Agentic AI Course in Hyderabad

GCC and Enterprise Engineering

Internal knowledge systems, enterprise APIs, workflow assistants, tool-connected operations, human approval, tracing and auditability. Agents that work across enterprise applications must be controllable and reviewable rather than unrestricted.

Cloud and Platform Engineering

Runtime behaviour, credentials, permissions, observability, retries, latency, cost, checkpoints and deployment boundaries. Platform engineers can extend existing infrastructure thinking into agent runtimes and controlled AI execution.

AI and Data Engineering

Retrieval, embeddings, enterprise knowledge, Agentic RAG, memory and evaluation. Move from evaluating one model response toward evaluating complete tool and retrieval trajectories.

Life Sciences and Health-Tech Engineering

Specialised knowledge retrieval, research-support workflows, internal document systems, evidence traceability and human review. Grounded and reviewable knowledge workflows.

Software Developer

You already build APIs, backend services, web applications or product features and want to move into AI applications that use tools, maintain state, retrieve knowledge and execute multi-step workflows.

Best fit if you want to move toward:

AI application engineering, AI developer or agent engineering work.

GenAI Developer

You already understand prompts, LLM APIs or basic RAG and now need deeper engineering around orchestration, memory, tools, evaluation, multi-agent coordination and production behaviour.

Best fit if your current GenAI work still feels like:

chatbots, prototypes or isolated RAG demos.

AI / ML Engineer

You already understand models, embeddings, retrieval or ML workflows and want to engineer systems in which models can make decisions, use tools, maintain context and operate across longer workflows.

Best fit if you want stronger depth in:

Agentic RAG, evaluation, multi-agent systems and production agent behaviour.

Platform / LLM Engineer

You work closer to infrastructure, cloud, serving or production systems and want to understand agent runtimes, observability, tool security, failure recovery and deployment boundaries.

Best fit if your interest is:

reliable operation of AI systems rather than prompt design alone.

Data Engineer

You work with data pipelines, data platforms and data infrastructure and want to build knowledge-access layers, tool-connected workflows and retrieval-driven agent systems on top of existing data systems.

Best fit if you want to add:

Agentic RAG, knowledge agents and tool-connected data workflows.

Stateful Agent Orchestration with LangGraph

State, branching, workflow control and checkpoints for multi-step agent execution.

MCP and Tool Integration

Structured tool access, APIs and resource integration with explicit execution boundaries.

Agentic RAG and Memory

Retrieval, grounding, reranking, context and memory for retrieval-driven agent workflows.

Multi-Agent Systems

Supervisor, handoff and specialised-agent patterns for coordinated multi-agent workflows.

Agent Evaluation and Production Reliability

Tracing, evaluation, retries, observability and failure analysis for production agent systems.

Live Instructor-Led Sessions

Architecture and implementation are discussed live so you understand why a workflow is designed a certain way, not only which framework call produces the output.

Project and Architecture Reviews

Projects are reviewed for system design, tool use, state handling, retrieval decisions and execution quality rather than only whether the final demo runs.

Recordings and Mentor Support

Session recordings are included for revision. Mentor support is available for implementation questions and project guidance throughout the program.

“I had tried LLM projects before joining, but this was the first time I properly understood evals, traces and why an agent workflow breaks after the first demo.”

Arjun P. · Software Engineer

“The biggest difference was the structure. We were not just given tools to try. We were shown how to reason about workflows, trade-offs and what to fix when outputs were unreliable.”

Nisha R. · AI Developer

“As a working professional, I needed live teaching and good recordings. That part mattered, but the real value was being able to ask implementation questions and get clear answers.”

Karthik S. · Senior Data Professional

“The course helped me talk about projects more credibly. Instead of saying I built a chatbot, I could explain orchestration, retrieval choices, trace reviews and deployment decisions.”

Megha T. · Applied AI Engineer