Agentic AI Tools for Building and Managing Agentic Systems
The recent emergence of large language models (LLMs) and generative AI has shown that there’s a lot of potential to create machine learning tools that would make different workflows more efficient and less tedious. While these AI models are quite powerful, they nevertheless have some limitations in taking on more complicated tasks where more complex and autonomous decision-making is required.
That’s where agentic AI can step in. Characterized as having the ability to take self-directed, proactive actions toward achieving goals without direct human supervision, agentic AI systems are designed to adapt and respond independently to ever-evolving situations where the context may change over time — all with minimal human intervention.
According to Shengran Hu, an AI researcher at the University of British Columbia and Vector Institute, it is this ability to execute complex tasks requiring higher-level reasoning, planning, and interaction that makes agentic AI distinct from traditional foundation models (FM) like GPT-4 and Claude.
“Traditional AI typically focuses on a monolithic model query, where we expect to receive a final answer from the AI system in a single forward pass,” explained Hu. “In contrast, agentic AI systems leverage the capabilities of foundation models to understand and reason through natural language, allowing them, as compound agentic systems, to solve complex tasks through iterative processing using language as unified representations of information. Techniques like planning, reasoning, reflection, and tool use enable agentic systems to tackle real-world tasks more flexibly and effectively.”
Agentic AI is still a relatively new field, but there is already some discussion around best design practices, as well as tools that developers can use to build agentic systems — whether it’s for larger enterprises, or for smaller organizations that need something that is easy to set up and deploy.
Here are several agentic AI tools to get you started.
Beam
Offering enterprise-grade solutions for agentic process automation, Beam helps users to build, manage and test their AI agents via an AI Agent Hub. It also provides pre-trained templates and customizable modules, both of which it claims are secure enough to be used even in the healthcare and insurance industries. Beam’s goal is to help clients increase efficiency by automating manual workflows while easing integration with any other tools that may already be in use.
PixieBrix
PixieBrix is a low-code browser extension that allows users to customize and automate web applications. It’s been likened to a gaming mod but for web apps. Users can build “bricks” with a simple editor that adds extra functionality to web apps (like buttons, overlays or forms), or configure them to trigger an action (like filling out a form automatically with client info).
Using a simple editor, users can begin with a “starter brick” (such as a button, menu or form), which can then be chained together with other “bricks” that perform various functions (collecting input, extracting data, or integrating with third-party apps). Pre-built “bricks” are also available via the PixieBrix Marketplace, and mods can be shared across teams and deployed at scale to help streamline workflows, boost productivity and efficiency, and reduce development costs by improving existing apps.
AutoGen Studio
Currently under active development, Microsoft’s AutoGen Studio is a user-friendly, open source option that runs on Microsoft’s LLM orchestration framework, AutoGen. It allows developers to quickly prototype AI agents using a low-code interface, with the aim of simplifying the process of building and managing complex workflows with multiple agents.
While the company cautions that AutoGen Studio may not necessarily become a full-fledged product anytime soon, it nevertheless offers developers an easy way to test their AI agents with an easy drag-and-drop interface, as well as a library of reusable components to customize their agentic workflows.
AgentOps
This agent observability platform enables developers to build, monitor and optimize AI agents. It is designed to be easily implemented and integrated with well-known AI agent frameworks like LangChain. AgentOps can be automatically set up to track and log AI agents’ run data, thus facilitating performance tracking, error detection, debugging, and cost management.
Hurdles To Be Aware Of
Despite all the hype, researchers like Hu emphasize that there are still hurdles in this emerging space.
“A major challenge in developing agentic AI systems is identifying effective building blocks and constructing systems by combining these blocks appropriately for various applications,” said Hu. “There are numerous potential components (such as planning, reasoning, memory, tool use, etc.) in agentic systems, and even more possible ways to combine them for specific use cases. The community is investing substantial effort in this area, but it often requires domain-specific manual tuning and considerable development time for each application.”
To tackle the problem, Hu and his team propose what they call automated design of agentic systems (ADAS), a process that automatically invents novel building blocks to design powerful agentic systems, where new and improved agents are “discovered” by a “meta-agent”, creating a “self-referential algorithm where AI agents invent new AI agents.”
With initial findings showing that new agents can significantly outperform their hand-designed counterparts, this suggests that AI agents might someday also have the capability to continually learn and self-improve, and may very well be the next step forward as agentic AI evolves.
