Tag: tech

How AI Agent Tech Stacks Accelerate Productivity

AI agents are quickly shaping the future of work, development, and decision making.

These AI-based software systems are capable of autonomously performing a huge range of tasks with the right prompting from a user, such as conducting research or managing a workflow.

Thanks to their ability to mimic human-like reasoning and observing skills, they can be trained to act as an assistant or thought partner by a human employee, and independently make decisions to achieve the employee’s goals.

AI agents are capable of improving productivity by quite a bit, and therefore expected to play a huge role in the workplaces of the future.

Let’s examine the AI agent tech stack in greater detail below.

Unpacking the AI Tech Stack

While the end user just sees a chat box and a long string of text, behind the scenes there is a carefully constructed tech stack that enables the AI agent to reason, act, and adapt according to the prompts they are fed.

A tech stack is a layered system of tools, and each tool plays a foundational role in making sure the AI agent is able to perform reliably.

Therefore it is essential for developers to understand what is powering their AI agents at each layer, and how they all work together to produce the desired outcomes.

The most critical layer of the tech stack is Data Collection and Integration. Before any action or reasoning can take place, the AI Agent will need to understand the world it is operating in.

The understanding is built off the back of mostly unstructured data. This data is the fuel for multiple use cases, including:

      • training AI models
      • powering a retrieval augmented generation (RAG) system
      • or enabling an agent to respond to live changes in the market

There are some specific tools that are geared to make this data collection and integration process more robust.

For example, Search API is able to surface relevant web content in real time.

Unlocker API is able to bypass anti-bot protections to make sure the AI agent can access public data sources as needed.

Once an agent has access to data, the next layer needed is agent hosting services.

This tool creates the digital environment where all the reasoning, decision making, and actions are able to take place.

In other words, these operating systems provide the infrastructure that turns static models consuming data into a dynamic, autonomous system.

These hosting platforms manage everything from orchestration to execution and make sure agents can interact with APIs successfully.

Developers are using a variety of tools – examples include LangGrap, which helps build multi-step agent workflows, and AWS, which offers infrastructure for managing agents at scale.

As agents become more autonomous, they will begin to require observability tools that help developers monitor performance and debug issues as they arise.

AI agents should not be designed as a black box; developers should always have a clear view into what is happening to make sure the agents are operating safely.

Frameworks are another tool used to maintain this visibility into an AI agent’s actions.

They define how agents are structured, how they reason, interact with tools, and collaborate with other agents.

In other words, frameworks give agents their structure and logic, while still relying on real-time data.

Memory is another very important layer in the tech stack.

Memory systems are responsible for allowing agents to retain context, build long term understanding of the problems they are asked to help with, and remember past conversations.

For example, if a worker is using an AI agent, it would be frustrating to have to feed it context about the workstream all over again each time the agent is used.

Memory systems also enable agents to learn and adapt, but requires high quality input in order to achieve this successfully.

AI Agents in Tech Stacks

There are a few other layers in the tech stack, including tool libraries, sandboxes, model serving, and storage.

Each plays their own important role in the success of an AI agent, but the most important layer is obvious: the initial data that it is fed.

AI agents are only able to reason, plan, and act only if they have access to the right data at the right time.

Without it, even the most advanced AI systems will quickly be unable to provide any relevant help.

The most valuable data source of all is the public web, so it’s important that an AI agent can access it at any time.

Tech Stack Development: AI Agent
Source: Bright Data

Cloud Monitoring: Are You Secure?

The complexity of the modern age serves as a double-edged sword and, as complexity grows, both the benefits and challenges of technology steepen.

Modern innovations such as cloud infrastructure enable people to do even more, but it also raises the minimum skill required to utilize this technology.

This is why it’s paramount for innovations to focus on not only efficacy, but also efficiency and usability.

In recent times, everything is being virtualized and connected to the cloud. 94% of enterprises already utilize the cloud and that number is likely to go up as more and more utility is built into it.

But how accessible are cloud processes?

Presently, the cloud operations market is led by Amazon with “Amazon Web Services” with an estimated 34% market share.

The second and third largest providers are Microsoft’s “Azure” and Google’s “Google Cloud” comprising 22% and 9.5% of market share respectively.

While the first mover advantage is prevalent for providers, the widespread usage of cloud services has been documented as well.

There is 1 exabyte, or over 1 billion gigabytes, of data already in the cloud.

Alternatively, this amount of data would require around 50,000 trees with the traditional pen-and-paper method of storing data.

For aggregate quantities of data, like the amount used commercially, the cloud provides more cost- and time-efficient methods of both storing and retrieving data.

The issue is when growing complexity intermingles with growing accessibility.

A Deeper Look at Your Cloud Operations

Around 4 in every 5 organizations report poor visibility in their cloud operations.

This happens for a variety of reasons ranging from ineffective tools to data being overly spread out.

Most notably, cloud tools are meant for development and security rather than troubleshooting and the plethora of services used provide a single metric each.

Consequently, it’s hard to gather the data relating to a problem and it’s even harder to actually identify a problem.

The measurable downsides include 330% increase in related incidents and 74% of companies reverting to physical alternatives to cloud operations.

Other smaller effects include more frequent outages, poorer application performance, and various delays in troubleshooting.

Network operators, or NetOps, often struggle to combat the inefficacy provided by most cloud operations.

But solutions have begun to arise and cloud monitoring services have become the most popular amongst them.

Introducing Cloud Monitoring Services

The implementation of cloud monitoring services shows a reduced security risk, lower mean time to resolution, and increased perceived business value.

Cloud monitoring helps to mitigate issues of poor visibility by consolidating the previously scattered metrics.

By centralizing this data, not only does it eliminate the need to accumulate data, but it also expedites the problem-assessment process.

As a result, the mean time to resolution (MTTR) mentioned above is reduced by a noticeable margin.

Conversely, cloud monitoring goes beyond mere function and aids with perception.

A business that implements cloud monitoring is more easily comprehensible, which makes a business appear more valuable.

While this seems more speculative, this notion is almost universally agreed upon across physical, virtual, and hybrid environments.

Finally, a more efficient and effective system means there is less room for error and more resources to allocate to security.

As discussed, cloud monitoring can save both time and effort by compiling data and making analysis easier.

However, with the improved cloud monitoring tools, your company is less susceptible to security risks and data breaches.

The improved tools enable real-time troubleshooting, allowing NetOps to solve problems before program and update deployment.

By reviewing and fixing programs before deployment, you significantly reduce the chance of security breaches and other issues.

In Conclusion

Ultimately, the modern age of technology is about optimization and increasing efficacy.

Just like the cloud can be a major improvement on pen-and-paper, cloud monitoring services are currently an improvement on the cloud.

You can vastly improve the experience for network operators, which, in turn, improves the health and longevity of the applications.

Cloud monitoring services go even beyond the traditional benefits and all integration of third-party services for even greater ease of use.

With the ever-evolving nature of both the cloud and cloud monitoring services, businesses without one are sure to be behind the curve.

Those who are able to get ahead with technology can get ahead of their competition.

The Importance of Cloud Monitoring & Why It’s So Hard
Source: Live Action