Autonomous AI Agents

Agent Reliability Engineering: Evaluation, Sandboxing, and Failure Modes

Building autonomous AI agents that work in demos is easy. Building agents that work reliably in production — across edge cases, adversarial inputs, and failure conditions — requires systematic evaluation frameworks, sandboxed execution environments, and explicit failure mode analysis before deployment.

Understanding Enterprise AI Agents: Why They Sometimes Fail

Enterprise AI agents are designed to automate tasks and enhance efficiency. However, they often fall short of expectations due to various challenges. In this card, we'll explore the factors that contribute to their failure, focusing on insights from recent research.

What Are Autonomous AI Agents?

Autonomous AI agents are AI systems that pursue multi-step goals independently — planning their approach, using tools, observing results, and adapting — without needing a human to direct every step. They represent a shift from AI as a question-answering tool to AI as an active, goal-directed actor in the world.

AssetOpsBench: A Bridge to Real-World AI Applications

AssetOpsBench is a new framework designed to connect AI agent benchmarks with real-world industrial scenarios. By offering practical insights into how AI can be applied in industry contexts, it aims to enhance the effectiveness of AI models in operational settings.