SELECTED BLOG POSTS

Salesforce makes an AI landgrab with Agentforce 2.0

Agentforce 2.0 brings substantive technology advances – but it is enough to open up a multi-trillion-dollar enterprise digital labor market for Salesforce?

Hard on the heels of Agentforce 1.0, unveiled just three months ago at Dreamforce, comes the 2.0 version. That’s the astonishing speed at which technology iterates in this new era of AI. So what’s different about Agentforce 2.0? Quite a few things, actually, down in the technology itself, which we’ll get to in a moment.

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Enterprise AI Requires the Fusion of LLM and Knowledge Graph | Stardog

The only way to win with AI in the enterprise is to have the right data strategy, and the key to data strategy for AI is to deploy a platform that offers the best of LLM and knowledge graph.

To get the best results in AI, the enterprise needs a platform that offers the best of LLM and Knowledge Graph, and it needs that combination for two reasons:

Precision. For the most precise results, LLM and KG have to work together: the former to understand human intent and the latter to ground the former.
Recall. For the most comprehensive results, LLM and KG have to work together: the former to handle unstructured data (i.e., documents) and the latter to handle everything else (i.e., structured and semistructured data, i.e., database records).

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Building effective agents Anthropic

A post for developers with advice and workflows for building effective AI agents

Over the past year, we’ve worked with dozens of teams building large language model (LLM) agents across industries. Consistently, the most successful implementations weren’t using complex frameworks or specialized libraries. Instead, they were building with simple, composable patterns.

In this post, we share what we’ve learned from working with our customers and building agents ourselves, and give practical advice for developers on building effective agents.

via: https://news.google.com/

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The Enterprise Tech Year – AI in action

The year in AI use cases shows the breadth of AI impact, across industries and lines of business. But let’s not sugar coat it: successful AI projects have…

The year in AI use cases shows the breadth of AI impact, across industries and lines of business. But let’s not sugar coat it: successful AI projects have not been a cake walk. The good news? Customers that take data projects head-on are seeing results – in AI and beyond.

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