gemini and Google Cloud agent services, RAG components, Cloud Run or GKE, IAM, logging and managed operations. Best when cloud connectivity and Google Cloud governance are acceptable.
AI agent development · Google Cloud · on-prem · hybrid
Secure AI agent development on Google Cloud, on-prem and hybrid.
For CTOs, platform leaders and IT operations teams that need an agent to do real work—not another chatbot demo. Cloudpeakify designs, builds and operates production AI agents connected to private knowledge, cloud APIs and operational tools, with controlled access and human approval where it matters.
Grounded data · API and MCP tools · least privilege · approval gates · evaluation · audit logs · managed operations
Google Cloud PartnerSelect tier for Services
15+ yearsEnterprise infrastructure experience
Cloud + datacentreGCP, Azure, VMware and Hyper-V
Operate after buildObservability, runbooks and 24/7 options
Placement before platform
Choose where every part of the agent belongs.
The model, orchestration, private data and operational tools do not have to live in the same place. We choose placement from security, latency, connectivity, sovereignty, cost and operating constraints.
Keep inference and data local
Run the agent, retrieval layer and approved models inside private infrastructure when data residency, offline operation or latency requires it. The stack may use Google Distributed Cloud or a private Kubernetes-based design.
Place data and tools deliberately
Keep sensitive systems on-premises while selected orchestration, models or evaluation run on Google Cloud—or reverse that pattern. Identity, network paths and audit evidence remain explicit across the boundary.
A deployment decision your security and operations teams can review
| Requirement | Likely starting point | Decision to prove |
|---|---|---|
| Fast delivery with existing GCP governance | Google Cloud native | Region, service selection, IAM, cost and production support |
| Strict local data or disconnected operation | On-premises / private | Hardware, model fit, patching, capacity and lifecycle ownership |
| Private systems plus elastic cloud capabilities | Hybrid | Data movement, latency, identity, failure modes and egress economics |
| Agent can change production systems | Any placement with controlled tools | Read, propose, approve and execute boundaries plus rollback |
High-value use cases
Build an agent around a costly workflow, not around AI hype.
We start where internal knowledge, repeated decisions and controlled tool use can remove operational friction without hiding accountability.
Incident and troubleshooting agent
Collect monitoring context, search approved runbooks, propose a diagnosis, draft the incident timeline and escalate with the evidence an engineer needs.
Support and knowledge agent
Ground answers in internal documentation, classify requests, identify missing context and draft safe steps while preserving human ownership of sensitive changes.
Cost and configuration agent
Review cloud inventory, billing or configuration evidence, explain anomalies and propose actions that enter an approval workflow before execution.
Kubernetes and delivery assistant
Connect cluster signals, repositories and deployment runbooks to shorten diagnosis and prepare reviewable remediation or change plans.
VMware and cloud assessment agent
Structure application evidence, dependencies and constraints to support keep, move, modernize or retire decisions without pretending the agent replaces architecture ownership.
Document and compliance assistant
Retrieve from approved sources, cite evidence and route low-confidence or policy-sensitive outputs to a named reviewer.
From architecture to ownership
A production path with a decision at every stage.
You can start with the architecture plan only. Each later stage proceeds when the previous evidence supports it.
AI Agent Architecture Plan
Define one valuable workflow, users, success measure, data and tool map, GCP/on-prem/hybrid placement, security controls, cost assumptions and a pilot backlog.
Grounded pilot
Build the smallest end-to-end agent, connect approved sources, create an evaluation set and demonstrate read, propose and approval behaviour against realistic scenarios.
Production engineering
Implement identity, network and data boundaries; controlled API or MCP tools; CI/CD; logging; monitoring; failure handling; rollout and rollback.
Operate and improve
Assign service ownership, incident response, quality and cost reviews, model and prompt changes, access recertification and a measurable improvement cadence.
Concrete outputs
What your team receives
The deliverables connect architecture, security and operations so the agent can be evaluated as a production system.
Architecture blueprint
Deployment view, trust boundaries, components, data flows and integration decisions.
Tool and access matrix
Named identities and read, propose, approve and execute permissions for APIs and MCP tools.
Evaluation and risk pack
Representative test cases, acceptance thresholds, misuse scenarios and human-review rules.
Production runbook
Monitoring, escalation, fallback, kill switch, recovery, ownership and change process.
Safety model
Autonomy is a permission decision.
A production agent should not jump directly from an answer to an irreversible action. We separate capability into explicit operating levels and promote only the workflows that have enough evidence.
ReadRetrieve approved data with scoped identity and traceable sources.
ProposeDraft a diagnosis, command, ticket or change without executing it.
ApproveRoute sensitive actions to the right human or policy decision.
ExecuteUse a limited tool with logging, validation, failure handling and rollback.
Good fit
You have a real workflow and systems the agent must understand.
- An IT, support, platform or knowledge workflow consumes repeated expert time.
- Private data, APIs or operational tools are part of the outcome.
- GCP, on-prem or hybrid placement must be justified.
- You need production ownership, not only a prototype.
Not a fit
You want an autonomous demo without an owner.
We do not promise a generic agent that replaces engineering judgement, deploy unreviewed write access or invent ROI before a use case and baseline exist.
Need a RAG or data pipeline rather than an agent? Review AI & LLM Pipelines →
AI agent development FAQ
Questions to settle before selecting a model or deployment platform.
Can you build an AI agent on Google Cloud?
Yes. We design and deliver the agent using the appropriate Google Cloud services, grounded data, controlled tools, evaluation, observability and an operating model. The exact service selection follows the use case and constraints.
Can the AI agent run on-premises?
Yes, when privacy, sovereignty, latency or connectivity requirements justify it. The architecture may use Google Distributed Cloud or a private Kubernetes and model stack selected for your environment. We include the hardware, capacity and lifecycle implications in the decision.
Can private data stay on-prem while the agent uses Google Cloud?
Potentially. A hybrid design can expose only approved retrieval or tool interfaces while keeping source systems private. Whether this is acceptable depends on the data path, identity model, latency, policy and threat model.
Do you only build with Gemini?
No. Google Cloud and Gemini are a primary delivery path, but we select models and infrastructure from the workload, data boundary, quality, cost and operating requirements. Private deployments may require a different model stack.
What should we prepare for the first call?
Bring one workflow, its users, current inputs and outputs, systems the agent may read or change, sensitive data constraints and the person who owns the result today.
Primary technical sources
Platform statements on this page are anchored in current Google Cloud documentation and product material. Product availability and architecture fit are confirmed during discovery.
- Google Cloud: agent development tools and Agent Development Kit
- Google Cloud documentation: managed Model Context Protocol servers
- Google Cloud documentation: identities and authentication for MCP
- Google Cloud documentation: Vertex AI RAG Engine
- Google Distributed Cloud: on-premises and edge AI infrastructure
Start with an architecture decision, not a model demo.
Send one workflow and the systems it must reach. We will identify the smallest useful AI Agent Architecture Plan and whether GCP, on-prem or hybrid is the defensible starting point.