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Case study

Enterprise RAG and agent knowledge platform

Designed a knowledge platform that turns internal documents, code, and operational knowledge into governed AI workflows, combining RAG, MCP tools, LangGraph orchestration, and LangSmith observability.

  • RAG
  • MCP
  • LangGraph
  • LangSmith

Context

A complex enterprise environment needed AI assistance over internal knowledge without treating a prototype chatbot as the final architecture.

Challenge

Useful information lived across internal portals, documents, and code repositories. Search alone was not enough, but a generic assistant could not be trusted without source grounding, scoped tools, evaluation, and operational visibility.

Constraints

  • multiple internal knowledge sources with different structure and freshness
  • enterprise authentication and access boundaries
  • answers that needed traceable sources rather than plausible summaries
  • agent workflows that had to be observable, testable, and recoverable

Approach

  • Designed ingestion and retrieval as a governed pipeline rather than a one-off vector import.
  • Separated knowledge access into focused MCP tools so agents could search, retrieve, and inspect context through explicit interfaces.
  • Used LangGraph to orchestrate multi-step reasoning and tool use while keeping workflow boundaries visible.
  • Added LangSmith-style tracing and evaluation practices so prompts, retrieval, and agent behaviour could be improved with evidence.

Architecture

A governed knowledge ingestion layer feeds retrieval services and MCP tools, which are orchestrated by LangGraph agents behind an API with tracing, evaluation, and operational observability through LangSmith.

Production RAG and agent workflowSource systemsdocs · data · APIsIngestionnormalise · chunkRetrievalhybrid · filteredModel + toolsMCP · scoped accessGuardrailseval · approvalDependable outputobserved · attributed
Diagram summary: source systems feed an ingestion and chunking pipeline; retrieval and context assembly ground the model, which acts through scoped MCP tools; evaluation and guardrails observe every step before an answer or action reaches the user.

Key decisions

  • RAG treated as a production knowledge system, not a chatbot feature
  • MCP tools as the boundary between agents and enterprise data
  • LangGraph orchestration with LangSmith observability for behaviour that can be inspected and improved

Details are anonymised. Additional context is available under NDA.

Related expertise

AI systems and agentic workflowsSoftware architecture

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