agentic ai

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Agentic AI in finance speeds up operational automation

In finance, achieving operational automation by integrating agentic AI requires a data-centric foundation to drive real value. Financial infrastructure provider SEI has engaged IBM to modernise its internal operations via AI and automation. The joint initiative focuses on process redesign and targeted system updates to deliver consistent client experiences, building a modern and data-enabled foundation […]

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How to Build a Risk-Aware AI Agent with Internal Critic, Self-Consistency Reasoning, and Uncertainty Estimation for Reliable Decision-Making

In this tutorial, we build an advanced agent system that goes beyond simple response generation by integrating an internal critic and uncertainty estimation framework. We simulate multi-sample inference, evaluate candidate responses across accuracy, coherence, and safety dimensions, and quantify predictive uncertainty using entropy, variance, and consistency measures. We implement risk-sensitive selection strategies to balance confidence

How to Build a Risk-Aware AI Agent with Internal Critic, Self-Consistency Reasoning, and Uncertainty Estimation for Reliable Decision-Making Read More »

ByteDance Releases DeerFlow 2.0: An Open-Source SuperAgent Harness that Orchestrates Sub-Agents, Memory, and Sandboxes to do Complex Tasks

The era of the ‘Copilot’ is officially getting an upgrade. While the tech world has spent the last two years getting comfortable with AI that suggests code or drafts emails, ByteDance team is moving the goalposts. They released DeerFlow 2.0, a newly open-sourced ‘SuperAgent’ framework that doesn’t just suggest work; it executes it. DeerFlow is

ByteDance Releases DeerFlow 2.0: An Open-Source SuperAgent Harness that Orchestrates Sub-Agents, Memory, and Sandboxes to do Complex Tasks Read More »

Andrew Ng’s Team Releases Context Hub: An Open Source Tool that Gives Your Coding Agent the Up-to-Date API Documentation It Needs

In the fast-moving world of agentic workflows, the most powerful AI model is still only as good as its documentation. Today, Andrew Ng and his team at DeepLearning.AI officially launched Context Hub, an open-source tool designed to bridge the gap between an agent’s static training data and the rapidly evolving reality of modern APIs. You

Andrew Ng’s Team Releases Context Hub: An Open Source Tool that Gives Your Coding Agent the Up-to-Date API Documentation It Needs Read More »

Anthropic Introduces Code Review via Claude Code to Automate Complex Security Research Using Advanced Agentic Multi-Step Reasoning Loops

In the frantic arms race of ‘AI for code,’ we’ve moved past the era of the glorified autocomplete. Today, Anthropic is double-downing on a more ambitious vision: the AI agent that doesn’t just write your boilerplate, but actually understands why your Kubernetes cluster is screaming at 3:00 AM. With the recent launch of Claude Code

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Agentic AI in health care and life sciences: autonomy, accountability and the architecture of trust

With all the change that’s happened in the past decade, a few key things remain the same across health care and life sciences. Clinical trials remain the engine behind every new therapy, care delivery systems determine whether patients receive timely and effective treatment and health care payers must steward finite […] The post Agentic AI

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Andrej Karpathy Open-Sources ‘Autoresearch’: A 630-Line Python Tool Letting AI Agents Run Autonomous ML Experiments on Single GPUs

Andrej Karpathy released autoresearch, a minimalist Python tool designed to enable AI agents to autonomously conduct machine learning experiments. The project is a stripped-down version of the nanochat LLM training core, condensed into a single-file repository of approximately ~630 lines of code. It is optimized for execution on a single NVIDIA GPU. The Autonomous Iteration

Andrej Karpathy Open-Sources ‘Autoresearch’: A 630-Line Python Tool Letting AI Agents Run Autonomous ML Experiments on Single GPUs Read More »

Building Next-Gen Agentic AI: A Complete Framework for Cognitive Blueprint Driven Runtime Agents with Memory Tools and Validation

In this tutorial, we build a complete cognitive blueprint and runtime agent framework. We define structured blueprints for identity, goals, planning, memory, validation, and tool access, and use them to create agents that not only respond but also plan, execute, validate, and systematically improve their outputs. Along the tutorial, we show how the same runtime

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OpenAI Introduces Codex Security in Research Preview for Context-Aware Vulnerability Detection, Validation, and Patch Generation Across Codebases

OpenAI has introduced Codex Security, an application security agent that analyzes a codebase, validates likely vulnerabilities, and proposes fixes that developers can review before patching. The product is now rolling out in research preview to ChatGPT Enterprise, Business, and Edu customers through Codex web. Why OpenAI Built Codex Security? The product is designed for a

OpenAI Introduces Codex Security in Research Preview for Context-Aware Vulnerability Detection, Validation, and Patch Generation Across Codebases Read More »

Google AI Releases Android Bench: An Evaluation Framework and Leaderboard for LLMs in Android Development

Google has officially released Android Bench, a new leaderboard and evaluation framework designed to measure how Large Language Models (LLMs) perform specifically on Android development tasks. The dataset, methodology, and test harness have been made open-source and are publicly available on GitHub. Benchmark Methodology and Task Design General coding benchmarks often fail to capture the

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