How To Use AI Agents To Benefit Your Business
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Artificial intelligence (AI), specifically generative AI (GenAI), is the hottest topic among today’s software developers, as well as the businesses making use of their technology tools. With more money and effort pouring into these areas than ever before, progress has been rapid, and there’s a need to keep up with the state of the art.
AI agents are one of the leading expressions of GenAI as a business tool. These programs, designed to take on multiple tasks and be responsible for automating ever-larger parts of workflows, fit into numerous companies’ plans as they expand their AI investments.
As with any new technology deployment, especially in a hyped and fast-developing area like GenAI, it’s important to get to know what AI agents are and what they do before integrating them into your own company’s infrastructure.
So, to that end, let’s discuss the ins and outs of agentic AI. We’ll help you find your ideal use case by going through:
- The definition of an AI agent and the basic traits of these solutions.
- How AI agents work in a business context.
- How businesses are already doing with AI agents.
- The ways agents intersect with existing data types, like video content.
There’s a difference between using a technology because it’s hot right now and using it because it can truly help you. When you add agentic AI to your workflows, you should make sure you’re taking the latter route.
What Is an AI Agent?
An AI agent is a type of GenAI program that is designed to achieve specific goals using the tools at its disposal. Agentic AI is more autonomous and self-guided than more straightforward algorithms. AI agents “remember” past information and use it to power their future decisions and next steps.
When it’s well-designed and functioning properly, agentic AI requires significantly less human oversight than other types of software. These programs are empowered to analyze facts and make decisions as they pursue the tasks they’ve been assigned.
A quick and functional definition of AI agents is that:
- An AI agent is an application built with generative AI.
- It is driven by goals and empowered to use tools and analysis to accomplish those goals.
- It can automate and take on more complex business processes compared to other GenAI apps.
Not all AI agents are alike in their level of power and autonomy. There is room within the umbrella of agentic AI for solutions that are meant to work alone, those that function with human guidance, and even those that work together with other GenAI apps.
Amid these differences between specific programs, there is a general pattern to the way AI agents work. These programs:
- Receive instructions to achieve a specific goal.
- Observe the situation by ingesting relevant data.
- Develop a plan based on interpretations of the goal and the data.
- Take actions that, when taken together, will achieve the objective.
It’s easy to see the potential in such an open-ended framework. AI agents are flexible enough to apply to a wide variety of business problems, helping companies achieve their day-to-day goals and allowing human employees to achieve more.
Common Types of AI Agents
AI agents have already spun off numerous variants, each describing the level of power and autonomy given to that program. This development has occurred over a longer timeframe than it may first appear. Agentic AI is not a wholly new concept, but rather an existing idea that has gone through rapid development based on a recent surge of interest and progress.
Leading AI agent types currently include:
- Reflex agents: A reflex agent, which can be simple or model-based, is one of the most straightforward kinds of agentic AI. This type of system works from a set of rules, rather than learning and expanding its remit. Simple reflex agents complete the same task in the same way every time, while model-based agents perform data analysis to determine the best way to proceed.
- Goal-based agents: Goal- or rule-based agents use the resources at their command to solve a specific goal. Given an assignment, this type of agentic AI model uses digital tools to address the objective it’s been given, whether that’s to monitor a specific process or ensure an outcome comes to pass.
- Utility-based agents: A utility-based agent is allowed to assess potential courses of action and decide on the best one for its role. These models require extensive data analysis capabilities so they can see which of the possible approaches will have the best effect on the metrics that matter to the company as a whole.
- Learning agents: As the name implies, a learning agent uses past performance to improve itself, learning new ways to achieve better results. These applications build their capabilities in a highly autonomous way, testing out multiple scenarios with their own problem generators.
- Hierarchical agents: These agents are designed to work in groups. By assigning groups of agents to work on a specific problem, project, or function, it’s possible to address especially challenging or complex tasks. These agents are not just large, multi-function agents but separate, independent algorithms that communicate with one another.
With this variety of agent types at their disposal and new development always ongoing, today’s companies can assign an increasing range of tasks to AI agents.
How Do AI Agents Help With Everyday Work?
Thinking about agentic AI on a conceptual level can only take you so far. The real value of these systems, and the real reason to adopt them, comes from the way they can improve your operations.
Today’s agentic AI offerings can deliver outcomes including:
- Automating repetitive tasks: Your employees’ time is valuable. By setting up AI agents to handle repetitive, time-consuming processes like data collection, analytics, or report generation, you can reassign those workers to higher-value tasks while still receiving effective service.
- Supporting human workers: Describing AI agents as autonomous can be somewhat misleading — while they don’t need human intervention, these technologies do work well in collaboration with employees. An AI assistant can respond to a user’s requests to handle specific technical or procedural tasks and can answer complex questions, drawing on internal data resources to find accurate responses.
- Performing advanced analytics and providing insights: An AI agent can serve as a next-generation big data analytics tool. By performing complex calculations and autonomously deciding which analytical avenues will be most impactful and significant, an agentic AI can turn your organizational data into a source of value.
These use cases are very malleable depending on your company’s industry and size. A small company in the financial space will perform different types of analysis compared to a large-scale research and development organization, but AI agents can adapt to either of these scenarios or anything in between.
