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Tier 2 · Delivery controls and private MCP

Private AI agents and Model Context Protocol (MCP) engineering

Schema-restricted MCP servers and deployment copilots for infrastructure teams

We connect AI workflows to GitLab, OpenShift and telecom systems through the Model Context Protocol. Copilots prepare manifests, run deterministic readiness checks and assemble incident evidence within an approved data boundary; people retain production change authority.

Request an MCP architecture reviewSee retainer and SLA pricing

2-week Assessment Sprint, $14,500 fixed · 100% credited toward an annual retainer or managed agreement signed within 30 days

Schema-Pinned MCP & Deterministic GitOps Execution PipelineReference architecture demonstrating how AI agents query read-only telemetry via JSON-Schema-pinned MCP servers and submit signed merge requests to GitLab CI and Argo CD.Open full-size video

Where enterprise AI initiatives break

AI initiatives stall when assistants cannot inspect trusted internal state or improvised LLM scripts can invoke commands without reliable permissions. Unreviewed prompts, excessive tool access and unapproved model endpoints create data and change-control risks that a conversational interface alone cannot resolve.

What we engineer

Custom Model Context Protocol Servers

Expose GitLab repositories, Argo CD APIs, Kubernetes/OpenShift state, Prometheus and approved telecom inventory through typed, schema-validated tools. Validate authorization, inputs and structured results, and handle timeouts, duplicate requests and source-system errors explicitly.

Pre-Flight & Manifest Copilots

Draft Helm/Kustomize changes for microservices or CAMARA/network API packaging. Validate applicable restricted-v2 requirements, quotas and cluster policy, bind findings to the reviewed revision and open a merge request containing readiness and change-impact reports for human review.

Private Model & Network Architecture

Use self-hosted models on OpenShift AI/vLLM where data must remain strictly inside your estate. Configure scoped endpoint policies, dedicated service accounts, and approved model access. Document the actual processing boundary, egress routes and retention settings.

Deterministic Guardrails & SIEM Auditability

Keep production integrations read-scoped and enforce mutation approval in execution systems. Add least-privilege RBAC, tool allowlists and usage limits. Export redacted tool-call metadata, authorization decisions and evaluation results to SIEM; retain prompt or response content only under an approved data policy.

Reference stack and standards

Protocols & Frameworks
Model Context Protocol specification, TypeScript/Python MCP SDKs, JSON Schema tool contracts and OpenAPI adapters. HTTP MCP authorization follows the applicable OAuth 2.1 security profile.
Private LLM Runtimes
Red Hat OpenShift AI/vLLM and enterprise self-hosted model inference gateways inside your security perimeter.
Target Integrations
GitLab, Argo CD, Kubernetes/OpenShift, Prometheus/Alertmanager, Jira/ServiceNow and authorized CAMARA/OpenAPI interfaces; mTLS may provide additional transport isolation.

Model outputs are probabilistic; deterministic validation and authorization gates constrain their use. We verify endpoint/model availability and the data boundary before implementation, redact secrets and subscriber data from logs, and test that the production tool identity cannot mutate state.

Reference architecture

T2Delivery controls and private MCP

AI prepares a reviewable change. Deterministic CI checks, protected approvals and runtime admission govern production delivery.

Merge request & bounded MCP tools

  1. GitLab merge requestEngineer commits · Helm / Kustomize / application code
    Webhook / requested review
  2. Pre-flight copilotDraft manifests · readiness report · change diff
    Validated tool calls
  3. Schema-restricted MCP serverTyped tools · server-enforced read-scoped identities

Approved execution boundary

  1. Private model endpointSelf-hosted OpenShift AI / vLLM inside perimeter
  2. Read-only cluster inspectionQuotas · SCCs · current topology; no production mutations
  3. HashiCorp Vault / External SecretsExternal Secrets Operator · controlled runtime access

Deterministic delivery gates

  1. GitLab CI security stageTrivy / Grype · Syft SBOM · Cosign signing
    Build & scan artifacts
  2. Policy validationOPA / Kyverno · SCC compatibility · quotas
    Pass configured checks
  3. Protected human approvalRequired reviewers · branch protections · approved merge

GitOps runtime & evidence

  1. Argo CDApplicationSets · approved desired state
    Reconcile after approval
  2. OpenShift productionAdmission controls · workload RBAC · configured mTLS
    Runtime events & evidence
  3. SIEM & evidence storeRedacted events · attestations · retention policy
Connections across boundaries
Pre-flight copilot / GitLab merge request
Copilot attaches the manifest diff and readiness report for human review.
Pre-flight copilot / Private model endpoint
Model traffic uses an approved private endpoint and processing region.
Schema-restricted MCP server / Read-only cluster inspection
Read-only tool identity can inspect state; it cannot deploy to production.
GitLab merge request / GitLab CI security stage
The merge request starts CI; AI output does not bypass the gates.
Protected human approval / Argo CD
Only the approved merge changes the GitOps desired state.
HashiCorp Vault / External Secrets / OpenShift production
ESO retrieves authorized runtime secrets.
Schema-restricted MCP server / SIEM & evidence store
Redacted tool events are collected under the approved audit policy.
Argo CD / SIEM & evidence store
Sync receipts join build attestations in the evidence store.

MCP servers enforce strict JSON Schema validation and read-only cluster/OSS credentials. Every state change must pass GitLab CI security stages, OPA/Kyverno admission policy and human CODEOWNERS sign-off before Argo CD sync.

Download Mermaid source (.mmd)

Deliverables in your repository

You keep the delivered code, manifests, diagrams, runbooks and validation evidence in your repositories. We agree the systems, acceptance criteria and access prerequisites in the SOW.

  1. Production MCP Server Codebase

    Containerized non-root servers, typed tool contracts, appropriate OAuth authorization, transport controls and read-scoped backend identities.

  2. Pre-Flight Deployment Copilot Workflow

    A versioned agent workflow that drafts manifests, runs SCC/policy checks and attaches revision-bound readiness reports to merge requests.

  3. AI Security & Governance Architecture

    Network isolation, endpoint policies, token/usage limits, evaluation cases and redacted SIEM logging with retention and incident procedures.

  4. Team Enablement & Extension Templates

    Documentation, tool templates and tests for engineers to add integrations while preserving schemas, permissions and approval boundaries.

  5. Private Model Endpoint Integration

    Self-hosted OpenShift AI or vLLM endpoints with egress controls, so prompts, manifests and telemetry stay inside your perimeter.

  6. Network API Pre-Flight Checks

    Typed checks for CAMARA OpenAPI contracts, OAuth2 consent scopes and NEF/SCEF adapter payloads, run before a change reaches review.

Inside the 2-week Assessment Sprint

What the $14,500 sprint covers for this service. Access stays read-only throughout, and you keep the output whether or not you continue.

  1. Days 1–3

    Review and data-boundary inventory

    Read-only review of how merge requests are checked, which readiness evidence is assembled by hand and which model endpoints are allowed inside your perimeter.

  2. Days 4–6

    Tool and permission risk map

    Which checks can run as typed, read-scoped MCP tools, which must stay with a person, and where current assistants can reach data or credentials they should not.

  3. Days 7–9

    Bounded MCP design

    Architecture in Mermaid (.mmd) source for the MCP servers, private model endpoints, the pre-flight report and the approval path before Argo CD reconciles.

  4. Day 10

    Read-out and backlog

    Tool catalog draft, governance controls and a fixed-scope backlog.

Engineering scope

We migrate and support telecom management systems and hybrid infrastructure, automate how workloads are validated and shipped, and build the portals and network API integrations that expose those platforms to your teams and customers.

AI Agents & MCP Integration

Automation that needs write access nobody will grant stays manual. The way through is a bounded workflow with read-scoped tools and a person approving each consequential action.

Best for

An agent needs evidence it can cite, a defined set of actions and a reliable route for exceptions. We begin with one workflow and measure its behavior against representative, incomplete and adversarial inputs.

