Agentic AI is more than a buzzword thrown around in business meetings and strategy sessions—it’s game-changing technology. Versatile, flexible, and beneficial across a variety of industries, this advanced tool might just be the difference between stagnation and success. Discover what agentic AI tools are capable of and determine how they’d fit into your organization today.

What is agentic AI?

As the conversation around AI evolves, so do the tools. Agentic AI, designed to run independently, has become the new gold standard of autonomous technology.

Unlike its predecessors, agentic artificial intelligence is trained to make decisions and plan to achieve predefined goals with little-to-no human intervention. Instead of waiting on a prompt, agentic AI tools take proactive steps to break down specific objectives, identify needs and execute the necessary workflow.

What is the meaning of agentic?

The term “agentic” refers to the fact that this type of artificial intelligence seems to have its own agency. After it is programmed with a goal, the system is self-directed and can take initiative to make whatever decisions are necessary to achieve the desired outcome.

How does agentic artificial intelligence work?

From an outside perspective, agentic artificial intelligence can seem like magic. In truth, this autonomous technology accomplishes its tasks by integrating advanced methods in four specific stages: perception, reasoning, acting and learning.

In the perception stage, AI agents start by collecting real-time data from a variety of sources like databases, APIs and user chats to assess the current situation and determine what information is useful to the task at hand. The agentic AI tool uses that information as the model moves to the reasoning stage to interpret the goals and develop an actionable plan. In this phase, the technology launches a long-term memory system to ensure that tasks remain consistent throughout the rest of the project.

Once the agentic AI tool completes the reasoning phase, it’s time to act. In the third stage, the technology sets up smaller tasks for the model to tackle in a specific order. In some cases, the actions rely on human interaction; in others, they’re completely autonomous. After the actions are fulfilled and logged, the agentic AI tool can enter the final learning stage to continuously improve functionality and effectiveness for future iterations—and the cycle repeats.

Elements of agentic AI:

  • Proactive: Rather than waiting to be triggered by user input, agentic AI tools are able to identify patterns, anticipate needs and take the proper steps to solve problems as they arise.
  • Specialized: In most cases, agentic AI tools are trained for hyperspecialized tasks and scenarios. When they share coordinates with one another to share insights, it enables a deeper level of performance.
  • Adaptable: Agentic AI tools are able to adapt to different domains, environments and industries by adjusting actions based on real-time input. Where standard solutions are built to handle repetitive tasks, they often lack the domain-specific knowledge needed to oversee complex scenarios.
  • Intuitive: By using large language models (LLMs), agentic AI tools can understand underlying business intent and manage ambiguous, intricate information.
  • Collaborative: Agentic AI tools are able to understand shared goals, interpret intent and fulfill actions, making them the perfect collaborative partner for both other artificial intelligence systems and humans.

Why is agentic AI the future?

One thing is clear: agentic artificial intelligence deserves the attention it’s receiving. By drawing from widespread databases and networks and learning from user behavior, this autonomous technology is able to adapt over time and handle multi-step applications in never-before-seen ways. The flexibility of LLMs makes it a more accessible option for users, negating the need for high-tech programming knowledge and making room for more creative tasks, like content generation. When you combine LLMs with traditional AI programming, you get the helpful and precise hybrid approach that agentic AI is known for.

While more passive conversational technology will always have its time and place, nothing compares to the extended reach that agentic AI tools provide. Since they are designed to continuously scan their sources, an agentic program can fetch information and data points in near real-time to improve its strategy, evolve its data and achieve optimization.

Agentic AI is also the closest we’ve gotten to truly autonomous technology. While certain processes may need limited human supervision, most agentic AI tools can operate completely independently. This makes managing long-term goals possible while freeing up valuable time for your human employees to focus on other vital tasks.

5 benefits of agentic AI:

  1. Increased efficiency: Agentic AI can automate tedious and repetitive tasks by analyzing data, setting appropriate goals and executing the final vision without involving human workers.
  2. Enhanced user trust: Agentic AI is designed to provide more personalized and proactive customer interactions, delivering more relevant and error-free support solutions at a faster rate than traditional methods.
  3. Continuous improvement: Unlike outdated automation tools, agentic AI designs rely on feedback loops to optimize their performance, adjust to changing market conditions and up their game over time.
  4. Reduced costs: Agentic AI tools reduce the need for human labor and workflow bottlenecks, enabling organizations to scale more economically and save money.
  5. Secure governance: Agentic AI ensures that every single action aligns with the industry-wide policies and privacy regulations.

What are the different types of agentic artificial intelligence?

AI agents are categorized by how they interact with their surroundings, the processes by which they make decisions and their overall level of intelligence. Each has its own strengths and applications, though they are often deployed together in a multi-agent system to cover that part of the task for which they are designed. Depending on the consulting firm, AI agents can be classified by their internal decision logic or their system architecture, which is broken down into two types: single-agent and multi-agent networks.

