Deep Dive 2026

Agentic AI,
RAG & MCPs

The Trinity of Modern Intelligent Systems

Agentic AI Retrieval-Augmented Generation Model Context Protocol
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Agenda

Part I

Agentic AI
Foundations, architecture, multi-agent systems, tool use, planning, memory, safety

Part II

Retrieval-Augmented Generation
Chunking, embedding, vector search, hybrid retrieval, evaluation, advanced RAG patterns

Part III

Model Context Protocol
Protocol design, transport, tools/resources/prompts, security, ecosystem, real-world use

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Part I

Agentic AI

From passive chatbots to autonomous agents

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What is Agentic AI?

An agentic AI system perceives its environment, reasons about goals, and takes autonomous actions to achieve them — without step-by-step human instruction.

  • Autonomy — acts independently within bounds
  • Goal-oriented — optimizes toward objectives
  • Tool use — calls APIs, queries databases, runs code
  • Memory — retains context across interactions
  • Self-reflection — evaluates and corrects own output
🤖

Agent Loop

Perceive -> Reason -> Act
^_________|
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The LLM at the Core

Reasoning Engine

LLMs provide chain-of-thought, step-by-step planning, and the ability to decompose complex tasks into sub-tasks.

Natural Language Interface

Humans interact in plain language. The agent translates intent into structured actions.

Code Generation

Many agents write and execute Python/shell code as a primary tool for computation and data manipulation.

Self-Correction

Agents critique their own outputs, retry on failure, and learn from mistakes within a session.

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Agent Architecture

+-----------+ +----------+ +----------+
| Perception| --> | Brain | --> | Action |
| (Input) | | (LLM) | | (Tools) |
+-----------+ +----------+ +----------+
^ | |
| v |
| +-----------+ |
+------ | Memory | <----------+
| (Short+Long)|
+-----------+

Perception

User messages, sensor data, file contents, web pages, API responses

Reasoning

LLM call with system prompt, tools schema, conversation history, planning instructions

Execution

Function calls, code execution, API invocations, file writes, UI actions

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Tool Use & Function Calling

Agents declare tools as structured JSON schemas. The LLM selects which tool to call and with what arguments.

Tool Schema Example

{ "name": "search_web", "description": "Search the internet", "parameters": { "query": { "type": "string" } } }

Common Tool Categories

  • Web search & browsing
  • Code execution (Python/shell)
  • File system read/write
  • Database queries
  • API integrations (Slack, email, etc.)
  • Image generation / analysis
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Planning & Decomposition

Complex tasks are broken into manageable sub-tasks. Two dominant approaches:

ReAct (Reason + Act)

Interleaves reasoning traces with actions. Each step: Thought -> Action -> Observation.

Thought: I need to find Q3 revenue.
Action: search("Q3 2025 revenue report")
Observation: Found report at example.com

Plan-ahead (Tree/Graph)

LLM generates a multi-step plan upfront, then executes. Supports backtracking and replanning on failure.

Plan: [1. Search docs, 2. Extract values,
3. Compute total, 4. Format output]
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Memory Systems

Short-term (Context)

Conversation history within the LLM context window. Manages token budgets via sliding window or summarization.

Episodic Memory

Stores past interactions, outcomes, and reflections. Retrieved via vector similarity when relevant.

Procedural Memory

Learned skills, reusable patterns, and cached tool outputs. Persists across sessions.

User Input -> [Short-term Context Window] <-> [Episodic Vector Store] <-> [Procedural Cache]
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Multi-Agent Systems

Multiple specialized agents collaborate, debate, and delegate to solve complex tasks.

Architecture Patterns

  • Orchestrator — Central agent delegates to workers
  • Debate — Agents argue positions, synthesizing better answers
  • Pipeline — Sequential handoff between specialists
  • Swarm — Emergent behavior from simple agent rules

Example: Research Agent

Planner -> [Searcher, Scraper,
Analyst, Writer] -> Reviewer

Each agent specializes. The orchestrator coordinates, reviews quality, and handles failures.

