Modern language agents need to handle multi-turn conversations, retrieving and updating information as tasks evolve. However, most current systems simply add all past interactions to the prompt, regardless of relevance. This leads to bloated memory usage, slower performance, and poor reasoning on longer inputs that weren’t seen during training. Real-world examples, such as research or…
Google has unveiled Gemini CLI, an open-source command-line AI agent that integrates the Gemini 2.5 Pro model directly into the terminal. Designed for developers and technical power users, Gemini CLI allows users to interact with Gemini using natural language directly from the command line—supporting workflows such as code explanation, debugging, documentation generation, file manipulation, and…
In this Ultra-Light Mistral Devstral tutorial, a Colab-friendly guide is provided that is designed specifically for users facing disk space constraints. Running large language models like Mistral can be a challenge in environments with limited storage and memory, but this tutorial shows how to deploy the powerful devstral-small model. With aggressive quantization using BitsAndBytes, cache…
The Challenge: Scaling Autonomous Agents with RL Autonomous AI agents have been at the forefront of taking computational abilities to various real-world tasks, and reinforcement learning (RL) is the key approach in agent creation. RL involves helping computational agents learn by repeatedly interacting with their surroundings, thereby improving their decision-making processes through the use of…
Why Web Agents Struggle with Dynamic Web Interfaces Digital agents designed for web environments aim to automate tasks such as navigating pages, clicking buttons, or submitting forms. These agents operate by interpreting browser data and simulating user interactions to complete specified tasks. Success in this domain requires an accurate understanding of dynamic content and the…
Redefining Job Execution with AI Agents AI agents are reshaping how jobs are performed by offering tools that execute complex, goal-directed tasks. Unlike static algorithms, these agents combine multi-step planning with software tools to handle entire workflows across various sectors, including education, law, finance, and logistics. Their integration is no longer theoretical—workers are already applying…
In this tutorial, we’ll implement content moderation guardrails for Mistral agents to ensure safe and policy-compliant interactions. By using Mistral’s moderation APIs, we’ll validate both the user input and the agent’s response against categories like financial advice, self-harm, PII, and more. This helps prevent harmful or inappropriate content from being generated or processed — a…
Or: Why “Can we turn off generation” might be the smartest question in generative AI Not long ago, I found myself in a meeting with technical leaders from a large enterprise. We were discussing Parlant as a solution for building fluent yet tightly controlled conversational agents. The conversation was going well—until someone asked a question…
In this tutorial, we walk you through the design and implementation of a custom agent framework built on PyTorch and key Python tooling, ranging from web intelligence and data science modules to advanced code generators. We’ll learn how to wrap core functionalities in monitored CustomTool classes, orchestrate multiple agents with tailored system prompts, and define…
The development and deployment of advanced AI systems increasingly depend on flexible, robust orchestration layers that bridge diverse models, tools, and resources. IBM’s MCP Gateway addresses this need by providing a FastAPI-based gateway for the Model Context Protocol (MCP), offering a unified interface to scale and manage the modern AI toolchain. This article explores MCP…