Common types of AI agents:

  • Simplex reflex: The most basic form of agentic AI, this tool does not store past information and acts on a fixed set of rules regardless of past experiences or future consequences.
  • Model-based reflex: A more advanced version of the simple reflex AI agent, this technology relies on condition-action rules and incorporates an internal model of the world to understand how past interactions might have impacted the current landscape and to make a more informed decision.
  • Goal-based: Instead of reacting based on predefined rules, a goal-based AI agent considers overall objectives to act with greater foresight and choose whatever action will get it closer to achieving a previously set goal.
  • Utility-based: The AI agent considers a range of outcomes, assigning a utility value to each to determine the best course of action and maximize overall benefits, making it the most effective option for dynamic environments attempting to achieve more than one goal.
  • Learning: Designed to improve performance over time and enhance decision-making abilities, learning AI agents update their behavior based on environmental feedback.

What is the difference between agentic AI and generative AI?

While generative AI and agentic AI can be used in the same workflows, during the reasoning and drafting stages the two tools have entirely different functions. Generative AI, or GenAI, is used largely to reactively create content, whether that be text, code or an image, based on a user-inputted prompt.

Agentic AI, on the other hand, is used to pursue high-level goals in an autonomous, hands-off way. Between reasoning, managing multi-step processes, calling tools and learning from the outcomes, this proactive artificial intelligence technology gets the job done.

What are the main challenges for agentic AI tools?

Agentic artificial intelligence has endless potential to change how your organization operates for the better. But, if handled incorrectly, the autonomous technology can bring myriad consequences to your workflow. All the normal risks of using AI stand, but since the tools are almost entirely machine-run, you’ll need to keep a few additional challenges in mind.

Agentic AI’s technical hurdles

If a set goal is defined too vaguely or an agent fails, the AI program can continuously attempt failing actions, getting trapped in an infinite cycle referred to as “agentic looping.” To avoid this conundrum, you’ll need to implement a form of circuit breaker that forces the agent to halt and hand over control to a human when a predetermined error threshold is hit.

There is also a possibility of losing control over your agentic AI tool. Since the technology is meant to modify its own plans as needed, even a tiny change can trigger a completely new and unpredictable sequence of actions. This can be prevented by breaking down large agents into specific skills and tools, instead of one whole model.

Implementing new agentic AI tools into an organization can be extra challenging if your existing legacy systems are rigid or outdated. Since this style of autonomous technology relies on real-time access and data, you’ll need to upgrade to a more modern, API-driven or cloud native architecture to continue.

Agentic AI’s operational struggles

The actions executed by an agentic AI tool are based entirely on the data it receives. In some cases, whether it’s due to a bug in the program or broken data pipelines, the technology can experience hallucinations and drift away from its original goal. To prevent objective shift, the agentic AI must be given immovable goals, strict execution boundaries and continuous verification systems.

Agentic AI and safety concerns

With new artificial intelligence technology comes questions about safety and vulnerability—agentic AI is no different. In some industries, using agentic tools to generate and manage important reports and documents can affect how consumers view and trust your operation. As long as your company is completely transparent about how the agents are being utilized, it should be enough to soothe your user base.

Since they’re barely touched by humans, agentic AI tools come with a few roadblocks when it comes to cybersecurity. If they are not carefully trained and checked, the program has the potential to risk data leaks, grant unauthorized access and be vulnerable to prompted manipulation. As long as proper cybersecurity frameworks, such as implementing mandatory human approval for high-impact actions and kill switches, are adopted, mitigating these risks is simple.

What is the ROI of agentic AI?

It doesn’t matter whether you’re leading a healthcare organization into a new phase or running a communications firm—any industry can benefit financially from implementing agentic AI into its workplace. With the potential of delivering up to three times the financial return compared to traditional automation, the ROI of agentic AI tools can vary anywhere between 49% and 171%, depending on the way it is deployed and what kind of department it is used within.

How to find a trusted agentic AI partner

When it comes to choosing who to work with on deploying and designing agentic AI into your organization, you’ll want to find someone who knows what they’re doing. We recommend searching for an agency or consulting firm that values the autonomous nature of the technology without neglecting human input and workers. Nothing beats a human-centric design model—even when it’s high-tech.

The best consulting firms will have access to the latest and greatest AI frameworks as well as the ability to seamlessly integrate agents into your system. When you are interviewing potential partners, ask to see proof of their past experiences introducing agentic AI tools into companies within your industry. If they cannot provide a successful track record with similar organizations, they might not be the best fit.

The best agentic artificial intelligence partners have a deeper understanding of the strict regulatory frameworks applicable to your unique operations. To prevent any actions from falling outside of your compliance requirements, they should offer post-launch support and tools to track the agent’s performance and behavior over time. These kinds of guardrails are crucial to the longevity of your organization.

Partner with CGI to discover agentic AI tools for your organization

As a trusted UiPath Diamond Tier Partner, CGI can offer a trustworthy fast track to agentic AI. Connect with one of our consultants to get started on the path to automation today.