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Agent-to-Agent Communication

Agents need structured protocols to coordinate, share context, and resolve conflicts.

Message Passing

Agents exchange typed messages (e.g., JSON) containing intent, data, and results. Supports pub/sub or direct routing.

Shared Context / Blackboard

A common data store where agents read and write intermediate results. Useful for parallel execution.

Tool-Based Handoff

One agent calls another agent as if it were a tool, passing control and receiving results synchronously.

Emerging Standards

Protocols like A2A (Agent-to-Agent) are emerging to standardize inter-agent contracts.

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Agent Frameworks (2026)

LangGraph

Graph-based state machine for agents. Nodes = steps, edges = transitions. Built-in persistence, streaming, human-in-the-loop.

CrewAI

Multi-agent orchestration with role-based agents, task delegation, and built-in tooling. Python-native, simple API.

AutoGen (Microsoft)

Multi-agent conversations with flexible agent roles, code execution, and group chat patterns.

OpenAI Agents SDK

Lightweight SDK for single and multi-agent apps with guardrails, handoffs, and tracing.

Semantic Kernel

Microsoft's AI orchestration SDK. Planners, memory, connectors. Deep Azure integration.

Dify / Coze

Visual agent builders with drag-and-drop workflows, tool marketplace, and no-code agent creation.

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Safety & Guardrails

Autonomous action introduces risk. Every agent system needs layered safety mechanisms.

Input Guardrails

  • Prompt injection detection
  • Topic/domain restrictions
  • Jailbreak resistance
  • PII redaction

Output Guardrails

  • Content moderation
  • Tool-call validation
  • Human-in-the-loop approval
  • Rate limiting & budgets

Runtime Safety

  • Sandboxed code execution
  • Maximum step limits
  • Idempotency keys
  • Audit logging

Observability

  • Full trace capture
  • Cost tracking per-step
  • Failure mode analysis
  • Behavioral monitoring
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Agentic AI — Key Challenges

Hallucination & Reliability

Agents confidently take wrong actions. Mitigations: verification loops, tool-grounding, confidence thresholds.

Context Window Limits

Long-running agents exceed context budgets. Solutions: summarization, sliding windows, external memory.

Error Recovery

Agents must detect failures, retry intelligently, and know when to escalate to a human.

Cost & Latency

Multi-step reasoning is expensive. Caching, model selection, and parallel execution help manage costs.

Evaluation

How do you grade an autonomous agent? Complex trajectory evaluation, outcome scoring, and simulation-based testing.

Security

Tool misuse, prompt injection, data exfiltration. Need sandboxing, permission models, and input validation.

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Evaluating Agent Systems

Outcome Metrics

  • Task success rate
  • Steps to completion
  • Cost per task
  • Human escalation rate

Process Metrics

  • Tool call accuracy
  • Recovery rate after failure
  • Context window utilization
  • Response latency

Adversarial Testing

  • Prompt injection resilience
  • Edge case handling
  • Budget abuse prevention

Tools

  • LangSmith / LangFuse
  • Arize Phoenix
  • Weights & Biases
  • Custom trajectory scoring
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Where Agentic AI Is Heading

Continuous Learning

Agents that improve from feedback and outcomes across sessions — not just within a single context window.

Agent Marketplaces

Buy/sell pre-built agents for specific domains: customer support, data analysis, DevOps, legal research.

Enterprise Agent Mesh

Interconnected agents across departments sharing context, tools, and governance — the "operating system" of the company.

On-Device Agents

Small, fast models running locally for privacy-sensitive tasks. Hybrid cloud/edge architectures.

Regulation & Standards

Emerging frameworks for agent accountability, disclosure (AI acting autonomously), and audit trails.

Human-Agent Collaboration

Agents as copilots, not replacements. Dynamic handoff, shared context, and mixed-initiative interaction.

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Part II

Retrieval-Augmented Generation

Grounding LLMs in external knowledge

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Why RAG?

