Most teams can get an AI agent talking to an internal system in an afternoon. The hard part comes after the demo, when security asks who the agent acts as, what it can touch, and how anyone would know if it went wrong. Model Context Protocol (MCP) gives agents a clean way to call tools, but it does not answer those questions for you. The architecture around the server does.
Treat the MCP Server as an API Product
An MCP server is a thin interface between a model and your systems, and it should be designed with the same discipline as any production API. Keep each tool narrow and single-purpose. A tool called get_invoice_status with a typed schema is far easier to reason about and audit than a general run_query tool that accepts free text. Validate every argument on the server side, because the model’s output is untrusted input, exactly like a form submission from the internet.
Resist the urge to expose everything the backend can do. Start with read-only tools, add write actions one at a time, and require an explicit confirmation step for anything that changes state or moves money. A smaller tool surface is a smaller blast radius, and it makes the review conversation with security much shorter.
Put Identity and OAuth 2.0 in Front of Every Call
The most common failure we see is an agent running with one shared service account that can reach far more than any single user should. A better pattern is to publish the MCP server behind an API gateway such as Azure API Management and require OAuth 2.0 on every request. The gateway validates tokens, enforces scopes, applies rate limits and writes an audit record before the request ever reaches your code.
Where the agent acts on behalf of a person, pass that user’s identity through, so downstream systems apply the same permissions that user already has. Where the agent runs autonomously, give it its own workload identity with least-privilege roles, scoped to the specific tools it needs. Using Microsoft Entra ID for both keeps conditional access, sign-in logs and lifecycle management in one place instead of scattering secrets across configuration files.
Run It on Kubernetes Like Any Other Production Workload
Once the contract is clear, hosting is a familiar problem. Running MCP servers on GKE gives you autoscaling, rolling deployments, health probes and network policy. Package each server as a container, deploy it with Helm, and manage the cluster and supporting resources with Terraform so every environment is reproducible and reviewable.
A few settings matter more than people expect. Use network policies so the server can only reach the systems it is meant to call. Keep secrets in a managed secret store rather than environment variables baked into images. Put a WAF such as Cloud Armor in front of public endpoints, and prefer private connectivity for anything that touches sensitive data. Agents tend to generate bursts of calls, so set sensible autoscaling limits and timeouts to keep one runaway loop from becoming an outage.
Make Every Tool Call Observable
If you cannot answer “what did the agent do, as whom, and why” after the fact, you are not ready for production. Log each tool invocation with the caller identity, the tool name, validated arguments and the outcome. Send those logs to your central platform so they sit alongside the rest of your security telemetry, where Microsoft Sentinel analytics can flag unusual patterns such as a sudden spike in write calls or access outside normal hours.
Add traces and metrics too, so your platform team can see latency, error rates and retry storms. Governance is much easier when the evidence already exists. For regulated environments in healthcare, finance and government, this audit trail is often what turns a pilot into an approved service.
Where to Start
Pick one workflow with clear value and a bounded risk, such as a read-only lookup against an internal knowledge base or ticketing system. Build the MCP server with narrow tools, put it behind your gateway with OAuth 2.0, deploy it with Terraform and Helm, and wire the logs into your SIEM from day one. Then expand deliberately, one tool and one permission at a time.
If you are planning agentic AI or MCP integration and want it designed for governance from the start, CloudevTech can help you architect, implement and operate it. Book a consultation at https://cloudevtech.net/contact/
#MCP #AgenticAI #GKE #ZeroTrust #Terraform