The leaders of the institutions say that the recipe for artificial intelligence agents is compatible with them with the current operations – not the other way around

Working with one colleague, not a swarm of robots

Block, the parent company with a value of 10,000 Square, Cash App and Peardpay, considers itself in a full discovery mode, after offering an AI’s inter -operating framework, Used nameIn January.

It was a goose It was initially presented Axen explained that Aksen now explains the engineering tasks of software, and is now used by 4000 engineers, with a monthly double. The statute writes about 90 % of code and engineers’ preservation of about 10 hours of work per week by automating the code generation, correcting information and filtering information.

In addition to writing software instructions, Goose works as a colleague in the digital team of a type, pressure SLACK and EMAIL flows, integration through the company’s tools and new agents when the tasks require more productivity and expanded range.

Axen emphasized that the bloc focuses on creating one interface that looks like working with one colleague, not a group of robots. “We want you to feel like you are working with one person, but they are behaving on your behalf in many places in several different ways,” he explained.

Goose in the actual time in the development environment, research, transition and writing, based on Language model (LLM) output, with file reading and writing independently, operating code and tests, refining outputs and fixing the dependencies.

Basically, anyone can create and operate a preferred LLM system, and can be imagined as the application layer. It contains the desktop application interface and command line interface, but Devs can also build a custom user interface. The platform is based on anthropor Form context protocol (MCP), an increasingly common uniform group of application programming facades and finishing points linking agents to data warehouses, tools and development environments.

Goose was released under the Apache Open Open Apache 2.0 (ASL2), which means that anyone can use it freely, modify and distribute, even for commercial purposes. Users can access databrics databases and make calls or SQL inquiries without the need for technical knowledge.

“We really want to reach a process that allows people to obtain value from the system without having to be an expert,” explained.

For example, in coding, users can say what they want in the natural language and the frame will explain this to thousands of code lines that Devs can read from. Block is also to see the value of the pressure tasks, such as reading Oze through Slack, e -mail and other channels and summarizing information for users. Moreover, in sales or marketing, agents can collect related information on a possible customer and its port in a database.

Artificial intelligence agents are not exploited, but human field experience is still necessary

The process was the biggest bottle. Not only can people give a tool and ask them to make it work for them; The agents need the opposite of the operations in which employees are already participating. Human users are not concerned about the artistic spine, instead, the work they are trying to do.

Aksin said that the builders of the builders, therefore, need to look at what employees try to do and design tools to be “literally as possible.” Then they can use this for a chain together and treat larger and larger problems.

“I think we lack a large extent of what they can do,” Axen said of the agents. “It is people and the practical because we cannot keep up with technology. There is a big gap between technology and opportunity.”

And when the industry blocks that, will there remain room for the experience of the human field? Of course, Axen says. For example, especially in financial services, the code must be reliable, compatible and safe to protect the company and users; Therefore, it must be reviewed by human eyes.

“We still see a really important role for human experts in every part of the operation of our company,” he said. “This does not necessarily change what experience means as an individual. It only gives you a new tool to express it.”

A bloc based on the spine open source

Axen pointed out that the human user interface is one of the most difficult elements of artificial intelligence factors. The goal is to make the interfaces simple to use while artificial intelligence in the background is proactively.

Aksen noted that it will be useful, if there are more players in the industry, they merge the MCP standards. For example, “I would like to go just Google and have a general MCP for Gmail.” “This would make my life much easier.”

When asked about Block’s commitment to an open source, he indicated that “we always had the backbone open source,” adding that during the past year the company was “renewed” its investments in opening technologies.

“In a space that moves this quickly, we hope we can create open source governance so that you can be this tool that keeps you even with the appearance of new models and new products.”

GSK experiments with multiple factors in drug detection

GSK is a pioneering drug developer, with a special concentration on vaccines, infectious diseases and oncology research. Now, the company has started applying the multi -agent structure to accelerate the discovery of the drug.

Kim Branson, Gsk’s SVP and international head of AI and ML, said the agents began to transform the company’s product and “completely essential to our business.”

Gsk scholars Branson explained that the combination of the field LLMS with ontology (subject concepts and categories that indicate the characteristics and relationships between them), tools and strict testing frameworks.

This helps them to inquire about giant scientific data groups, planning experiments (even if there is no earthly reality) and collecting evidence via genome (DNA study), protein (protein study) and clinical data. Factors can surface the hypotheses, verify the health of data joining and press research cycles.

Branson indicated that the scientific discovery has come a long way; Sequencies have decreased, and protein research has decreased much faster. At the same time, though, Discovery becomes more difficult than ever where more and more data are collected, especially through wearable devices and devices. Branson said: “We have more continuous pulse data than people ever as a type.”

He pointed out that it may be almost impossible for humans to analyze all these data, so the goal of GSK is to use AI to accelerate repetition times.

However, at the same time, artificial intelligence can be difficult in Big Pharma because often there is no ground truth without conducting major clinical trials; It comes to hypotheses and scientists who explore the evidence to reach possible solutions.

“When you start adding agents, you find that most people have actually did not get a standard way to do so with each other,” Branson pointed out. “This contrast is not bad, but it sometimes leads to another question.”

He said: “We do not always have an absolute fact to work with it – otherwise my job will be much easier.”

He explained that the whole matter is related to reaching the right goals or knowing how to design what could be a biological sign or evidence of different hypotheses. For example: Is this the best way to observe people with ovarian cancer in this particular case?

To get artificial intelligence, he understands that thinking requires the use of ontology and ask questions like, “If this is true, what does x mean?” The field factors can collect relevant evidence from large internal data groups.

Branson explained that the GSK built the eagle -backed language for the zero point that it uses for reasoning and training. “We are building very specific models for our applications as no one else,” he said.

He pointed out that the speed of reasoning is important, whether for the background with a form or deep independent research, and GSK uses different groups of tools based on the ultimate goal. But large context windows are not always the answer, and liquidation is crucial. “You can only play the filling of context,” said Branson. “You can not only throw all the data in this thing and trust in LM to find out.”

Continuous critical test

GSK puts a lot of tests in its agent systems, which gives priority to inevitability and reliability, and often runs multiple factors in parallel with cross verification results.

Branson recalled that when his team started building for the first time, they had a SQL agent that they were running “10,000 times”, suddenly “fake” details.

He said: “We did not see this happening again, but this happened once and we did not even understand why it happened with this particular LLM.”

As a result, his team will often turn on multiple copies and models in parallel with the application of communication and restrictions on tools; For example, two LLMS will completely with the same sequence and GSK scientists will verify it.

His team focuses on active learning rings and collects its internal standards because it is popular for the public often “somewhat academic and does not reflect what we do.”

For example, they will create many biological questions, and record what they think is the gold standard, then the LLM application against it and see how it ranks.

“We are particularly looking for problematic things as they did not succeed or do something stupid, because this is when we learn some new things,” Branson said. “We are trying to use humanity experts where it matters.”

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