LLMs are frozen in time at training cutoff. They hallucinate, lack access to private data, and cannot cite sources. RAG solves this by retrieving relevant documents at inference time.

  • Up-to-date information
  • Access to proprietary/private data
  • Verifiable citations & grounding
  • Reduced hallucination
  • Cost-effective knowledge updates

RAG vs Fine-Tuning

Fine-Tuning: Memorization - Expensive to retrain - Can't update facts easily - Risk of catastrophic forgetting RAG: Retrieval at inference - Zero retraining cost - Instant knowledge updates - Source-attributable answers
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Classic RAG Architecture

+----------+ +----------+ +----------+ +----------+
| Documents | -> | Chunking | -> | Embedding | -> | Vector DB |
+----------+ +----------+ +----------+ +----------+
|
User Query ----------------------> [Query Embedding] --+
|
v
+----------+ +----------+ +----------+ +----------+
| Answer | <- | LLM | <- | Context | <- | Retrieval |
+----------+ +----------+ +----------+ +----------+

Indexing pipeline (offline) + Retrieval pipeline (online)

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Chunking Strategies

Fixed-Size Chunking

Split by token count with overlap. Simple but can break semantic units (e.g., splitting a table in half).

Semantic Chunking

Split at natural boundaries: paragraphs, sections, markdown headers. Preserves meaning.

Agentic Chunking

Use an LLM to identify logical chunks. Most expensive but best quality for complex docs.

Late Chunking

Embed full document then extract chunk vectors from contextualized representations. Best of both worlds.

Chunk Size: 256-1024 tokens | Overlap: 10-20% | Strategy depends on document type
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Embeddings & Vector Search

Embeddings convert text into dense numerical vectors. Semantically similar texts produce nearby vectors.

  • OpenAI text-embedding-3-small/large — 1536/3072 dims
  • Cohere Embed v3 — multi-lingual, 1024 dims
  • voyage-2 — optimized for RAG, 1024 dims
  • BGE / E5 — open-source, SOTA

Similarity Measures

Cosine Similarity
cos(A,B) = A.B / (|A|*|B|)

Dot Product (normalized)
same as cosine for L2-normed vecs

L2 Distance
sqrt(sum((Ai-Bi)^2))
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Vector Databases

Pinecone

Fully managed, serverless. High performance, hybrid search, metadata filtering. Good for production at scale.

Weaviate

Open-source, hybrid (vector + keyword). Built-in modules for Q&A, summarization, generative search.

Qdrant

Rust-based, fast filtering, quantization support. Self-hosted or cloud. Very developer-friendly.

Milvus / Zilliz

Cloud-native, GPU-accelerated. Handles billion-scale with sharding and replication.

ChromaDB

Lightweight, embedded. Great for prototyping. In-memory with optional persistence.

pgvector

PostgreSQL extension. Perfect if you already use Postgres. IVFFlat and HNSW indexes.

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Hybrid Search

Vector search finds semantic matches but can miss exact keyword matches. Keyword search (BM25) excels at exact terms. Hybrid combines both with reciprocal rank fusion (RRF).

score = w1 * vector_score + w2 * bm25_score

RRF: score = sum(1 / (k + rank_i))

Typical weight: 0.5 / 0.5
or tuned per domain

When Hybrid Wins

  • Code search (exact symbols + semantics)
  • Medical/legal (precise terminology)
  • Product search (brand names + intent)
  • Multi-lingual corpora
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Metadata Filtering & Pre-Retrieval

Pre-Retrieval Techniques

  • Query rewriting — expand/rewrite user query for better recall
  • Query decomposition — split complex queries into sub-queries
  • HyDE — generate hypothetical document, embed that
  • Step-back prompting — retrieve broader context first

Metadata Filters

{ "filter": { "date": {"$gte": "2025-01-01"}, "author": "John Doe", "category": {"$in": ["tech", "science"]} } }

Reduces search space, improves relevance. Critical for enterprise RAG.

