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Explore Syncfusion A2UI Composer to create, preview, and refine agent-driven interfaces using prompts, templates, structured A2UI definitions, and reusable Syncfusion components.
Explore how Syncfusion A2UI helps AI agents describe interactive interfaces that applications render with reusable Syncfusion components.
A chatbot can explain data, summarize a screen, or answer questions, yet the application still behaves largely as before: business state lives elsewhere, actions remain disconnecte...
How agentic interfaces are changing what designers control, from interactions to agency, orchestration and attention.Agentic canvas can now brings context from across tools into on...
One description, four renderers. That is the whole argument in a picture.Every assistant draws its own way, and the bill lands on the people who already maintain your button compon...
Most agent projects start the same way: one model, one prompt loop, three or four tools. That's the right place to start, and for a lot of workloads it's also the right place to st...
The way we build interfaces is changing. As AI agents write more of our code, the tools we use to render that code may need to change too.
Your fields can now think. Route any Drupal field automator through a tool-using AI Agent and let RAG pick the answer.
We give coding agents the knowledge to make sound decisions and turn ideas into accessible, production-ready experiences.
Agentic AI systems are moving beyond simple question-and-answer applications. AI agents can reason about tasks, use external tools, inspect files, call APIs, work with databases, e...
Everybody wants to build agents these days  —  the internet is bombarded with stories of developers who built their own productivity agents in a single day. Can enterprises d...
Over the last two years, the industry has moved from simple chatbots and retrieval-augmented generation pipelines to something fundamentally more powerful: agentic systems. These s...
Large agent prompts often begin as a practical shortcut: policies, domain rules, tool descriptions, examples, recovery procedures, and integration notes are placed in one system me...
If you have ever pointed an agent at an enterprise API, you will know how it goes. The response comes back five levels deep, the model spends half its context working out where the...
An AI agent is not defined by how intelligently it talks. It's defined by what it's trusted to do. Give a language model a chat window, and you have an interface. Give it access to...
Coordinating agent capability development with the application it powers The post Where the Agent Development Lifecycle Fits appeared first on Towards Data Science.
Driven Tech, Inc., an AI and Cloud Native Platform systems integrator, today announced the general availability of Lasius, an agent orchestration environment designed to help enter...
AI coding agents are moving software automation beyond code completion toward systems that can inspect repositories, choose tools, edit files, execute tests, and iterate until an e...
When AI knows how to resolve a ticket, teams still need a way to decide how much it can do on its own. Today, that often means choosing between keeping a human in charge of every a...
A chat interface is not an integration. Give agents accurate state, bounded actions and a result they can verify. By Tej Pandya, founder of GrowEasy.ai I think of the next intern...
AI agents extend language models from simply generating responses to carrying out goal-oriented workflows. By combining reasoning, tool use, feedback, and control logic, agents can...
AI agents become useful when they can do more than generate text. The moment an agent can update a CRM, approve a refund, create a purchase order, change a price, or send a custome...
Weekly curated resources for designers — thinkers and makers.“AI coding agents are bringing some of that interaction model back. You can describe an outcome and let the agent decid...
Agentforce’s agent for building, testing and improving agents accelerates the development loop—from business intent to learning from production—with humans guiding the work.
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