Bring to the review

One workflow, its source systems, current handling time and the actions that need approval.

Implementation

Define the tool schemas, source-system permissions and allowed data. Connect the workflow, validate structured results and enforce approvals in the systems that execute consequential actions.

Prerequisites

One approved workflow with its systems of record, the read-only data sources the agent may use, a private model endpoint or an agreed hosting decision, your repository and CI gates, and the people who approve each proposed change.

Operational support

Trace failed runs, unexpected outputs and connector changes using execution records and evaluation cases. Test timeouts, duplicate events, usage limits and the manual fallback before extending the workflow.

Acceptance evidence

Tool and access matrix, evaluation dataset and results, approval tests, execution records and fallback runbook.

  • Representative, adversarial and incomplete-input cases evaluated.
  • Approval, authorization and duplicate-event handling demonstrated.
  • Manual fallback, audit records and usage limits validated.
Responsibilities & dependencies

Include the process and source-system owners. Agree permitted actions, data access, review thresholds and cost limits, including any model or connector charges.

Support coverage

Normal support:
Monday–Friday, 8 a.m.–6 p.m. Eastern time (America/New_York), excluding public holidays. Supported systems, access and responsibilities are agreed in writing.
24/7 emergency on-call:
Part of managed operations. For critical customer outages under that coverage, reported through the agreed channel, we aim to investigate, provide substantive engineering updates and attempt resolution within four elapsed hours of emergency receipt, on a best-effort basis.
Resolution:
Resolution within that window is not guaranteed. Other cases follow your SLA.
Licenses and entitlements:
Vendor licenses and support entitlements are separate from our services.
Request an architecture reviewCompare engagement optionsSee sample deliverables

Planning considerations

  1. Business trigger
  2. Review and approval
  3. Controlled action

Explore a sample service scope. We agree your interfaces, permissions and acceptance criteria before delivery; use the engagement design for deployment.

Rules for predictable decisions
Map the workflow and record its manual baseline and acceptance criteria. Deterministic rules can handle validation and routing; evaluate where AI assistance adds value before giving it access to more actions.
AI assistance for interpretation
Use a model where interpretation adds value: extracting fields, classifying a request or proposing a draft. Check outputs against allowed values and supporting records. Uncertain or incomplete results go to a named reviewer.
Bounded autonomy, when justified
Allow a system to select and execute actions only within an explicitly approved scope. Enforce permissions in the connected systems, require approval for consequential changes, and set time, retry and cost limits. Stop and route exceptions to a person.
Secure the integration boundary
For API or Model Context Protocol (MCP) connections, define authentication, server-side authorization, data controls and audit requirements. Test the controls and treat retrieved text and tool output as untrusted input.
Example: service-request intake

Use this illustrative design to agree permitted actions and approval thresholds for your process.

  1. Receive a request and validate required fields with rules.
  2. AI proposes a category and summary using approved records.
  3. A reviewer approves the proposed action; missing or uncertain information enters the exception queue.
  4. An authorized integration performs the approved action and records the result. Failed or duplicate events use bounded retries or manual handling.

A closer look at the deliverables

Use our sample checklists to scope work and review deliverables. Adapt them to your environment. They are illustrative, not customer results, vendor-certified procedures or executable production instructions.

Illustrative expected result

Automation acceptance checklist

AI workflow evaluation report

Prerequisites
Describe the process, approved data sources and permitted actions. Capture baseline cycle time, error rate and human review effort; document permissions and approval boundaries.
Expected result / acceptance threshold
Evaluation outcomes compared with the baseline; approvals, exception routing and manual fallback demonstrated.
Open the full worksheet

Technical decisions and runbooks

Discuss this service with an engineer

Share your architecture and a principal engineer will review it and reply within one business day.

Sign an Annual Platform Retainer or an annual Managed Orchestration agreement within 30 days of sprint completion and the full assessment fee is credited on your first annual invoice. Allocation, coverage and acceptance criteria are agreed in writing.

Request an architecture reviewSee pricing and coverage