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Post-Retrieval Enhancement

Re-ranking

A cross-encoder (e.g., Cohere Rerank, BGE Reranker) scores retrieved chunks against the query for precision. Top-3 from top-20.

Query + Chunk[0..19] -> Reranker -> Top 3

Context Compression

LLM extracts only relevant sentences from retrieved chunks. Reduces token usage and noise.

Fusion

Multiple retrieval sources (vector + keyword + graph) fused into one ranked set before LLM.

Citation Extraction

Map generated sentences back to source chunks. Enables grounded, verifiable answers.

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Advanced RAG Patterns

Agentic RAG

An agent decides when and how to retrieve. It can ask clarifying questions, iterate on search, and combine multiple sources autonomously.

Self-RAG

LLM generates special tokens to reflect on relevance, support, and usefulness of retrieved passages. Decides whether to retrieve at all.

Corrective RAG (CRAG)

If retrieved docs are irrelevant, the system triggers web search or retries with a different query. Automatic fallback.

Graph RAG

Extract entities and relationships into a knowledge graph. Answer questions by traversing the graph + vector retrieval.

Multi-Modal RAG

Retrieve images, tables, audio alongside text. Use vision-language models for answers grounded in multi-modal context.

Streaming / Real-Time RAG

Continuously index streaming data (news, logs, chats). Freshness-aware retrieval prioritizes recency.

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RAG Evaluation

Retrieval Metrics

  • Recall@k — % of relevant docs in top-k
  • MRR — Mean reciprocal rank
  • NDCG@k — Rank-aware relevance
  • Precision@k — % of top-k that are relevant

Generation Metrics

  • Faithfulness — % of claims supported by context
  • Answer relevancy — Does answer address query?
  • Citation accuracy — Correct source attribution
  • Hallucination rate — Unsupportable claims

Evaluation Frameworks

RAGAS TruLens DeepEval LangSmith Arize — Automated eval with LLM-as-judge and human annotation.

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RAG — Common Pitfalls & Fixes

Lost in the Middle

LLMs focus on top/bottom of context, ignore middle. Fix: re-rank, place best docs first and last.

Chunk Boundary Issues

Answer spans across chunks. Fix: overlapping chunks, sentence-window retrieval, or late chunking.

Stale Index

Knowledge changes but index is outdated. Fix: incremental indexing, TTL-based refresh, change detection.

Query Ambiguity

Vague queries retrieve garbage. Fix: query rewriting, clarification agents, HyDE.

Over-Retrieval

Too many chunks exceed LLM context. Fix: compression, stricter top-k, better chunking.

Latency

Embedding + retrieval + generation. Fix: caching, async pipeline, smaller models for retrieval.

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Production RAG Stack

ETL & Indexing Pipeline

Unstructured.io -> Chunking ->
Embedding -> Vector DB

Document parsing (PDF, HTML, PPTX), cleaning, chunking, embedding.

Serving Stack

Query -> Router -> Retriever ->
Reranker -> LLM -> Post-processing

Async, cached, monitored. A/B testable retrieval strategies.

Observability

Trace every step: query, retrieved chunks, reranker scores, LLM response, citations. Alert on low recall or high hallucination.

Continuous Improvement

Log user feedback, mark bad retrievals, re-chunk problematic documents, fine-tune embedding models on domain data.

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RAG + Agents = Agentic RAG

The convergence: an agent that decides how and when to retrieve knowledge.

User: "Compare Q2 vs Q3 revenue for the EU region"

Agent Thought: I need financial data. I'll query the vector store
with "EU Q2 2025 revenue" and "EU Q3 2025 revenue".
If not found, I'll search the web and scrape earnings reports.

Action: search_vector_db("EU Q2 2025 revenue")
Observation: Partial data found (Q2 only, missing Q3).

