Users, workloads and agent identities
Separate human, workload and agent principals. Map the permissions required at the model, repository, MCP, API, data and production-system boundaries.
Enterprise Agentic AI Infrastructure · GCP · on-prem · hybrid
Enterprise infrastructure for AI agents that must run safely in production.
For CTOs, CIOs and platform leaders moving from isolated pilots to an operated agent platform. Cloudpeakify designs the runtime, identity, MCP and API access, private model capacity, RAG data paths, observability and service ownership behind production agents—while keeping Google Cloud and VMware-to-GCP delivery at the core.
Vertex AI and Gemini · Claude Code · Codex · MCP · Ollama · GKE · Terraform · audit · managed operations
Google Cloud PartnerSelect tier for Services
15+ yearsEnterprise infrastructure experience
Cloud + datacentreGCP, Azure, VMware and Hyper-V
Operate after buildRunbooks, incident ownership and 24/7 options
Engagement focus: Germany, Austria, Switzerland, Czech Republic and the United Kingdom.
The missing production layer
Your agents share infrastructure risk even when they use different models.
A working agent does not create a governable platform. As Claude Code, Codex, Gemini agents, MCP servers and private model runtimes spread across teams, the organisation needs one explicit model for identity, network paths, data access, approvals, telemetry, cost and support.
Separate search and buying intent: use AI Agent Development when one workflow still needs to be built. Use this service when several agents, developer tools or model runtimes need a shared enterprise platform and operating model.
MCP and API access
Inventory servers and tools, distinguish read from change actions, place approval gates, protect credentials and retain evidence for each consequential call.
Managed and private inference
Place Gemini, approved third-party models or self-hosted Ollama runtimes according to quality, sovereignty, latency, capacity, lifecycle and cost constraints.
RAG and private knowledge
Define source ownership, retrieval paths, document permissions, indexing, citations, freshness and data-loss boundaries before internal knowledge reaches an agent.
Agent Engine, Cloud Run, GKE or private Kubernetes
Select a runtime from workload behaviour and operating requirements, then standardise infrastructure as code, delivery, secrets, rollback and recovery.
Observability, audit and service ownership
Collect traces, tool outcomes, latency, errors, quality and cost signals. Connect alerts to named owners, incident paths, runbooks and change review.
Placement before product selection
Choose the platform boundary that your organisation can defend and operate.
Models, agents, data and tools do not need to live in one location. We document why each component belongs on Google Cloud, inside the datacentre or across a controlled hybrid boundary.
| Starting point | Good fit | Infrastructure decisions | Operating obligation |
|---|---|---|---|
| Google Cloud managed | Teams using Gemini and Google Cloud governance that want managed agent runtimes and cloud integration. | Agent Engine or other Gemini Enterprise Agent Platform services, Cloud Run/GKE, IAM, networking, MCP, RAG and observability. | Service ownership, evaluation, access review, cost control, incident handling and controlled release. |
| Hybrid GCP + on-prem | Private systems or data must remain local while selected agent, model or evaluation capabilities use Google Cloud. | Identity federation, private connectivity, egress, latency, failure modes, tool gateways, audit flow and recovery. | End-to-end monitoring and an owner for both sides of the boundary—not separate blind spots. |
| Private / Ollama | Local-only inference, controlled hardware or disconnected operation is justified by a real requirement. | Model fit, GPU/CPU capacity, container or Kubernetes runtime, network exposure, access layer, storage and update process. | Capacity, patching, model lifecycle, queue behaviour, monitoring, backup and support remain your responsibility unless transferred. |
Enterprise developer-agent environments
Standardise the environment around Claude Code, Codex and Gemini—not only the licence.
Each product has its own administration model. We implement the shared engineering controls around repositories, local and hosted runtimes, identity, network access, tool permissions, secrets, approvals and evidence, then map vendor-specific settings to that target state.
Gemini and Vertex AI agent platform
Design GCP projects, IAM, networking, Agent Engine or GKE/Cloud Run runtimes, managed MCP access, RAG services, logging and evaluation around the selected production use case.
- Google Cloud identity and least-privilege tool access.
- Terraform, CI/CD, observability and environment separation.
- Hybrid access to approved private systems where required.
Claude Code and Codex rollout boundaries
Separate workspace entitlement from local runtime, repository, cloud environment and connected-system permissions. Standardise repository guidance, allowed operations, proxy or gateway paths and administration ownership.
- Representative-user rollout and policy validation.
- Repository rules, reusable workflows and approval boundaries.
- Audit, analytics and compliance integration where supported.
Self-hosted Ollama infrastructure
Use Ollama when a validated model and local deployment meet the workload. We add the production boundary around its local API rather than treating a model server as a complete enterprise platform.
- Local-only or controlled network exposure and proxy design.
- Hardware sizing, model storage, concurrency and capacity testing.
- Monitoring, patching, model promotion and rollback ownership.
