A \ V — MCP & Agentic Architecture

MCP Server Deep-Dives

Production MCP server integrations across every major cloud and enterprise platform — each exposing Claude to real infrastructure via the Model Context Protocol.

☁️

AWS MCP Server

PRODUCTION

Full AWS service orchestration via Claude — EC2, S3, Lambda, Bedrock, SageMaker, and CloudFormation exposed as MCP tools. Enables conversational infrastructure management, automated cost analysis, and agentic deployment pipelines.

list_ec2_instances invoke_lambda s3_read_write bedrock_invoke cloudformation_deploy cost_explorer sagemaker_endpoints

40+

Tools

Services

IAM

Auth

🌐

GCP / Vertex AI MCP

PRODUCTION

Google Cloud Platform integration with deep Vertex AI tooling — Gemini model access, BigQuery analytics, Cloud Run deployment, and Pub/Sub event streaming. Designed for GCP-native agentic workflows and multi-model routing.

vertex_predict bigquery_query cloud_run_deploy gcs_operations pubsub_publish gemini_invoke

35+

Tools

Services

OAuth2

Auth

💎

Azure OpenAI MCP

PRODUCTION

Azure-native MCP server bridging Claude to Azure OpenAI deployments, Cognitive Services, Azure AI Search, and enterprise data via Azure Data Factory. Built for regulated enterprise environments with full RBAC and audit logging.

aoai_chat_complete ai_search_query cognitive_analyze blob_storage adf_pipeline_run monitor_metrics

32+

Tools

Services

Entra

Auth

⚙️

ServiceNow MCP

ENTERPRISE

Full ServiceNow ITSM integration — incident lifecycle management, change request automation, CMDB queries, and knowledge base operations exposed to Claude. Enables conversational IT operations and autonomous incident triage workflows.

create_incident update_incident query_cmdb create_change search_kb list_assets resolve_incident

28+

Tools

ITSM

Domain

OAuth

Auth

Agentic Architecture Patterns

Six foundational patterns deployed in production — each designed for reliability, observability, and graceful failure at enterprise scale.

Pattern 01

Orchestrator–Subagent

A central orchestrator LLM decomposes goals and delegates to specialized subagents via MCP tool calls. Each subagent has a narrow scope — search, compute, write, verify. Results are synthesized by the orchestrator before returning to the user.

LangGraph Claude Opus Tool routing

Pattern 02

RAG + Agentic Retrieval

Beyond static vector retrieval — the agent decides when to retrieve, what to retrieve, and how many times. Iterative retrieval loops with query rewriting, re-ranking, and source fusion before final generation.

LlamaIndex pgvector HuggingFace

Pattern 03

Multi-LLM Router

A routing layer directs requests to the optimal model based on task type, latency budget, cost ceiling, and output quality requirements. Claude for reasoning, GPT-4o for multimodal, Llama for on-prem sensitive workloads.

LiteLLM Claude + GPT-4o Cost routing

Pattern 04

Human-in-the-Loop Agentic

Async agent workflows with explicit approval gates — the agent pauses, summarises its plan, awaits human confirmation, then continues. Designed for high-stakes operations: deployments, financial actions, data mutations.

Prefect Slack MCP Approval flows

Pattern 05

Reflection & Self-Correction

An agent critiques its own output before returning it — a second LLM pass checks for accuracy, completeness, and policy compliance. Failed checks trigger targeted reruns rather than full restarts.

Constitutional AI Critic LLM AutoGen

Pattern 06

Event-Driven Agentic

Agents triggered by real-time events — Pub/Sub messages, webhook payloads, monitoring alerts, or scheduled jobs. Stateless execution with full audit trails. Zero idle cost, elastic scale.

Cloud Run Pub/Sub Airflow

Real Project Case Studies

Production deployments across healthcare, enterprise IT, and financial services — click any row to expand.

2024 — Present

Enterprise AI Platform — Multi-cloud LLM Orchestration

Healthcare · 12 Business Units · AWS + Azure

40% cost reduction ↓

Designed and deployed a unified AI gateway serving 12 enterprise business units — routing requests across Claude, GPT-4o, and Azure OpenAI based on task type, cost budget, and data residency requirements. Implemented FinOps dashboards, per-team cost attribution, and automated model fallback chains. Achieved 40% reduction in LLM spend vs. single-provider approach while improving P95 latency by 35%.

Claude Sonnet 4GPT-4oLiteLLM AWS BedrockAzure OpenAILangGraph PrometheusGrafanaKubernetes

2024

Agentic ITSM — Autonomous Incident Response

Enterprise IT · ServiceNow · Claude MCP

60% MTTR improvement ↑

Built an autonomous incident triage agent using Claude + ServiceNow MCP. The agent receives PagerDuty alerts, queries the CMDB for affected services, retrieves runbooks from the knowledge base, and generates a recommended resolution plan — all within 90 seconds of alert firing. Human engineers approve or override before execution. Reduced mean time to resolution by 60% across Tier-1 incidents.

Claude Opus 4ServiceNow MCPPagerDuty LangGraphSlack MCPpgvector RAGHITL Approval

2023 — 2024

Private LLM Platform — Air-gapped Financial Services

FinServ · Red Hat OpenShift AI · On-premises

100% data residency ✓

Architected a fully air-gapped LLM platform for a regulated financial institution — zero data leaving the private network. Fine-tuned Llama 3.1 70B with LoRA on proprietary financial corpora, deployed via vLLM on OpenShift AI with GPU autoscaling. Full MLOps lifecycle including model versioning, drift detection, and automated retraining pipelines. Passed regulatory audit with full data lineage documentation.

Llama 3.1 70BLoRA / PEFTvLLM Red Hat OpenShift AIHuggingFaceMLflow GPU AutoscalingDrift Detection

2023

Multi-Agent Research Pipeline — GCP + Vertex AI

Life Sciences · BigQuery · Gemini + Claude

20× research throughput ↑

Designed a multi-agent pipeline for clinical research acceleration — a planning agent decomposes research questions into subtasks, specialist agents query PubMed, internal trial databases, and BigQuery genomics datasets, then a synthesis agent produces structured evidence summaries. Deployed on Cloud Run with Pub/Sub event triggering. Increased research throughput by 20× vs. manual literature review.

Gemini 2.0 ProClaude SonnetGCP Vertex AI BigQueryCloud RunPub/Sub LangGraphLlamaIndex