Agentic AI is generating excitement and being cheered as the next frontier in automation and decision-making. However, beneath the buzz, lies a sobering forecast: Gartner warns that “over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value, or inadequate risk controls.”
With limited coffers and high public expectations, governments don’t have the luxury of following hype and diving into agentic AI projects without first being well-informed. The pressure to innovate is real, but before pursuing agentic AI, it’s essential to ask: Is this the right tool for the problem?
That brings us to a sticking point: the ongoing uncertainty around what qualifies as an AI agent or agentic AI. The terms are used liberally and sometimes interchangeably. For governments seeking to boost productivity, clarity isn’t just helpful, it’s essential to understand and choose the right solutions that will deliver on their goals.
What’s the difference between AI agents and agentic AI?
AI agents Agentic AI
| An AI agent is a sequence of AI models, rules and/or analytic algorithms that execute a workflow with little or no human intervention. | Agentic AI is a broader framework that coordinates multiple AI agents to autonomously pursue and accomplish complex goals. |
An AI agent is a sequence of AI models, rules, and/or analytic algorithms that execute a workflow with little or no human intervention. These AI agents can automate processes, analyze data, and make decisions based on predefined rules and predetermined goals.
AI agents are said to have “agency,” which means they can:
- Act independently.
- Make decisions.
- Pursue goals with a degree of autonomy.
- Interact with their environment, systems, people and processes to shape their decisions and actions.
AI agents vary in their degree of sophistication. Some use large language models (LLMs), while others do not. Some involve decision intelligence, while others do not. That said, it's important to distinguish true AI agents from simple chatbots or LLMs. AI agents surpass both by integrating data and advanced analytics tools to be more adaptable and capable of complex reasoning. Anushree Verma, Senior Director Analyst at Gartner, indicates that many use cases positioned as agentic today don’t require agentic implementations.
Further, the hype around agency has led to many people confusing the terms “AI agent” and “agentic AI.” Unlike AI agents, which are specific, task-oriented components designed to perform repetitive tasks on behalf of a user, agentic AI is a broader framework that coordinates multiple AI agents to autonomously pursue and accomplish complex goals.
AI agents are the tools, and agentic AI is the system that uses those tools to think, decide and act independently.
Think of the many government tasks built on repetitive steps: processing forms, monitoring sensors and responding to routine triggers. From mountains of data waiting to inform real-time decisions to teams bogged down in resource-heavy “busy work,” these are prime opportunities for AI agents or agentic AI systems to step up and deliver value.
How do AI agents actually work?
Now that we’ve unpacked the fundamentals of what AI agents can deliver, it’s essential to know how they function to really determine if they are the right fit for your organization. Take a closer look these five key components of robust AI agents: perception, cognition, decisioning, action and learning.
1. Perception: collecting data ️
An AI agent's foundation is its ability to perceive the world by collecting data from sensors, inputs and databases. The quality and breadth of this data are critical. Accurate, relevant information enables better decisions, while incomplete data can lead to errors. Perception sets the stage for all subsequent actions.
2. Cognition: analyzing information
Once the AI agent gathers data, it processes and interprets it in the cognition phase. Here, the agent identifies patterns, detects trends, and draws insights using analytics, machine learning, linguistic rules, inference, and/or LLMs.
3. Decisioning: determining the best action
In the decisioning phase, an AI agent determines the best course of action based on its analysis and any conditions placed on the agent – which could be designed to include human check-in at customizable intervals. The agent selects the most effective response just as we make choices using available information. A well-defined decision framework is crucial, as poor decisions can have financial, operational, or reputational consequences.
4. Action: executing the decision
After decisioning, the AI agent puts that choice into action. This could mean completing a task, recommending a solution, or triggering a response in another system. This is where it moves from processing to doing, turning insights into real-world results.
5. Learning: improving over time
Unlike traditional systems that need manual updates, AI agents improve over time by analyzing the results of their actions. If a decision works, the agent reinforces that approach; if it fails, it adjusts. This ability to adapt makes AI agents smarter, more efficient, and better aligned with a specific goal over time. Agents can document the improvements and learnings that occur to allow their deployers to track and audit their evolution, allowing for both transparency in decision making and accountability in action.
Unlocking the value of AI agents
AI agents offer governments a powerful edge by automating workflows and processing tremendous amounts of data with unprecedented speed. However, success depends on identifying the right tasks, selecting technology that delivers true agency, and thoughtfully integrating machine intelligence with human know-how.
