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Reasoning & Planning
Understand how agents break tasks into smaller steps and choose actions intelligently.
✨ 8 Weeks • Live Training • Labs • Capstone • Interview Prep
Build intelligent Agentic AI systems with planning, memory, tools, and autonomous workflows.
This Agentic AI program by JSG is designed for developers, architects, data engineers, automation professionals, and AI enthusiasts who want to move beyond simple prompts and build systems that can reason, plan, call tools, use memory, collaborate across steps, and solve real business tasks.
Autonomous Workflows
Learn how agents think through steps, choose tools, and complete multi-stage goals.
Enterprise Use Cases
Build copilots, support agents, research assistants, automation bots, and decision systems.
Production Readiness
Cover observability, safety, memory control, cost optimization, and deployment design.
Agentic AI Learning Dashboard
From LLM assistants to multi-agent systems and orchestration
Capstone + Real Use Cases
8
Structured weeks of guided learning
4+
Major build stages from single-agent to multi-agent
6
Enterprise-oriented labs and workflow demos
1
Final capstone project with presentation flow
🧩 Mid Program Build
Create an agent that uses search, summarization, and decision rules.
🚀 Final Capstone
Deliver a multi-tool or multi-agent business automation assistant.
Why this Agentic AI program stands out
Most AI courses stop at prompt engineering. This program focuses on how to build systems that can plan, act, retrieve information, invoke tools, manage memory, validate outputs, and coordinate complex work.
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Tool Use & APIs
Connect agents with calculators, search, APIs, databases, code tools, and enterprise systems.
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Memory & Context
Learn short-term, long-term, episodic, and vector-backed memory strategies for agents.
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Business Automation
Apply agents to support desks, research, workflow routing, incident response, and process acceleration.
Live
Interactive sessions with guided demos and discussions.
Labs
Hands-on exercises for every major concept and framework.
Review
Design reviews and architectural thinking built into the journey.
Capstone
Final project with use case, architecture, and demo readiness.
8-week syllabus design
This syllabus starts from foundations and steadily moves into orchestration, tools, memory, evaluation, multi-agent patterns, and deployment.
Foundations of Agentic AI
Understand what makes an AI system “agentic” and how it differs from simple chatbot flows.
- AI assistants vs workflows vs agents
- Single-step vs multi-step reasoning
- Agent loop basics: observe, plan, act, reflect
- Use cases across enterprise and product teams
Prompting, Role Design & Task Decomposition
Learn how system design and prompting shape agent behavior, boundaries, and decision quality.
- System prompts and role framing
- Task decomposition strategies
- Goal definition, subtask planning, output constraints
- Lab: build a planner-style assistant
Tools, Functions & External Actions
Make agents useful by connecting them to real tools and data sources.
- Function calling and structured outputs
- API integrations and action design
- Search, calculator, database, and web tools
- Lab: tool-using research assistant
Memory, Context Windows & Knowledge Access
Teach agents how to remember user preferences, prior steps, and business knowledge.
- Short-term vs long-term memory
- Session state and history compression
- RAG for contextual grounding
- Lab: agent with memory + document retrieval
Agent Frameworks & Orchestration Patterns
Explore the practical frameworks used to create agent systems and workflow graphs.
- LangChain agents and LangGraph basics
- Semantic Kernel planning
- AutoGen and collaborative patterns
- Supervisor-worker and router patterns
Multi-Agent Systems & Collaboration
Move from one smart agent to a team of specialized agents working together.
- Planner, executor, reviewer model
- Specialist agents for domains and roles
- Conflict handling and handoffs
- Lab: multi-agent content or support workflow
Evaluation, Safety & Governance
Build trust by measuring agent quality, reducing failures, and enforcing safety boundaries.
- Failure modes in agent systems
- Evaluation rubrics and test suites
- Guardrails, permissions, and approvals
- Human-in-the-loop checkpoints
Deployment, Monitoring & Capstone Delivery
Prepare your agent system for real use with production thinking and final presentation flow.
- FastAPI or app integration patterns
- Logs, traces, observability, and cost tracking
- Deployment options and scaling considerations
- Capstone project build and demo presentation
Industry Readiness & Interview Preparation
Translate your learning into job-ready articulation and portfolio value.
- Scenario-based interview questions
- Architecture explanation techniques
- Resume project bullet writing
- Capstone presentation sequence for recruiters or clients
Tools and frameworks covered
The course uses a practical mix of model providers, orchestration frameworks, memory tools, and deployment options.
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Development Stack
- Python
- VS Code
- Jupyter / notebooks
- Git and GitHub
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Model Platforms
- OpenAI
- Azure OpenAI
- Anthropic Claude
- Open-source model options
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Agent Frameworks
- LangChain
- LangGraph
- Semantic Kernel
- AutoGen
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Retrieval & Memory
- ChromaDB
- Pinecone
- pgvector
- Session and structured memory patterns
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Monitoring & Evaluation
- Prompt logs
- Agent traces
- Evaluation scorecards
- Failure analysis dashboards
☁️
Deployment Options
- FastAPI
- Container apps
- Cloud deployment patterns
- Workflow triggers and integrations
Assignments, milestones, and capstone
Learners progressively build toward a production-style agent system instead of completing disconnected exercises.
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Weekly Labs
- Planner assistant
- Tool-using agent
- Memory-enabled support assistant
- Multi-agent workflow prototype
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Capstone Project
- Choose a business problem
- Design the agent architecture
- Implement tool access and memory
- Present results, limitations, and future scope
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Interview & Career Support
- Agentic AI interview questions
- Scenario-based explanation practice
- Portfolio positioning guidance
- Resume bullets for agent projects
Week 2Planning Demo
Submit a task decomposition example and explain how the agent decides what to do first.
Week 4Memory Checkpoint
Show how your agent uses retrieval and memory safely while remaining grounded in facts.
Week 6Multi-Agent Review
Demonstrate collaboration between multiple specialized agents with routing or supervision.
Week 8Final Demo
Present the full capstone with architecture, flow, limitations, governance, and business impact.
Program outcomes
By the end of the program, learners should be able to design, build, evaluate, and explain agentic AI systems confidently.
Design agent workflows
Create systems that plan tasks, make decisions, and complete multi-step goals rather than answering only one question at a time.
Integrate real tools
Connect agents to APIs, search, databases, file systems, and enterprise actions in a controlled way.
Use memory intelligently
Implement short-term and long-term memory patterns for better personalization and continuity.
Build multi-agent solutions
Compose specialist agents that collaborate under routing, supervision, or review patterns.
Evaluate reliability
Measure failures, safety risks, hallucinations, and tool errors using structured evaluation approaches.
Deliver production-ready demos
Deploy, monitor, and present a real agentic AI solution for business or technical audiences.
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