The development of Large Language Models (LLMs) has shifted from passive text generators to proactive, goal-oriented "agentic LLMs," capable of planning, utilizing tools, interacting with environments, and maintaining memory. This talk provides a critical review of this rapidly evolving field, particularly focusing on innovations from late 2023 through 2025. We will explore the core architectural pillars enabling this transition, including hierarchical planning, advanced long-term memory solutions like Mem0 , and sophisticated tool integration. Prominent operational frameworks such as ReAct and Plan-and-Execute will be examined alongside emerging multi-agent systems (MAS). This talk will critically analyze fundamental limitations like "planning hallucination" , the "tyranny of the prior" where pre-training biases override contextual information, and difficulties in robust generalization and adaptation. We will also discuss the evolving landscape of evaluation methodologies, moving beyond traditional metrics to capability-based assessments and benchmarks like BFCL v3 for tool use and LoCoMo for long-term memory.

Furthermore, the presentation will address the critical ethical imperatives and safety protocols necessitated by increasingly autonomous agents. This includes discussing risks like alignment faking, multi-agent security threats , and the need for frameworks such as the Relative Danger Coefficient (RDC).

Finally, we will explore pioneering frontiers, including advanced multi-agent systems, embodied agency for physical world interaction, and the pursuit of continual and meta-learning for adaptive agents. The talk will conclude by synthesizing the current state, emphasizing that overcoming core limitations in reasoning, contextual grounding, and evaluation is crucial for realizing robust, adaptable, and aligned agentic intelligence.