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Unlock MentorAgentic AI: From Chatbots to Systems That Act
Live Webinar4.6 Rating
The jump from a model that responds to a system that plans, uses tools, and acts on its own is smaller than it sounds — and far harder to get right than the demos suggest. This session unpacks what agentic AI really involves: how agents are structured, why they fail in ways ordinary AI applications don't, and what it takes to run one reliably enough to trust. Practical, technically grounded, and focused on the gap between an impressive prototype and something you'd put in front of real users.
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Agentic AI: From Chatbots to Systems That Act Overview
Agentic AI has become the dominant theme in applied AI, and for good reason. Giving a model the ability to plan a sequence of steps, call tools, retain context, and act with a degree of autonomy unlocks a class of work that single-turn prompting never could. It also introduces a set of failure modes that most teams meet for the first time in production.
This webinar walks through how agentic systems are actually built. We'll cover the core components — planning and reasoning loops, tool and API integration, memory, and orchestration — and how these fit together in single-agent and multi-agent designs. From there, we move to the harder part:...
From the Speaker
Surendra Pandar is an AI / Agentic AI / ML specialist with 5 years of hands-on experience in LLM application development, systems architecture, MLOps. He focuses on building production-grade agentic systems bridging engineering and applied AI, bringing a practical, implementation-first perspective to complex technical concepts.
Key Takeaways
- A clear definition of what makes a system "agentic" — and how it differs from chatbots, copilots, and standard RAG pipelines
- Understanding of core agent architecture — planning loops, tool use, memory, and orchestration, and how design choices affect behaviour
- When to use a single agent versus multi-agent designs, and why more agents frequently means more failure surface rather than more capability
- Practical techniques for reliability — scoping autonomy, constraining tool access, handling errors, and placing human checkpoints where they matter
- How to evaluate and monitor non-deterministic systems, including what to log, what to test, and why traditional QA falls short
- A grounded view of security and permissions — the risks that emerge specifically when AI is allowed to take actions
- An honest read on cost, latency, and current maturity — which agentic use cases are production-ready today and which are still research
- A framework for your first agent project, including how to pick a use case where failure is cheap and value is measurable
Meet the Guest Speaker
Agentic AI: From Chatbots to Systems That Act Related FAQs
The program duration varies by path, typically ranging from a few weeks to several months. It is structured into comprehensive modules covering foundational to advanced topics with hands-on exercises and real-world projects.
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