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Director, Intelligent Automation, AI & Process Excellence at Fidelity Investments
Santhosh Vijayabaskar is a global thought leader, speaker, and author in technology, specializing in digital transformation and innovation. He brings deep expertise in Intelligent Automation, AI/ML, and Process Excellence, working with Fortune 500 companies to enhance efficiency, scale solutions, and empower teams to thrive in a digital-first world. As the author of Process Coach Playbook, he provides practical strategies for leaders and practitioners to accelerate digital transformation, implement business process automation, and drive sustained innovation and growth. His expertise in Robotic Process Automation (RPA) and low-code/no-code solutions has made him a sought-after thought leader in the field. Through his books, speaking engagements, and mentorship, Santhosh inspires the next generation of problem solvers and leaders. When not writing or speaking, Santhosh engages in mentoring aspiring professionals and contributing to thought leadership forums, pushing the boundaries of innovation in the digital age.
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Comments
Jan 09, 2025 · Santhosh Vijayabaskar
Thank you, Sibasis! I’m glad you found the insights and hands-on valuable. It’s an exciting area with immense potential to enhance productivity and streamline our dev workflows. If you have any questions or want to explore specific aspects further, feel free to share!
Dec 31, 2024 · Peter Verhas
Joel, thanks for bringing up Rebol! It's fascinating to see how languages like Rebol have explored minimalistic syntax while focusing on concepts like 'words' and 'blocks.' Rebol's influence on projects like JSON and the Red programming language is indeed noteworthy.
Dec 30, 2024 · Santhosh Vijayabaskar
Thank you for the kind words and thoughtful question! Using variables is one approach, but the key lies in designing a robust coordination mechanism. Context and coherence are often maintained through shared states or context variables, enabling agents to pass critical information seamlessly. However, depending on the system's complexity, layering this with task prioritization strategies or reinforcement learning can further optimize performance.