Agentic AI
195 articles in this category (Page 8 of 9)
AI Interview Series #2: Explain Some of the Common Model Context Protocol (MCP) Security Vulnerabilities
Three critical Model Context Protocol (MCP) security vulnerabilities—Tool Poisoning, Rug Pulls, and Tool Hijacking—exposed in 2025 AI research.
How to Design an Advanced Multi-Agent Reasoning System with spaCy Featuring Planning, Reflection, Memory, and Knowledge Graphs
Build a multi-agent AI system with spaCy that extracts entities, constructs knowledge graphs, and learns from experience using reflection and memory modules.
How to Build a Fully Self-Verifying Data Operations AI Agent Using Local Hugging Face Models for Automated Planning, Execution, and Testing
Build a self-verifying DataOps AI agent using Microsoft’s Phi-2 model for automated planning, execution, and testing with local Hugging Face models.
Neural Memory Agents with Differentiable Memory, Meta-Learning, and Experience Replay for Continual Adaptation
A comprehensive guide to building neural memory agents that leverage differentiable memory, meta-learning, and experience replay to adapt to dynamic environments without catastrophic forgetting.
Multi-Agent System for Integrated Multi-Omics Data Analysis with Pathway Reasoning
A tutorial on building a multi-agent system to analyze transcriptomic, proteomic, and metabolomic data for biological insights using pathway reasoning and drug repurposing.
Building an Autonomous Wet-Lab Protocol Planner with Salesforce CodeGen for Agentic Experiment Design and Safety Optimization
A detailed tutorial on creating an AI-driven system for automating lab protocols, reagent validation, and safety checks using Salesforce CodeGen and Python.
Moonshot AI Introduces Kimi K2 Thinking: A Breakthrough in Long-Horizon Reasoning and Tool Use
Moonshot AI releases Kimi K2 Thinking, an open-source thinking model capable of executing 200–300 sequential tool calls without human intervention, optimized for long-horizon reasoning and agentic tasks.
Creating AI-Ready APIs: Best Practices for Enhancing AI Performance and Reliability
Explore Postman's checklist for building AI-ready APIs, emphasizing machine-readable metadata, error semantics, and consistency to ensure AI agents interact reliably with your systems.
Anthropic's Research Demonstrates Claude's Introspective Awareness Through Concept Injection in Controlled Layers
Anthropic's study reveals that Claude models can detect injected concepts via internal activations, offering causal evidence of introspection. The research highlights controlled success rates and implications for LLM transparency.
A Comprehensive Enterprise AI Benchmarking Framework for Evaluating Rule-Based, LLM, and Hybrid Agentic Systems
A detailed coding implementation of a framework to benchmark rule-based, LLM-powered, and hybrid agentic AI systems across real-world enterprise tasks like data transformation, API integration, and workflow automation.