Benefits of AI Agents vs. Drawbacks
The final decision over whether you should use AI agents now rather than waiting for further tech development comes down to a very simple calculation: the benefits of these systems vs. their weaknesses. As it stands today, these break down as follows:
Considering recent increases in the underlying technology’s power and flexibility, there are likely use cases within your organization where autonomous AI agents will be useful. Many of today’s businesses have already made this leap.
How Are Companies Using AI Agents?
AI agents have begun to make an impact across industries and throughout departments. Companies that are able to provide reliable sources of data, including by extracting clearly machine-readable information from multimedia content like video, can fuel a variety of functions with that content, fueling efficient and autonomous actions.
Some compelling example uses include:
- HR and training: Agentic AI can design customized learning plans for individuals based on their progress and the way they interact with existing training content, such as learning videos and interactive training modules.
- Security and safety: Physical security can represent a powerful AI use case, allowing algorithms to assess large amounts of security video footage and determine when a threat is occurring. Agentic AI can review more information than a human employee for improved coverage.
- Financial services: An AI agent can create the predictive models central to financial functions. By using a self-improving agentic AI workflow, leaders can find novel insights by running the AI solution month after month and quarter after quarter.
- IT and coding: Coding tools incorporating GenAI, like GitHub Copilot, are becoming popular among software developers and IT teams. These utilities allow employees to boost their productivity.
- Marketing: Using GenAI agents to generate parts of marketing campaigns is a way to save human employees’ effort, allowing workers to focus on the highest-touch work and build out their content more quickly.
- Procurement: AI agents in procurement roles can analyze data over time to create highly efficient orders over time and minimize waste.
- Sales and customer service: Customer-facing AI agents can handle a variety of question-answering functions, providing information without requiring human intervention. Algorithms can assess videos or transcripts of customer calls to determine when to escalate a question to a human representative.
- Supply chain: Agentic AI’s ability to handle more complex, multidisciplinary issues and perform sophisticated calculations allows it to add efficiency to everyday logistics and supply chain decision-making.
Organizations today are rich in digital data. As long as you’ve built the infrastructure to make this data available to your AI algorithms, including by extracting meaning from your videos and other multimedia content, you can build out empowering use cases.
Unlocking Video Efficiencies With AI
The idea of using video in tandem with AI agents is no fluke. AI in enterprise video management is a surprisingly strong match, one that revolves around video’s role as a store of rich information. The interaction between video and GenAI takes two major forms, both of which require your organization to use an advanced enterprise video platform.
1. Video Content As Fuel for AI Agents
Extracting data from your videos, learning from text transcripts, imagery, metadata, and multimodal content, allows you to perform a variety of powerful use cases. This could mean:
- Detecting a need for escalation in a customer service video.
- Finding risk factors in security camera data.
- Empowering compliance functions by detecting restricted content in videos.
- Allowing for customized learning by assessing how users are interacting with training videos.
2. GenAI As a Video Management Tool
If your organization uses an enterprise video platform that is, itself, infused with AI video capabilities, you can improve your video management in several notable ways, including:
- Performing more effective contextual searches for content.
- Generating clear automated transcripts and translations.
- Translating raw audio into new languages.
- Summarizing the contents of a video accurately.
- Consulting a virtual video management assistant powered by a large language model.
- Analyzing data and generating insights based on information in videos.
- Tagging and classifying video contents based on recognition algorithms.
Data is too important a resource for you to settle for legacy storage, access, and sharing methods. By using an enterprise video platform like Vbrick’s EVP, you can make sure your use of video data and your forays into advanced AI adoption work smoothly together.
Frequently Asked Questions About AI Agents
What is an AI agent tool?
An AI agent is a generative AI model that is designed to solve a problem. More advanced AI agents are more autonomous and multi-functional in their ability to address their assigned tasks or improve over time.
What can I do with an AI agent?
An AI agent can handle business tasks that would be time-consuming or labor-intensive if handled manually. This can mean automating repeated processes, providing support and answers for human workers or customers, performing advanced analytics, and other jobs across multiple departments.
What are the main types of AI agents?
AI agents come in a variety of forms with varying levels of depth and autonomy. The primary types include:
- Simple reflex agent or model-based reflex agent.
- Goal-based agent.
- Utility-based agent.
- Learning agent.
- Groups of hierarchical agents.
How are AI agents developed?
IT teams can develop AI agents to tackle specific business problems and goals. This means determining their mission, giving them access to data, and testing their performance. These teams can enable continuous improvement by tuning the agents’ algorithms over time or by programming them to self-improve.
What are some good data sources for AI agents?
AI agents are at their best when they have access to organizational data relevant to their task. This can include stored content in various formats, including written content or video, along with real-time input.
Up Your Business’s Intelligence Now
Working with agentic AI is a way to keep up within your industry. These solutions, in their ability to save work for human employees and deliver useful outcomes based on data, represent the promise of generative AI solutions in action.
To ensure you’re ready to make AI agent software work for you, your organization should ensure its data is accessible to these solutions. For example, you can implement Vbrick’s enterprise video platform to turn your video files into fuel for your algorithms.
From HR and training to compliance, governance, and even physical security, video data can empower advanced AI agent solutions. The next generation of enterprise performance can depend on your ability to seamlessly build out GenAI capabilities, with your data as fuel.
Schedule a demo to see how AI solutions can deliver real value for your video management processes.