RAG + MCP + AWS
Elevata designs AI systems that combine knowledge retrieval, Model Context Protocol (MCP), tool context, and AWS services to deliver auditable answers and actions.
Architecture decisions
Data boundary
Retrieved, embedded, and logged data
Which documents, records, prompts, embeddings, logs, traces, and evaluation data can be used by the AI workflow, and where are they allowed to live?
Retrieval design
Knowledge Bases, vector stores, search, and ranking
Will the system use Bedrock Knowledge Bases, a custom vector store, search APIs, or a hybrid approach? How will permissions, freshness, citations, and source ranking be handled?
Tool boundary
Explicit, limited, and reversible actions
Which actions are read-only, which require approval, and which should never be delegated to an agent? MCP should make tool access explicit, limited, logged, and reversible.
Production controls
Quality, cost, latency, and auditability
What will be measured before launch: answer quality, retrieval accuracy, tool-call accuracy, cost per answer, latency, safe refusal, auditability, and rollback?
Canadian Region and privacy review
Region and privacy by workflow
Decide whether prompts, retrieved context, embeddings, logs, traces, backups, and evaluation data need to stay in a Canadian AWS Region, and whether cross-Region inference is acceptable for the workflow.
RAG
RAG retrieves documents, policies, data, or search results to ground model answers. In production, the challenge is permissioning, freshness, chunking, evaluation, traceability, and cost.
MCP
Model Context Protocol (MCP) helps agents and applications communicate with tools and data sources in a standardized way. On AWS, it needs to integrate with IAM, network, logs, secrets, limits, and observability.
Practical architecture
RAG and MCP solve different problems. The architecture should decide when to answer, when to retrieve context, and when to act.
Reference architecture
Scope
We map sources, freshness, access filters, and traceability by user or role.
We define chunking, embeddings, retrieval, quality tests, and workflow-level metrics.
We connect tools with limits, authentication, logs, and approval to reduce the risk of wrong actions.
We create observability, budgets, fallback, alerts, and playbooks for production operations.
RAG
answers grounded in controlled knowledge
MCP
tool context
AWS
security and operations
Related proof
Assess requirements, IAM, models, networking, and rollout before releasing Claude Code to engineering.
RAG, agents, and Region decisions for Canadian teams.
Bedrock architecture with attention to LGPD, logs, and the São Paulo Region.
Control model, prompt, and RAG cost on Bedrock.
Assess Claude, RAG, privacy, and cross-Region inference profiles (CRIS) for Canadian workloads.
About Elevata
Elevata implements RAG, MCP, and agents with controls for permissioning, evaluation, traceability, cost, and operations. The architecture is designed to answer better and act with clear limits when actions are needed.
More about usFrequently asked questions
RAG retrieves knowledge to ground answers. Model Context Protocol (MCP) standardizes how applications and agents access tools and context sources. They are complementary in AI systems that need to answer and act.
Yes, depending on chosen services, Region requirements, and availability. The design should assess data, logs, network, authentication, and integrations before production.
Use agents when AI needs to execute steps, query tools, call APIs, or follow workflows. For informational answers only, simple RAG may be enough.
References
Note: AWS service availability, model availability, pricing, program terms, and regional support can change. Validate current AWS documentation before making production architecture decisions.
Next step
Share data sources, tools, users, and Region requirements. We will respond with an initial architecture for review.