What is Agentic AI?

​It seems like suddenly, everyone’s talking about agentic AI – but not everyone has a firm grasp on what it means. In this blog post, we’ll define the term and examine the technology’s impact for software development teams and organizations looking to take advantage of this evolving technology.  

So, what exactly is agentic AI? 

Agentic AI refers to autonomous systems designed to perform tasks on behalf of a user or another system. These agents are capable of making decisions and taking actions independently, often by leveraging technologies such as large language models (LLMs), natural language processing (NLP), and machine learning (ML). Their agentic nature means they can plan, reason, and adapt in pursuit of goals, often with minimal human oversight. 

What are the core components of an agentic AI system? 

Agentic AI systems must include the following capabilities: 

• Goal management: The system must be able to understand and pursue a goal. That goal may be generated internally or user-provided. An agent knows what it’s trying to achieve and can break big goals into smaller, manageable tasks.
• Decision-making and planning: It must be capable of making choices independently, deciding how to achieve a goal, even if that involves breaking it into steps or adapting along the way. Agentic AI figures out the best steps to take using logic, reasoning, or learned strategies.
• Feedback loop (perception-action cycle): The agent needs to act based on its decisions, observe the outcomes, and adjust accordingly—this loop is what gives it agency rather than being a one-shot tool. An agentic AI system learns from outcomes, adjusting its behavior based on what’s working and what’s not. This helps it become smarter and more useful with each run.

Some additional components are typically present, but aren’t essential. These include:

• Language skills (LLMs & NLP): Agentic AI, powered by LLMs, uses natural language to understand instructions, ask questions, and communicate results. 
• Tool use: The system can interact with other tools, websites, APIs, or software to get a job done.
• Memory: Agents remember past actions, conversations, or facts to stay consistent and improve over time. This memory can be short-term (for the task at hand) or long-term (for future use).
• Safety and alignment guidelines: Agents typically have some means to keep actions within ethical, legal, or operational bounds. These can include value alignment mechanisms, guardrails, or human-in-the-loop systems.

How does agentic AI differ from generative AI (Gen AI) and traditional AI?

Traditional AI is usually trained to do one thing really well. It reacts to inputs but doesn’t plan or make decisions — it relies on human prompts or fixed logic to operate. Traditional AI is great for pattern recognition and tasks like filtering spam, recommending related products, or recognizing  images. It follows static rules and doesn’t act autonomously or create anything new. 

Gen AI creates something in response to a user request, such as text, an image, a piece of software code, a video or audio. Taking the pattern recognition power of traditional AI further, Gen AI draws on deep learning models, extensive training data and natural language understanding to interpret user prompts and produce the requested output. Once it has responded to the initial prompt, it won’t take any further action without additional prompts. Gen AI, such as an LLM, may serve as a component of an agentic AI system.

Traditional and generative AI are both typically reactive, relying on human prompts or input. Agentic AI is more proactive, able to work toward a goal and complete multi-step processes autonomously. It will loop, retry, revise and adapt until it meets the goal, using a variety of tools to guide its decisions. 

What are the differences in the data and training methods needed for different types of AI?

AI training data requirements vary in terms of structure, specificity and scale depending on the intended use case. Different types of AI benefit from different training methods – again, the best method often depends on the desired use case or output. 

Traditional AI relies on labeled, structured datasets. Data must be clean and well-annotated; it is typically domain-specific and narrow in scope. The most common training method is supervised learning: the model learns from input-output pairs. Unsupervised, rule-based systems are also common, as traditional AI is typically designed to perform one specific task that follows a repeatable workflow. 

Generative AI requires much larger, broader unstructured datasets. Think books, websites, libraries or images or code… volume is important. Depending on the use case, diversity may also be a factor. For some internal applications, diversity may not be a priority, but if the app will serve a broad audience, training data should reflect that diversity to reduce the possibility of bias. Gen AI typically relies on self-supervised learning for initial training, then human input guides fine-tuning and reinforcement learning. Humans rank model outputs, teaching the model to optimize for helpful, safe, high-quality responses.

Agentic AI learns from structured, goal-oriented data, such as task logs, workflows, and records of historical actions. Agentic AI may also train on synthetic or interactive data from simulated environments, as well as feedback signals. Training combines fine-tuning of generative models with reinforcement learning, behavioral cloning, and training on tool use. Behavioral cloning teaches AI by example — copying what experts do, instead of figuring it out through trial and error. It’s often the first step in building agentic systems that can later improve themselves with more advanced techniques like reinforcement learning or real-time feedback. Iterative feedback and goal-based evaluation are also essential in training agentic AI. 

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What’s the difference between agentic AI, an agent and an agentic workflow?

While these terms are closely related, they describe different layers of autonomy and structure within AI systems.

• Agentic AI is the overarching concept — it refers to a type of AI system that exhibits specific behaviors and capabilities. Agentic AI systems are autonomous, proactive, and goal-directed. They often include components like planning, memory, tool use, and feedback loops that enable them to operate independently and adaptively.
• An AI agent is an individual unit within such a system — a single autonomous entity designed to achieve a specific goal. Agents carry out tasks such as generating content, retrieving data, or executing actions. A chatbot, code assistant, or workflow bot are all examples of agents. You can think of agents as the building blocks of more complex AI systems.
• An agentic workflow is the structured process or sequence of actions that the system (or multiple agents) takes to complete a larger goal. Agentic workflows often chain together multiple agents, allowing them to collaborate, hand off subtasks, and adapt dynamically to changing circumstances.

What is the impact of agentic AI? 

Agentic AI doesn’t just generate content—it gets things done. By enabling systems to think, plan, and act with autonomy, agentic AI has the potential to reshape how we work, build, and interact with technology. By automating complex, multi-step workflows, agentic AI can boost productivity while reducing the need for manual task coordination. It can free up humans for more creative or strategic thinking. 

Agentic systems can string together tasks without needing constant human input. This enables end-to-end automation. Self-operating systems that can dynamically collaborate have tremendous potential for more adaptive and flexible problem-solving. Like any system, however, there are risks. Agentic AI raises new questions about trust, safety, and control. For example, an agent tasked to optimize software development spend could come up with a great plan – or it could shift budgets in ways that ultimately reduce overall quality and harm user experience.  

What are some of the risks of agentic AI?

While agentic AI is incredibly powerful, its ability to act autonomously introduces a new category of risks that go far beyond traditional or generative AI systems. Because agentic AI systems can operate independently, there’s a risk they could make decisions without human review, or move faster than humans can monitor. They may also operate in ways that weren’t intended – especially if goals are poorly defined or misunderstood. 

Because they often integrate with other tools, agentic AI systems raise concerns about what could happen if they access, alter or expose sensitive data. They could also potentially make changes in the wrong system or chain tools in unsafe ways. And with the ability to work rapidly, by the time problems are detected, damage can really add up. 

To mitigate risks, organizations should focus on: 

• Clear task boundaries and permissions
• Human-in-the-loop oversight
• Audit trails and decision logs
• Safe defaults and sandboxed environments
• Strong access control and rate limiting for tools/APIs

With the agentic AI market expected to reach $126.89 billion by 2029, it’s no wonder many organizations are investing in the technology. Human oversight and comprehensive testing while developing these applications remains critical. Learn more about how Applause can help. Contact us today.