Action: search_web("EU region Q3 2025 revenue report")
Observation: Found report at investor-relations.example.com

Action: scrape_url("investor-relations.example.com/q3")
Observation: Q3 EU revenue = $4.2B

Final Answer: Q2: $3.8B, Q3: $4.2B, Growth: +10.5%
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Multi-Modal RAG

Retrieve and reason over images, tables, charts, and audio alongside text.

Architecture

Document -> [Text Chunks] -> Text Embeddings
-> [Images] -> Image Embeddings
-> [Tables] -> Table Embeddings

Query -> Multi-modal encoder ->
Hybrid retrieval across all modalities
-> VLM generates answer

Use Cases

  • PDF analysis (text + figures + tables)
  • Medical imaging + radiology reports
  • Slide decks with charts + narrative
  • Video transcripts + frame retrieval
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Where RAG Is Heading

Long-Context LLMs

1M+ token context windows reduce the need for retrieval. But cost and latency still favor RAG for most use cases.

Learned Retrieval

Models that natively learn when and what to retrieve (e.g., RETRO, Atlas). Retrieval as part of the model architecture.

Agentic Knowledge Management

Agents proactively curate knowledge: identify gaps, trigger indexing, summarize and merge documents.

Personalized RAG

User-specific retrieval: re-rank based on user history, role, preferences. Context-aware personalization.

Benchmarks for RAG

Standardized eval suites: RGB, CRUD, FRAMES. Better metrics for faithfulness and citation quality.

RAG-as-a-Service

Managed RAG APIs (OpenAI, Cohere, Google). Bring your own data, get a hosted RAG pipeline in minutes.

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Part III

Model Context Protocol

Standardizing AI-to-tool communication

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What is the Model Context Protocol?

MCP is an open protocol (Anthropic-led) that standardizes how AI applications connect to external tools, data sources, and services.

Think of it as "USB-C for AI" — a universal connector instead of a dozen proprietary integrations.

  • Open standard (not tied to one provider)
  • JSON-RPC based messaging
  • Client-server architecture
  • Built-in security model

Without MCP

Every agent framework reinvents tool integrations. Fragmented, non-portable, duplicated effort.

With MCP

One protocol. Any MCP server works with any MCP client. Plug and play.

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MCP Architecture

+---------------+ JSON-RPC +---------------+
| MCP Client | <---------------------> | MCP Server |
| (Host App) | (stdin/stdio or SSE) | (Tool Host) |
+---------------+ +---------------+
| |
| Session lifecycle: |
| Initialize -> List Resources/Tools |
| -> Read Resource / Call Tool |
| -> Subscribe to notifications |
v v
+-----------+ +-----------+
| Agent | | External |
| / LLM | | APIs |
+-----------+ +-----------+

Client (Host)

The AI application — agent framework, IDE, CLI tool. Initiates connection and manages sessions.

Server

Exposes capabilities: tools, resources, prompts. Can run locally or remotely.

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Transport Layer

stdio Transport

Parent process spawns child MCP server. Communication over stdin/stdout. Fast, simple, local-only.

// Client spawns server process
const server = spawn("mcp-server", []);
server.stdin.write(jsonRpcRequest);
server.stdout.on("data", handleResponse);

SSE Transport

Server-Sent Events over HTTP. Supports remote MCP servers, streaming, and reconnection.

// Server runs HTTP SSE endpoint
POST /mcp (JSON-RPC request)
GET /mcp (SSE stream for responses)

Streamable HTTP (Experimental)

HTTP with streaming responses. Combines request/response with server-sent events.

Transport Comparison

stdio: Lowest latency, local only
SSE: Remote capable, persistent connection
HTTP: Stateless, firewall-friendly

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MCP Primitives: Tools

Tools are functions the model can call. They are the primary way agents take action.

Tool Definition

{ "name": "get_weather", "description": "Get current weather", "inputSchema": { "type": "object", "properties": { "city": { "type": "string" } }, "required": ["city"] } }

Tool Call

// Request
{ "method": "tools/call", "params": { "name": "get_weather", "arguments": { "city": "Tokyo" } } }

// Response
{ "content": [{ "type": "text",
"text": "Tokyo: 28C, clear" }], "isError": false }
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MCP Primitives: Resources

Resources expose data to the model — documents, database records, images, logs. Like a read-only filesystem for AI.

