Shashank Shekhar Singh is a decision scientist, AI practitioner, and independent researcher working at the intersection of data science, machine learning, generative AI, and agentic systems. He is the creator and maintainer of Agentic AI Kit, an open-source Python framework for building AI agents with memory, tools, retrieval, SQL intelligence, multi-agent workflows, and adaptive episodic memory. The project combines practical agent engineering with research into how artificial agents can retain experience, update identity-relevant evidence, and use memory to influence future decisions.
His work focuses on turning complex ideas into transparent, reusable, and testable systems. This includes analytical decision frameworks, machine-learning workflows, generative AI applications, memory architectures, and open-source software designed for both practitioners and researchers. Agentic AI Kit reflects that philosophy: build systems that are useful in practice, inspectable in design, and open to experimentation, criticism, and extension.
Shashank’s current research explores emotion-aware episodic memory, identity-conditioned appraisal, retention, correction-safe replay, tree-based retrieval, and memory-informed reinforcement learning. The research record for Agentic AI Kit is available through OpenAIRE and ORCID, while the framework itself is developed publicly on GitHub and distributed through PyPI.
LinkedIn
Professional experience, data science, AI, and project updates
OpenAIRE
Research record and archived publication for Agentic AI Kit
ORCID
Researcher identity and publication record
GitHub
Source code, experiments, examples, and ongoing development
PyPI
Official Python package and release history