AI Agent Development

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What Is AI Agent Engineering? A Complete Guide to Building Intelligent AI Agents

agentic ai, ai, AI (Artificial Intelligence), AI Agent Architecture, AI Agent Development, AI Agent Engineering, AI Agents, AI Automation, AI in Automation, AI in Engineering, AI orchestration, AI Tools, Artificial Intelligence, Enterprise AI, enterprise AI agents, Generative AI, Intelligent Automation, Multi Step AI Workflows, Spritle Blog

A chatbot waits for your question. An automation waits for a predefined trigger. An AI agent can take a goal, figure out what needs to happen, use the right tools, make decisions along the way, and take action. That difference is changing how enterprises think about software. Instead of building applications that only respond to […]

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Stop managing infrastructure: A new way to deploy AI agents and models

ai, AI (Artificial Intelligence), AI Agent Development, AI deployment, Artificial Intelligence

Standing up an agent as a production service on Kubernetes means five YAML files, a few hundred lines between them, and (in most enterprises) a ticket in someone else’s queue. On the Workload API it means one spec file, one command, and about five minutes to a live URL. No manifests, no kubectl, no namespace,

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Antigravity CLI autocompleting the DataRobot skills slash commands

DataRobot for Developers — integrating with the Google Antigravity CLI

ai, AI (Artificial Intelligence), AI Agent Development, Artificial Intelligence, Gemini, mcp, skills

Antigravity CLI is the newest agentic coding CLI from Google, replacing the now-deprecated Gemini CLI. It inherits the asynchronous subagent model that makes Antigravity stand out from the field, syncs bidirectionally with Antigravity Desktop, and is optimized for speed on Gemini 3.5 Flash. DataRobot ships a full plugin for Antigravity CLI directly from the same

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Build with Cursor and deploy production-ready AI agents on DataRobot

ai, AI (Artificial Intelligence), AI Agent Development, Artificial Intelligence, cursor, skills

Cursor has changed how developers write code. The agent mode is good: you describe what you want, it reasons through the problem, picks the right tools, and ships working code. For greenfield projects and standard libraries, it works smoothly. Where it gets harder is when you’re building agents on a specialized platform with its own

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Build an agent that writes its own tools

ai, AI (Artificial Intelligence), AI Agent Development, Artificial Intelligence

The third post from Build Club, our weekly live build session. The companion GitHub repo can be found here, docs here and you can try the agent live in the hosted playground. Your agent framework is not the bottleneck. The bottleneck is that every new external system your agent needs to talk to requires another

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Build a digital twin agent (with guardrails)

ai, AI (Artificial Intelligence), AI Agent Development, Artificial Intelligence

The second post from Build Club, our weekly live build session. A companion GitHub repo can be found here. Your inbox is not the problem. The problem is that you are the person other people are waiting on. Some of those messages need you specifically. Most of them need an answer you have already given

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Four developer workflows using DataRobot Skills, MCP, templates, agent assist, and LLM Gateway.

DataRobot for Developers: Skills, MCP, and the agentic developer surface

ai, AI (Artificial Intelligence), AI Agent Development, Artificial Intelligence, cursor, mcp, skills

You shouldn’t have to leave Cursor to build, deploy, or monitor a production-grade agent. You can wire together LangChain, a vector DB, a monitoring tool, and a deployment pipeline yourself, but you’ll spend more time on that plumbing than on the agent itself. DataRobot is the shortcut. It now lives where you build, integrating directly

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A playbook to run an agent Build Club

ai, AI (Artificial Intelligence), AI Agent Development, Artificial Intelligence

This is Build Club. We’ve been running it for two months. It is the single highest-signal hour of our week, and it is genuinely easy to copy. Every Friday afternoon, twenty-something DataRobot employees pile into a Google Meet. Someone shares their screen. They start typing. There are no slides, no demo script, no agenda beyond

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