Prompt Injection Attacks on AI Agents: The New Enterprise Vulnerability
At its core, a prompt injection attack involves an attacker inserting malicious instructions into an AI’s input in order to manipulate the AI’s behavior.
Read the full articleEnterprise AI Agent Insights & Technical Guides
Expert insights on AI agents, RAG systems, and enterprise AI implementation. Technical tutorials, security guides, and industry trends.
What's Next After Agentic AI
Beyond Agentic AI: The Next Wave of Intelligent Systems The current wave of agentic AI represents a significant leap from generative models, but it's just one step in a...
What Are Autonomous AI Agents? Complete Guide to Self-Extending Intelligence
Autonomous AI agents can create their own tools, make independent decisions, and extend their capabilities in real-time without human intervention. Learn how autonomous agents work, their key differences from traditional AI, security features, and real-world applications transforming businesses in 2025.
The Future of AI & Humans: From Creators to Curators
The Future of AI & Humans: From Creators to Curators. We are becoming the editors, directors, and quality‑assurance officers of machine output. The future of AI and humanity hinges on how well we embrace this curatorial mandate.
AI Memory: How RAG-Powered Memory Systems Will Transform Enterprise AI in 2025
The AI Memory Revolution: How RAG-Powered Memory Systems Will Transform Enterprise AI in 2025. AI memory is emerging as the next frontier in enterprise AI adoption
MCP Enterprise Adoption Report 2025: Challenges, Best Practices & ROI Analysis
The Model Context Protocol (MCP) is rapidly becoming the enterprise standard for AI-tool integration, with organizations reporting 30% reductions in development overhead and 50-75% time savings on common tasks. However, successful enterprise adoption requires navigating complex technical, security, and operational challenges. This comprehensive report analyzes real-world MCP implementations, identifies key roadblocks, and provides actionable strategies for enterprise success.
In-Depth Analysis: OpenAI Assistants API vs. OpenAI Responses API, A Complete Comparison Guide
OpenAI Assistants API vs. OpenAI Responses API (April 2025) OpenAI offers two distinct APIs for building AI-driven assistants and agents: the Assistants API and the Responses API.
Ragwalla Agents: Agentic RAG Beyond Simple Retrieval
Introducing Ragwalla Agents: Beyond Simple RAG Ragwalla Agents extend the traditional RAG (Retrieval-Augmented Generation) model by adding tool execution, conversation memory, and real-time interaction capabilities. While basic RAG systems...
AI Files: An App for The OpenAI Files and Vector Stores API
AI Files, is a free app designed to simplify file management for OpenAI’s Files and Vector Store APIs. Quickly bulk upload, delete, manage vector stores, and securely store API keys. Streamline your workflow—no scripting required!
Key Differences Between the OpenAI Responses API and the Assistants API
The OpenAI Responses API simplifies conversation state management by handling it server-side. Developers can maintain context across interactions by including the `previous_response_id` parameter in their requests, referencing the last response's ID. This approach eliminates the need to manually track conversation history, as the API retrieves and incorporates the entire conversation chain automatically. However, it's important to note that all previous input tokens in the conversation chain are billed as input tokens.
Managing Costs with the OpenAI Assistants API
Warning: using the OpenAI Assistants API can result in unexpectedly high costs because of inherent flaws in its architecture. Ragwalla Assistants addresses these flaws head-on.
Hybrid Search With The OpenAI Assistants API
The OpenAI Assistants API does not natively support hybrid search or metadata indexing, but Ragwalla's implementation does. Hybrid search allows developers to build smarter, faster, and more precise data retrieval systems. Whether manually setting keywords or automating the process with LLM Auto mode, this approach enhances the effectiveness and efficiency of vector-based applications.
Real Use Cases for Cosine Similarity, Dot Product, and Euclidean Distance
Understanding cosine similarity, dot product, and Euclidean distance can be much easier with real-world analogies. These measures each capture “similarity” or “distance” in different ways — direction vs. magnitude vs. straight-line distance . We explore two narrative-style scenarios for each measure, showing when to use them and why.
Choosing Between Cosine Similarity, Dot Product, and Euclidean Distance for RAG Applications
Choosing the right similarity measure depends on your use case. If you’re dealing with text embeddings and want to isolate direction over magnitude, go with Cosine Similarity. When overall vector size or weighting matters, Dot Product shines. And if your model benefits from real geometric distances like clustering points in space — Euclidean Distance is the way to go.
Use Multiple Vector Stores With The OpenAI Assistants API
Use multiple vector stores in an OpenAI Assistants API service to enable parallel querying across each store that each handle 5M vectors—500x OpenAI's current limit.
Introducing Ragwalla: A Wire Compatible OpenAI Assistant
The OpenAI Assistant was a major leap forward for developers, but pitfalls can sink your application and team. The Ragwalla Assistant addresses the challenges related to scale, transparency, support and cost.