Nintex Agent Designer

As you start building your Nintex AI agents, the first step is knowing where to configure and manage them. All agent setup happens in Nintex Agent Designer, a dedicated experience where you define how your agent thinks, behaves, and interacts with tools and data.  For more details and instructions use any of the following resources:Nintex HelpConfigure an Agent blog  (by Andy Hayes)

Configuring an agent is one of the foundational steps in building effective Agentflows. In this short how-to video, you'll learn how to configure a single Agent Action and understand the key components that influence how an agent behaves, accesses information, and returns results.  Whether you're building your first AI-powered workflow or refining an existing Agentflow, this overview will help you understand the core configuration options available. What You'll LearnThis video provides a high-level walkthrough of:Adding an Agent Action to an Agentflow. Understanding the four key configuration areas: Model Tools Knowledge Output Selecting and configuring an AI model and connection. Adjusting model settings such as: Reasoning effort Temperature Defining the agent's: Role and rules Work request Adding and using tools to enable agent actions. Connecting agents to knowledge sources and document stores. Understanding shared context and external vector embeddings. Configuring user mes

A question that keeps coming up: when do I use an agent versus a workflow?It's exactly where most teams get tripped up when adopting AI. Here's a clean mental model and some practical guidance you can actually use.Summary: Use agents to interpret and choose. Use workflows to execute. Use both together for scale and safety. The Core Distinction  Agent Workflow Role Interprets, reasons, decides Executes, routes, enforces Best for Ambiguity, judgment, adaptation Precision, repeatability, governance Output A choice or recommendation A deterministic action  When to Use an AgentUse agent tools when human-like judgment, adaptation, decisioning or choosing can be a solution without needing a human in the loop and/or less effort to build.Signals:The path forward isn't known in advance Inputs are unstructured (emails, documents, messages) Decisions depend on nuance or context Multiple valid next steps exist The

You are invited to join the weekly Agent and Orchestration Office Hours session focused on learning how to design, build, and operationalize AI agents.When? Weekly - Tuesdays 3:00 PM CT (Americas) / Wednesdays 8:00 AM AEDT (APAC)Where? Teams call: Join here!Each session will walk through a specific topic with a short demo and practical guidance you can apply immediately, such as:Build AI agents and understand when to use agents vs. simple actions Embed agents into workflows and orchestrations Design multi-agent solutions and master agents Connect agents to knowledge sources, tools, and external systems Implement guardrails, testing strategies, and human-in-the-loop patterns Deploy agents safely and monitor performance in productionThe office hours are designed to be interactive. Bring your questions, ideas, or scenarios you’d like help with.

AI is on everyone’s radar; the question is knowing where to start? The good news, you don’t need a massive overhaul to begin benefiting from AI. In fact, most companies find success by identifying just one clear use case and running a small instance or project. This introductory blog post – the first in our series on building AI agents for business applications – lays out practical best practices for getting started. We’ll explore why it’s smart to start with small, simple projects (think five steps or fewer), how to focus on straightforward, deterministic tasks for quick wins, and which high-impact, low-complexity use cases (like document processing, translation, or sentiment analysis) are perfect for early AI successes. Finally, we’ll discuss the importance of beginning at the department level and making AI a natural part of your design process. By focusing on clear, actionable steps and avoiding unnecessary complexity, you can dip your toes into AI confidently and set the stage for

Runtime AI brings AI-powered capabilities directly into the execution of a Nintex Workflow or App. Unlike design-time AI, which assists with building workflows, Runtime AI enhances how your live workflows operate by analyzing content, generating summaries, detecting entities, and making light, context-aware decisions as the workflow runs. Runtime AI Actions are lightweight, single-step automations that use large language models (LLMs) such as GPT and services like Azure Document Intelligence to perform specific content generation or analysis tasks. They are easy to configure and fit naturally into workflow execution, allowing you to add intelligence without complexity. All actions are delivered through the Runtime AI Platform Service and are accessible via API for integration across Nintex products. In this release, there are 11 out-of-the-box actions available in Nintex Workflow. VideosAnalyze sentiment Detect language Extract information from document or image Extract text from docum