Agentic AI Engineering: Build Autonomous AI Workers (Python & LangChain)
Duration : 40 Hours
Master the future of automation. Design self-correcting, multi-step AI systems that execute complex business logic with minimal human oversight.
New to Web3 & AI
No coding background
Aspiring for Remarkable Growth
Prefers interactive learning
Desires practical experience
Prepare for Success
Covers the full stack from LLM foundation to secure, scalable deployment and monitoring (Source 4.1)
Program Eligibility
Software Developers, Data Scientists, MLOps Engineers, and Python Developers.
Prerequisite: Intermediate Python knowledge.
Build Autonomous AI Workers (Python & LangChain)
27K+ Students Enrolled
Rs.35,000
+ Taxes
Overview of Educity Programs
Course Schedule
- LLM Fundamentals: Core concepts of transformers, model training (fine-tuning vs. RAG), and optimizing models for latency and cost (e.g., using open-source models like Llama, Groq, or DeepSeek).
- The Agent Architecture: Deep dive into the core components: Planning, Memory (short-term & long-term), Tool-Use, and Reflection/Self-Correction.
- Prompt Engineering for Agents: Mastering advanced techniques like Chain-of-Thought (CoT), Tree-of-Thought (ToT), and using Pydantic for reliable, structured output from LLMs.
- Mastering CrewAI/LangGraph: Hands-on development of specialized multi-agent systems using the industry’s leading frameworks.
- Role-Based Collaboration: Defining roles, goals, and processes for collaborative agents (e.g., a Researcher Agent feeding information to a Writer Agent).
- Tool Creation & Function Calling: Equipping agents with custom tools (API access, database queries, web scraping) and implementing complex, multi-step function calling for real-world tasks.
- Advanced Flow Engineering: Designing dynamic, event-driven workflows using CrewAI Flows and LangGraph state machines for precise control and auditing of complex tasks.
- RAG Architecture Deep Dive: Moving beyond Naive RAG to implement advanced techniques like Query Transformation (e.g., Hypothetical Document Embeddings) and Reranking for high-quality context retrieval.
- Vector Database Implementation: Practical labs using production-grade vector databases (e.g., Pinecone, Qdrant, or Chroma) for high-speed, semantic search.
- Data Preparation: Mastering text chunking strategies, metadata filtering, and embedding model selection for optimal retrieval performance.
- Agentic RAG: Integrating RAG directly into the agent’s memory and reasoning process, allowing agents to autonomously decide when and how to retrieve external information.
- Containerization & Scaling: Packaging agents using Docker and deploying them reliably using Kubernetes or serverless functions for horizontal scaling.
- CI/CD for AI Agents: Setting up automated testing and deployment pipelines to manage agent versions and dependencies efficiently.
- Observability & Monitoring: Implementing logging, metrics, and tracing (LangSmith or equivalent) to monitor agent cost, latency, and model drift in production.
- Security & Compliance: Best practices for securing LLM API keys, managing user data, and auditing agent behavior for enterprise compliance and ethics.
What can you expect from the program
- Stop writing code; start building autonomous workers. You will jump ahead of 90% of developers who are still focused only on basic LLM calls.
- Webinars and Workshops: Interactive sessions with industry experts.
- Connect with peers, mentors, and professionals from the AI and Blockchain ecosystem to expand your knowledge and build lasting professional relationships.
- Receive dedicated guidance and mentorship to clarify queries, strengthen your understanding, and explore additional learning opportunities in AI and Blockchain technologies.
- Showcase your AI and Blockchain capstone project to a panel of industry experts, demonstrating your technical proficiency, creativity, and problem-solving skills.
Attain Recognition with the Certificate of Course Completion
Upon completing the course, you will receive a certificate—an impactful addition to your LinkedIn profile that can capture the interest of our hiring partners and prominent big data companies.