A Practical Guide to Building AI Agents

Build AI agents with the Mistral Agents API

How to Design Architectural Guardrails Around AI Agents

The article "How to Design Architectural Guardrails Around AI Agents" discusses essential agent design patterns that data engineers should understand. It emphasizes the importance of establishing clear architectural boundaries to ensure AI agents operate safely and effectively within larger systems. The post was originally published on Towards Data Science.

AI Agents Are Here. What Now?

Agentic Systems: A Practitioner's Guide to 6 Advanced Architectural Patterns

The article "Agentic Systems: A Practitioner's Guide to 6 Advanced Architectural Patterns" discusses six production-oriented architectures designed to build autonomous, reliable, and scalable large language model (LLM) agents, moving beyond basic ReAct loops. It provides a practitioner-focused guide to implementing these advanced patterns in real-world systems.

A Framework of User Experience Principles for Human-AI Agent Interaction in the Workplace

arXiv:2607. 19941v1 Announce Type: cross Abstract: As AI agents become integral to business workflows, establishing guiding user experience (UX) principles is crucial for ensuring user trust and successful adoption.

Practices for Governing Agentic AI Systems

New tools for building agents

Defining AI Agents: A Compendium of Criteria, Metrics, and Benchmarks

The article "Defining AI Agents: A Compendium of Criteria, Metrics, and Benchmarks" surveys the lack of a standard definition for AI agents and organizes this ambiguity into five dimensions: environmental interaction, learning and adaptation, autonomy, goal‑directed behavior, and temporal coherence. It reviews how each dimension has been conceptualized in prior work and compiles the metrics, benchmarks, and evaluation frameworks used to assess them. The authors also introduce the Agent Compendium, a public digital resource that extends these evaluation methods, aiming to provide a common structure for evaluating and comparing agent capabilities across AI systems.

Emergence of Agentic AI: A Review on Evolution, Background, Working Principles, Applications, Adoption Factors, and Future Research Directions

The article reviews the emergence of Agentic AI, covering its evolution, theoretical foundations, working principles, and architectural aspects. It surveys recent scholarly contributions across various domains, highlighting real‑world applications, current research findings, and existing challenges. The review also proposes a framework for stakeholder adoption and outlines future research directions to guide researchers and practitioners.

How to manage AI investments in the agentic era

Learn how enterprises can manage AI investments in the agentic era by measuring useful work per dollar, improving efficiency, and scaling high-value workflows.

Towards Agentic AI Governance: A Preliminary Assessment

arXiv:2607. 07612v1 Announce Type: cross Abstract: Artificial intelligence is rapidly evolving from generative systems to agentic AI capable of autonomously planning and executing tasks.

Orchard: An open framework for scalable agentic AI

Orchard is an open-source framework for the research community to train and evaluate AI agents across task types. It reduces complexity while supporting strong performance from smaller models by enabling researchers to reuse the same infrastructure.