Google’s SIMA 2 agent uses Gemini to reason and act in virtual worlds
Google DeepMind has introduced SIMA 2, an advanced AI agent that leverages Gemini’s language and reasoning capabilities to interact more effectively in virtual environments. This new version improves upon...
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From Developer to AI Teammate: Building an Agentic Automation Prototype
This article explores how developers can transform repetitive workflows into intelligent, AI-powered tasks using tools like LangChain, RAG, and automation loops. The author, Sayan Chatterjee, explains how agentic AI...
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AI Code Refactoring: Principles, Techniques, and Benefits
AI code refactoring is a process that uses artificial intelligence to improve the quality, readability, and maintainability of code without changing its functionality. This technique helps developers streamline legacy...
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Prompt Engineering vs Context Engineering
This article explains the differences between prompt engineering and context engineering, two approaches used to interact with large language models (LLMs). It highlights that prompt engineering is ideal for...
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Build a LangGraph Multi-Agent system in 20 Minutes with LaunchDarkly AI Configs
Scarlett Attensil’s article demonstrates how to build a LangGraph multi-agent system in under 20 minutes using LaunchDarkly AI Configs. The guide explains how to set up the system, test...
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Agentic AI Using Apache Kafka as Event Broker With the Agent2Agent Protocol (A2A) and MCP
Agentic AI is becoming a popular design pattern for creating more intelligent and autonomous systems. Unlike traditional automation, agentic AI involves agents that operate independently and collaborate with other...
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Using Schema Registry to Manage Real-Time Data Streams in AI Pipelines
This article explores the role of schema registries in managing real-time data streams within AI pipelines, emphasizing their importance in ensuring data consistency, integrity, and scalability. The tutorial is...
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How Tool-Call Observability Enables You to Support Reliable and Secure AI Agents
Tool-call observability is essential for ensuring the reliability and security of AI agents. As these agents become more autonomous, traditional monitoring methods fall short in tracking their decision-making processes....
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