Move beyond basic prompt engineering. Join AI Leader Anirban in this exclusive masterclass to architect resilient, stateful multi-agent systems that evaluate, self-correct, and continuously improve over time.
*Based on industry reports from Gartner, McKinsey, and World Economic Forum 2025–2026.
of Fortune 500 companies are piloting multi-agent AI systems
of AI leaders say agentic AI is their top investment priority in 2026
demand-supply gap for certified agentic AI professionals
Traditional linear prompt chains and basic Retrieval-Augmented Generation (RAG) pipelines frequently hit a wall in enterprise environments. When faced with complex, non-deterministic tasks, single-shot LLM calls struggle with memory retention, tool errors, and edge-case handling.
The industry is shifting toward "Loop Engineering"—architecting autonomous, stateful, and cyclic multi-agent workflows that can evaluate their own outputs, recover from tool failures, and self-correct over time.
In this exclusive ADaSci Masterclass, Anirban breaks down the practical frameworks required to build production-grade agentic loops. You will learn how to design Planner-Executor dynamics, manage state across prolonged executions using frameworks like LangGraph, implement Human-in-the-Loop (HITL) governance, and leverage semantic memory for continuous system improvement.
Beyond Linear Prompts: Transitioning from static RAG pipelines to cyclic, stateful multi-agent workflows.
ReAct & Planner-Executor Dynamics: Designing agents that break down complex enterprise tasks, route tools, and assess their own outputs.
Self-Correction & Reflection Patterns: Implementing autonomous evaluation checks (LLM-as-a-judge) and graceful API/tool error recovery.
State & Memory Management: Maintaining execution state with LangGraph and using episodic memory to learn from past trajectories.
Governance & HITL Checkpoints: Setting up interruptible loops for human oversight on high-stakes enterprise actions.
Live Interactive Q&A: Direct AMA with Anirban on enterprise AI architecture and career growth in 2026.
Architect production-ready multi-agent systems using cyclic, stateful design patterns.
Implement self-correction mechanisms to prevent agent loops from stalling or crashing on API timeouts.
Master state management frameworks (such as LangGraph) for multi-step reasoning.
Design Human-in-the-Loop (HITL) safety checkpoints for enterprise compliance and governance.
Optimize token consumption and execution latency across autonomous agent trajectories.
AI Engineers & Developers building production-grade LLM applications and agentic workflows.
Data Scientists & ML Architects transitioning from prototype notebooks to enterprise AI pipelines.
Tech Leads, Engineering Managers & CXOs evaluating multi-agent systems, build vs. buy choices, and AI governance.
ADaSci Candidates (CFDE, CAASA, AIDP) seeking deep technical insights directly from industry leaders.
Fundamental understanding of Python and Generative AI concepts (LLMs, Prompts, RAG).
Familiarity with agentic frameworks (e.g., LangChain, LangGraph, AutoGen, or CrewAI) is helpful but not mandatory.
Basic understanding of cloud infrastructure or enterprise software architecture.
Complete Live Recording access to the masterclass.
Presentation Deck & Architecture Blueprints shared by the speaker.
Access to ADaSci Community Forum for post-webinar Q&A and networking.
Director, AI & Data Science | ADaSci
Anirban Ghatak is a seasoned AI & Data Science leader with over 21 years of experience building, scaling, and leading analytics, BI, and data science business units with full P&L ownership and C-level reporting. An alumnus of BITS Pilani and IIM Indore, Anirban is an ex-founder and intrapreneur specialized in taking enterprise AI practices to scale. He is a prominent industry voice and speaker on the evolution of work in an Agentic AI world, advocating for the transition from systems of execution to hybrid systems of autonomous orchestration and robust AI governance.