Resource Definition

{ "uri": "docs://project/api.md", "name": "API Documentation", "mimeType": "text/markdown", "description": "Project API reference" }

Resource Templates

{ "uriTemplate": "db://users/{id}", "name": "User Record", "variables": { "id": "string" } }

// Read: db://users/42

Resource Contents

  • Text — markdown, code, logs
  • Blob — images, PDFs, binaries (base64)
  • JSON — structured data

Use Cases

  • Reference documentation
  • Database records
  • Project source files
  • Knowledge base articles
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MCP Primitives: Prompts

Prompts are templated messages that guide model behavior. Servers expose pre-built prompts for common tasks.

Prompt Definition

{ "name": "code_review", "description": "Review a code change", "arguments": [ { "name": "code", "required": true }, { "name": "language", "required": false } ] }

Prompt Result

{ "messages": [ { "role": "system", "content": { "type": "text", "text": "Review this {{language}} code..." } }, { "role": "user", "content": { "type": "text", "text": "{{code}}" } } ] }

Clients can use these prompts as building blocks for complex agent workflows.

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MCP Primitives: Sampling

Server-to-client LLM request. A server can ask the client to generate text — enabling agent-to-agent, summarization, and moderation flows.

How Sampling Works

// Server requests LLM generation
{ "method": "sampling/createMessage", "params": { "messages": [{ "role": "user",
"content": "Summarize: ..." }], "modelPreferences": { "hints": [{ "name": "claude-3" }], "costPriority": 0.5 } } }

Use Cases

  • Server delegates summarization to client's LLM
  • Moderation / content checks
  • Multi-step tool orchestration
  • User confirmation prompts
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Roots & Notifications

Roots

The client tells the server about its workspace — directories, URLs, or resources the server is allowed to access.

{ "method": "roots/list", "params": {} } // Response: ["file:///project/src", // "file:///project/docs"]

Notifications

Real-time updates. Servers push changes when resources or tools change. Clients push cancellation or progress.

// Server notifies of resource change
{ "method": "notifications/resources/list_changed" } // Server notifies of tool list change
{ "method": "notifications/tools/list_changed" }

Together they enable dynamic, responsive integrations — the server adapts to the client's environment.

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MCP Security Model

Host Controls

The host application retains full control over which MCP servers to connect, what resources to share, and which tool calls to approve.

User Consent

Users must explicitly approve server connections, resource access, and potentially dangerous tool calls (e.g., file write, shell exec).

Least Privilege

Servers only access resources within declared roots. No arbitrary filesystem or network access without explicit permission.

Audit Logging

All tool calls, resource reads, and sampling requests are logged. Full traceability for security review.

Security Recommendations

Run remote MCP servers with authentication. Validate all tool arguments server-side. Never expose MCP servers to untrusted clients without sandboxing.

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MCP Ecosystem (2026)

Official SDKs

TypeScript, Python, Java, Kotlin. Reference implementations maintained by Anthropic.

Hosts / Clients

Claude Desktop OpenCode VS Code (Cline) Continue.dev Windsurf Cursor

Server Registries

npm @modelcontextprotocol PyPI mcp Smithery MCPHub

Tool Servers

Filesystem, GitHub, Slack, Postgres, Brave Search, Puppeteer, Docker, Kubernetes, SQLite

Framework Integration

LangChain, CrewAI, AutoGen, Semantic Kernel all support MCP tools natively.

API Gateways

Azure API Management, Kong, Envoy adding MCP support for enterprise governance.

