The adoption of Large Language Models (LLMs) and Generative AI is changing a multitude of industries, including the legal sector. The practice of law traditionally relies on experience, extensive study and nuanced judgment. As AI grows exponentially, it leads to more human-like interactions and improved efficiency by automating routine tasks like contract drafting and legal documentation. This paper introduces AgentLegal, a state-of-the-art Retrieval-Augmented Generation (RAG) based LLM tailored for corporate legal applications, with the main focus on Indian law. AgentLegal assists corporate lawyers with its numerous features which includes contract drafting, strategic guidance in corporate takeovers, sentiment analysis, real-time user feedback, and risk assessment, while also simplifying complex legal jargon. Developed using Python, Streamlit for UI, Mistral AI: all-MiniLM-L12-v2 as the LLM model and Pinecone as the vector database, AgentLegal is a significant step forward in the advancement in legal technology. While existing AI tools focus narrowly on tasks like case comprehension or contract review, AgentLegal provides a consolidated view of legalities across all domains of Indian Law. It achieves a BLEU score of 0.3655, reflecting moderate lexical similarity. ROUGE scores (Recall: 0.3773, Precision: 0.1739) indicate that the content is considered effective with scope for accuracy improvement. A BERT Score F1 of 0.0378 highlights initial semantic alignment and contextual relevance. This paper evaluates its architecture, capabilities, and potential to enhance efficiency and decision-making for corporate legal practitioners.