Concrete first engagement
Agentic AI Infrastructure Assessment
A technical assessment for leadership teams that need a defensible target architecture and implementation decision—not a generic AI strategy presentation.
Agent estate and risk register
Current agents, models, developer tools, MCP servers, data sources, identities, runtime locations, owners and the highest-impact production gaps.
Target platform blueprint
GCP, on-prem and hybrid placement; network and identity boundaries; runtimes; RAG paths; tool gateways; observability and recovery dependencies.
Permission and approval matrix
Read, propose, approve and execute capabilities mapped to named users, workloads, agent identities, tools and production systems.
Operations readiness scorecard
Logging, traces, evaluation, SLO signals, incident response, kill switch, rollback, access review, cost ownership and support coverage.
Prioritised implementation backlog
Sequenced platform work, quick risk reductions, dependencies, acceptance evidence and the smallest production workload for validation.
Build, harden, migrate or stop
A clear recommendation for the next paid step—or a reason not to expand the platform until a prerequisite is resolved.
From assessment to operations
One accountable path from platform decision to production ownership.
Assess and place
Inventory the estate, select the first production workload and decide what belongs on GCP, on-prem or behind a hybrid boundary.
Build the platform baseline
Implement identity, network, runtime, repositories, IaC, secrets, MCP/API gateways, RAG paths, observability and release controls.
Onboard one real workflow
Validate the platform against production data, tools, permissions, failure modes, quality evidence and rollback—not against a synthetic demo.
Transfer or operate
Hand over documented ownership to your team or scope managed operations, incident paths, platform change and optional 24/7 coverage with Cloudpeakify.
Use the right entry point
This platform offer connects the existing Cloudpeakify AI and cloud services.
AI Agent Development
Choose this when one useful agent still needs architecture, data grounding, tools, evaluation and production delivery.
AI Agent Security & MCP Review
Choose this when agents already exist but tool permissions, approvals, threat modelling or incident controls are the urgent gap.
AI & LLM Pipelines
Choose this when RAG, internal data preparation, evaluation or a Vertex AI pipeline is the primary delivery problem.
Managed Cloud Operations
Transfer monitoring, incidents, patching, runbooks, service desk and reporting after the agent platform boundary is defined.
VMware to Google Cloud
Keep agent-platform decisions aligned with the wider migration, landing-zone, connectivity and operating-model programme.
Terraform and Kubernetes platform engineering
Standardise GKE, private Kubernetes, CI/CD, policy and developer workflows that the agent platform depends on.
Enterprise Agentic AI Infrastructure FAQ
Questions to close before several AI tools become an unsupported production estate.
How is this different from AI agent development?
Agent development delivers a specific workflow. This engagement creates the shared runtime, identity, network, MCP, data, observability and operations model used across agents, developer tools and teams.
Can the platform support Gemini, Claude Code, Codex and private models?
Yes, where the selected product editions support the required controls. The vendors keep separate entitlement and administration models; we map them to common enterprise boundaries for repositories, identity, tools, network access, evidence and support rather than pretending one setting governs everything.
Can we run Ollama entirely on-premises?
Yes. Ollama supports local operation and local-only mode, but the model server is only one component. A production design still needs validated models, hardware capacity, network and access controls, monitoring, model lifecycle, patching, backup and accountable operations.
How do you prevent an MCP tool from receiving excessive access?
We use a separate workload or agent identity where the platform supports it, grant the minimum underlying resource permissions, restrict tools and read/write behaviour, add human approval for consequential actions and retain logs that attribute each request.
Can Cloudpeakify provide 24/7 operations?
Optional 24/7 coverage can be scoped after runtime boundaries, dependencies, monitoring, escalation, severity and change ownership are agreed. We do not attach an undefined support promise to an undefined agent platform.
What should we bring to the first assessment call?
Bring the current agents and developer tools, model providers, repositories, data sources, MCP/API integrations, production systems they can reach, deployment locations, security constraints and the people who own platform and incident decisions.
Primary technical sources
Product statements are anchored in current first-party documentation. Availability, edition requirements and architectural fit are revalidated during assessment because these platforms change quickly.
- Google Cloud Architecture Center: multi-agent AI system runtimes and components
- Google Cloud: MCP authentication and agent or workload identities
- Google Cloud: control MCP tool use with IAM
- Google Cloud: observability for agents and MCP servers
- Anthropic: Claude Code enterprise deployment options
- Anthropic: Claude Code corporate proxy configuration
- OpenAI: Codex enterprise admin rollout boundaries
- OpenAI: Codex roles, local runtime and connected-system permissions
- Ollama: local-only operation, network exposure and capacity configuration
- Ollama: container and GPU deployment options
Make the platform decision before agent sprawl becomes an operations problem.
Send your current agent tools, deployment locations and the production systems they can reach. We will recommend the smallest defensible scope for an Agentic AI Infrastructure Assessment.