LLMs alone aren't production intelligence. You need RAG to ground answers in your data, Context Engineering to make outputs policy- and role-aware, Agentic frameworks (e.g., LangGraph) to plan and act, LLMOps (e.g., LangSmith) to evaluate and observe, and MCP to standardize tool/data access across apps. Together, this is how you get reliable, explainable, and scalable GenAI in the enterprise.
LLMs alone aren't production intelligence. You need RAG to ground answers in your data, Context Engineering to make outputs policy- and role-aware, Agentic frameworks (e.g., LangGraph) to plan and act, LLMOps (e.g., LangSmith) to evaluate and observe, and MCP to standardize tool/data access across apps. Together, this is how you get reliable, explainable, and scalable GenAI in the enterprise.
RAG is the de-facto way to cut hallucinations by injecting live, proprietary knowledge into prompts.
Agentic systems turn one-shot prompts into plans with tools, memory, and multi-step control flow.
MCP (Model Context Protocol) is emerging as the USB-C for AI apps, standardizing how models access tools, data, and prompts.
LangChain/LangGraph/LangSmith/LangServe give you the bricks for building, orchestrating, testing, and serving—all production-grade.
What it is: a pattern that retrieves relevant knowledge (DBs, documents, EHRs, wikis) and injects it into the model's context so outputs cite real data.
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_core.prompts import ChatPromptTemplate
from langchain_community.vectorstores import FAISS
from langchain_text_splitters import RecursiveCharacterTextSplitter
# 1) Ingest & index
docs = ["<your doc 1 text>", "<your doc 2 text>"] # replace with loaders
splits = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=120).split_text("\n\n".join(docs))
vs = FAISS.from_texts(splits, OpenAIEmbeddings())
# 2) Retrieve
retriever = vs.as_retriever(search_kwargs={"k": 4})
# 3) Prompt with grounded context
prompt = ChatPromptTemplate.from_messages([
("system", "Answer using the provided context. If unknown, say 'I don't know'. Cite sources."),
("human", "Question: {question}\n\nContext:\n{context}")
])
def answer(question: str):
ctx_docs = retriever.get_relevant_documents(question)
ctx = "\n\n".join([d.page_content for d in ctx_docs])
return ChatOpenAI(model="gpt-4o", temperature=0).invoke(prompt.format(question=question, context=ctx))
print(answer("What did our Q2 compliance report say about sepsis alerts?"))For "current, specific, and verifiable" knowledge, retrieval is cheaper, faster, and safer than constant re-training. It also reduces hallucinations by grounding answers in retrieved text.
Goal: apply role, policy, structure, and memory so outputs are consistent, compliant, and useful.
(This is where Finarb's consulting-led design shows up: we codify process & compliance into the prompt+tooling layer so outputs match how your org reasons.)
LLMs don't just chat; they can plan—call tools, write code, check results, and iterate. LangGraph gives you a stateful graph to do this reliably (persistence, branching, human-in-the-loop, debugging with LangSmith, and prod-ready deployment).
from langgraph.graph import StateGraph, END
def plan(state): # decide next step(s)
# inspect user goal + context → choose "search" or "sql" or "summarize"
return {"next": "search" if "guidelines" in state["goal"] else "sql"}
def search(state): # call retriever / web / RAG
return {"context": "...retrieved passages...", "next": "respond"}
def sql(state): # call DB tool, return rows
return {"context": "...query results...", "next": "respond"}
def respond(state): # LLM crafts final answer with citations
return {"answer": "...final...", "next": END}
g = StateGraph()
for n in [plan, search, sql, respond]: g.add_node(n.__name__, n)
g.add_edge("plan", "search"); g.add_edge("plan", "sql")
g.add_edge("search", "respond"); g.add_edge("sql", "respond")
agent = g.compile()
print(agent.invoke({"goal": "Summarize latest clinical guidelines for sepsis"}))It's a low-level orchestration framework for long-running, stateful agents with durable execution, HITL, and deep LangSmith integration for debugging/evals—used by teams shipping production agents today.
What is MCP? The Model Context Protocol is an open standard so AI apps/agents can plug into data "resources," callable "tools," and reusable "prompts" through a common interface—think USB-C for AI.
Stop building bespoke connectors for every app. Integrate once; reuse across assistants.
Strong security posture: consent, privacy, and tool-safety are first-class in the spec.
Works with agent frameworks: expose tools/resources via MCP servers; agents (LangGraph, others) consume them.
{
"server": "ehr-mcp",
"features": {
"resources": ["ehr://patients/{id}", "ehr://labs/{id}"],
"tools": ["get_patient_summary", "run_sql", "create_ticket"],
"prompts": ["clinical_audit_template", "phi_redaction_prompt"]
}
}Takeaway: MCP lets your data and tools be first-class citizens that any compliant agent can use safely and consistently.
The observability & evaluation layer—trace every run, build datasets from traces, run evals (LLM-as-judge + human), monitor quality/cost/latency. Self-host options exist for strict data boundaries.
Ship chains/agents as REST APIs (FastAPI/pydantic), with client SDKs. Clean handoff from R&D to prod.
Graph-aware retrieval, hybrid sparse+dense, reranking, provenance tracking.
Long-running agents with robust state, guardrails, and HITL checkpoints (LangGraph direction).
MCP unifying how assistants connect to your systems across vendors.
Eval-driven iteration (LangSmith) becomes table stakes for compliance and ROI.
As a consult-to-operate partner, we don't just wire components—we design the decision system around your business: