Full Stack Agentic AI Development using OpenAI Agents SDK Framework

Full Stack Agentic AI Development Using OpenAI Agents SDK Framework

Total Classes: 15
Duration: 2 Hours per Class
Prerequisite: Python & Basic Programming Knowledge

NOTE : As part of this course, you will prepare 50 Most Frequently Asked Agentic AI Development Interview Questions with Detailed Answers (Theory + Coding-Based).

Day 1 – Introduction to OpenAI, Agents & the Agent SDK

1. What is OpenAI and where the Agent SDK fits
   • Overview of OpenAI API products (chat completions, assistants/agents, tools)
   • Why use the Agent SDK vs. direct HTTP calls
2. Core concepts and terminology
   • Agents, tools, runs, state, messages, threads/sessions
   • Differences between “classic” API usage and the Agent SDK model
3. Development environment setup
   • Installing the SDK (language of choice: Node.js or Python)
   • API keys, environment variables, and basic security practices
4. First “hello world” agent
   • Minimal agent that echoes user messages
   • Running locally from CLI or a simple script

Day 2 – SDK Basics: Creating and Configuring an Agent

1. Creating an agent
   • Initializing an agent instance
   • Setting model, instructions (system prompt), and defaults
2. Agent configuration options
   • Temperature, max tokens, response format options
   • Safe defaults vs. custom configurations
3. Managing sessions / conversations
   • Starting, continuing, and ending sessions
   • Session IDs and user IDs
4. Logging and basic debugging
   • Printing requests and responses
   • Handling errors from the SDK

Day 3 – Message Handling & Conversation Patterns

1. Message types and roles
   • User, system, assistant messages
   • How instructions interact with user prompts
2. Conversation memory
   • How the SDK preserves context
   • When to truncate or summarize history
3. Prompt design basics
   • Structuring prompts clearly for tools vs. pure chat
   • Using examples in prompts (few‑shot prompting)
4. Simple multi‑turn flows
   • Implementing a Q&A bot
   • Handling clarifying questions and follow‑ups

Day 4 – Tools & Function Calling (High Level)

1. What are tools in the Agent SDK
   • Concept of tool calling vs. pure text generation
   • Examples: calculator, web search, internal APIs
2. Tool schemas
   • Defining a tool name and description
   • Request/response JSON schemas for tools
3. Tool selection and routing
   • How the agent decides when to call a tool
   • Best practices in tool descriptions
4. Basic custom tool integration
   • Creating a simple function (e.g., math or date)
   • Wiring it into the agent and testing

Day 5 – Advanced Tools: API Integration & Error Handling

1. Integrating external REST APIs
   • Calling third‑party services (weather, CRM, internal APIs)
   • Managing API keys and secrets securely
2. Tool error handling
   • Handling timeouts, invalid arguments, and API failures
   • Designing robust tool responses for the model
3. Tool result formats
   • Structured data vs. assistant‑facing summaries
   • Returning user‑friendly vs. model‑friendly data
4. Multiple tools and orchestration patterns
   • When to split tools vs. have a single “mega tool”
   • Priority and conflict resolution between tools

Day 6 – Working with Files, Knowledge & Context

1. Providing documents and data as context
   • Uploading / referencing files (if applicable in SDK version)
   • When to embed vs. when to pass raw text
2. Retrieval‑augmented generation (RAG) foundations
   • Concept of retrieval and vector search
   • Using tools to fetch knowledge and feed it to the agent
3. Handling long documents
   • Chunking strategies and summarization
   • Citing or referencing sources in outputs
4. Designing knowledge‑aware workflows
   • FAQ assistant using a document store
   • Internal knowledge bot (e.g., policy helper)

Day 7 – Structured Outputs & Data Extraction

1. Why structured outputs matter
   • Use cases: forms, JSON, database records, APIs
2. Response format specifications
   • Enforcing JSON schemas or typed responses (if supported)
   • Validating and parsing the result safely
3. Extracting fields from unstructured text
   • Name, date, amount, category extraction
   • Handling ambiguous or missing values
4. Building a data‑extraction agent
   • Example: invoice parser, ticket classifier, log summarizer

Day 8 – Building a Web Backend with the Agent SDK

1. Integrating the agent into a web server
   • Node/Express or Python/FastAPI/Flask pattern
   • Request/response flow from browser → server → agent
2. Designing HTTP endpoints around the agent
   • Chat endpoint, task endpoint, tool endpoints
3. Authentication and access control basics
   • API keys, user sessions, rate limiting considerations
4. Error handling and observability in production
   • Logging, request IDs, basic metrics

Day 9 – Front‑End Integration & Streaming

1. Building a basic chat UI
   • HTML/JS or React/Vue front‑end pattern
   • Sending user messages and showing agent responses
2. Streaming responses
   • Why streaming improves UX
   • Implementing streaming with Server‑Sent Events or WebSockets
3. Typing indicators and partial results
   • Handling partial updates on the client
4. Handling reconnection and retry logic
   • Front‑end resilience patterns

Day 10 – Multi‑Step Workflows & Orchestration

1. Multi‑step tasks with an agent
   • Planning, tool calls, and execution loops
   • Asking the user for clarification mid‑workflow
2. Decomposing complex tasks
   • Breaking down big goals into smaller steps
   • Controlling how many tool calls or steps are allowed
3. Workflow patterns
   • “Plan‑then‑execute” vs. “reactive” agent
   • Chaining tools: search → analyze → summarize
4. Ensuring determinism where needed
   • Fixing temperatures, constraining outputs, and validation

Day 11 – Evaluation, Testing & Guardrails

1. Basic testing of agents
   • Unit tests for tools and prompt templates
   • Integration tests for whole conversations
2. Evaluation strategies
   • Spot checks vs. scripted test suites
   • Measuring helpfulness, correctness, and safety
3. Guardrails and safety
   • Content filters, policy checks, and red‑team prompts
   • Limiting tools and data exposure by design
4. Logging and feedback loops
   • Storing conversations (within policy) for review
   • Iterating on prompts and tools based on failures

Day 12 – Performance, Costs & Optimization

1. Understanding cost drivers
   • Token usage, model choice, and tool calls
   • Reducing unnecessary context
2. Latency optimization
   • Parallel vs. sequential tool calls
   • Caching results where appropriate
3. Prompt and context optimization
   • Shortening instructions while keeping clarity
   • Summarization strategies to shrink history
4. Monitoring costs over time
   • Simple logging and dashboards (even basic CSV logs)

Day 13 – Specialized Use Case 1: Task Automation Agent

1. Designing an automation agent
   • Repetitive back‑office or developer tasks
   • Identifying safe operations to automate
2. Connecting to task systems
   • Ticket systems, project tools, calendar/email APIs (conceptually)
3. Handling confirmations and approvals
   • When to ask users before executing an action
4. Auditing and traceability
   • Keeping a record of actions for review

Day 14 – Specialized Use Case 2: Knowledge & Support Agent

1. Designing a customer or internal support agent
   • FAQs, troubleshooting flows, escalation criteria
2. Knowledge integration
   • Connecting to a knowledge base or CMS via tools
   • Updating responses when content changes
3. Handling edge cases and escalations
   • When to say “I don’t know” or route to a human
4. Metrics for support agents
   • Deflection rate, satisfaction, first‑response quality

Day 15 – Packaging, Deployment & Best Practices Recap

1. Project structure and packaging
   • Organizing code: agents, tools, routes, config
   • Environment configuration (dev/stage/prod)
2. Deployment strategies
   • Deploying to a cloud platform (high‑level: containers, serverless)
   • Managing secrets in production
3. Operational best practices
   • Rotating keys, updating models, monitoring health
4. Course recap and next steps
   • Review of key Agent SDK capabilities
   • Suggested advanced topics (multi‑agent systems, custom models, fine‑tuning, advanced RAG)