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Building an MCP Server (Python)

from mcp.server import Server, NotificationOptions from mcp.server.models import InitializationOptions server = Server("weather-server") @server.list_tools() async def handle_list_tools(): return [{ "name": "get_forecast", "description": "Get weather forecast", "inputSchema": { "type": "object", "properties": { "city": {"type": "string"}, "days": {"type": "integer", "default": 3} }, "required": ["city"] } }] @server.call_tool() async def handle_call_tool(name: str, args: dict): if name == "get_forecast": data = await fetch_weather(args["city"], args.get("days", 3)) return [{"type": "text", "text": json.dumps(data)}] async def main(): async with server.run_stdio() as run: await run()
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MCP vs Native Function Calling

OpenAI Function Calling

  • Tightly coupled to one provider
  • Tools defined inline in each request
  • No standard discovery mechanism
  • No resource abstraction
  • No lifecycle management
  • Works only with OpenAI models

MCP

  • Provider-agnostic protocol
  • Tools discovered via standard API
  • Dynamic tool/resource lists
  • Resources + Prompts + Tools
  • Session lifecycle, notifications
  • Works with any model via any host

MCP doesn't replace function calling — it standardizes the layer above it.

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Real-World MCP Use Cases

AI-Powered IDE

VS Code extension connects to filesystem MCP, GitHub MCP, terminal MCP. Agent reads files, commits code, runs tests.

Customer Support Agent

MCP servers for CRM, knowledge base, ticketing system, and email. Agent handles end-to-end support workflows.

Database Administrator

Postgres MCP + Kubernetes MCP + Monitoring MCP. Agent diagnoses slow queries, scales pods, and optimizes schemas.

Research Assistant

Web search MCP + PDF reader MCP + ArXiv API MCP + Note-taking MCP. Agent conducts literature surveys.

DevOps Pipeline

CI/CD MCP + Cloud provider MCP + Incident management MCP. Agent triages alerts, rolls back deployments, and runs diagnostics.

Data Analysis Platform

SQL MCP + Pandas execution MCP + Visualization MCP. Agent explores data, runs statistical tests, and generates charts.

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The Trinity: Agents + RAG + MCP

+----------------------------------------------------------+
| Agentic AI |
| (Planning, Reasoning, Multi-step Execution, Reflection) |
+----------------------------------------------------------+
| | |
v v v
+-----------+ +---------------+ +-----------+
| RAG | | MCP | | Memory |
| (External | | (Tools, | | (Short + |
| Knowledge| | Resources, | | Long) |
| + Vector | | Prompts) | | |
| Search) | | | | |
+-----------+ +---------------+ +-----------+
| | |
v v v
+----------------------------------------------------------+
| External World (APIs, DBs, Web, Files) |
+----------------------------------------------------------+

Retrieval grounds the agent. MCP empowers it. Memory sustains it.

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Architecture Decision Guide

When to use RAG?

Need factual grounding, citations, dynamic knowledge. Avoid: when all knowledge fits in context or data is highly structured (use SQL instead).

When to use Agents?

Multi-step tasks, tool orchestration, autonomous decision making. Avoid: simple Q&A, single-turn classification.

When to use MCP?

Multiple tools, provider-agnostic design, pluggable architecture. Avoid: one-off simple integration with a single API.

When to combine all three?

Complex enterprise workflows: support bots, research assistants, DevOps automation, code generation platforms.

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Looking Ahead: 2026 & Beyond

Convergence

RAG pipelines are becoming MCP servers. Agent frameworks are becoming MCP hosts. The stack is standardizing.

Small, Specialized Models

Small embedding models, small rerankers, small agents. Distillation enables on-device intelligence.

Multi-Modal Everything

Agents that see, hear, and interact with the physical world. MCP extending to vision and audio tools.

Regulatory Compliance

EU AI Act, US executive orders. Traceable agent decisions, grounded RAG outputs, auditable MCP call logs.

Self-Improving Systems

Agents that evaluate their own RAG quality, register new MCP tools, and optimize their own prompts at runtime.

Open Ecosystems

Open-source models + open protocols + open tool registries. The AI stack is becoming as standardized as the web.

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Thank You

Questions & Discussion

Agentic AI RAG MCP

Full slide deck available at this URL

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