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Intermediate CoursePart of AI Operator

Custom Agents, MCP & Debugging

Build the intelligence layer of enterprise AI. Over three weeks, you will deploy a private knowledge agent on a vector database, integrate enterprise tools via the Model Context Protocol, and instrument a multi-agent system with full observability and retry logic.

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3 weeks

What You'll Learn

Build an internal chatbot grounded strictly in private SOPs using RAG and vector search
Reduce API costs through semantic caching of similar queries
Connect enterprise tools via MCP to create a unified, secure integration layer
Instrument agents with traces, runs, and threads for full execution visibility
Engineer retry and fallback logic to eliminate single points of failure in agent pipelines

Course Content

W1
Week 1: RAG & Vector Databases
Ground your agents in private, governed knowledge — not public internet noise.
1
Enterprise Data Isolation
Understanding why private, governed knowledge bases are non-negotiable for enterprise AI accuracy and compliance.
2
Demystifying Embeddings
Translating unstructured corporate documents — PDFs, wikis, SOPs — into mathematical vectors a search engine can query.
3
Vector Search Mechanics
Learning how AI retrieves semantically relevant context to inject into prompts, replacing keyword search with meaning-based retrieval.
4
Semantic Caching
Reducing API costs by caching responses to queries with semantically similar meanings rather than exact string matches.
Weekly Win
Knowledge Agent
Deploy an internal chatbot grounded strictly in private SOPs that returns cited, source-linked answers — refusing to hallucinate outside its knowledge base.
W2
Week 2: The Model Context Protocol (MCP)
Replace brittle custom integrations with the universal standard for AI connectivity.
1
The Integration Crisis
Analyzing the fragility and security risks of custom API connections, and why point-to-point integrations fail at enterprise scale.
2
Introduction to MCP
Understanding the Model Context Protocol as the universal "USB-C" standard that lets any AI agent connect to any enterprise tool securely.
3
The Universal Semantic Layer
Unifying disparate enterprise software silos — CRM, calendar, docs, databases — under a single, secure, AI-readable protocol.
4
Client & Server Architecture
Configuring MCP servers to securely expose internal tools and data to AI clients with strict permission scoping.
Weekly Win
Meeting Automation
Deploy a multi-platform MCP automated meeting workflow that reads calendars, summarizes prior notes, and drafts agendas across connected tools.
W3
Week 3: Orchestrating & Debugging Agents
Build agents you can see inside — and fix when they fail.
1
The Black Box Problem
Understanding the non-deterministic failure modes of autonomous agents and why standard logging is insufficient for debugging them.
2
Observability Primitives
Defining Runs, Traces, and Threads as the core building blocks of agent execution logs and their relationship to debugging.
3
Tracing & Telemetry
Integrating industry-standard observability platforms to monitor request latency, token usage, and individual agent steps.
4
Retry & Fallback Logic
Engineering systemic resilience against API timeouts, rate limits, and infinite logic loops using structured retry and circuit-breaker patterns.
Weekly Win
Multi-Agent Ecosystem
Orchestrate and debug a multi-agent research team using trace logs to identify the root cause of a simulated agent failure and deploy a fix.

Prerequisites

Familiarity with common business tools
Basic AI understanding

Tools You'll Use

ClaudeClaude
ChatGPTChatGPT
PineconePinecone
LangChainLangChain
NotionNotion
📚
Intermediate Level
Course Price
₹6,999
India
$149
International · One-time payment
Next cohort starts Mar 30
Duration3 weeks
LevelIntermediate
FormatCohort-based
Modules3

What's included:

Live cohort sessions
Hands-on projects
Certificate of completion
Lifetime access
Career support

Part of Learning Track

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AI Operator
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