Generative AI vs Agentic AI | Use Cases, Tool Stacks & Benefits

Agentic AI is one of the hottest topics in the world of enterprise technology. This is leading businesses in all industries to rush to adopt AI agents, in pursuit of efficiency gains and enhanced accuracy within workflows.

However, as with any other new technology, it’s vital that we have a firm grasp of the key concepts that underpin it.

Today, we’re exploring one of the most important elements to this by examining the relationship between generative AI and agentic AI, including what each one is, how they work, when they’re used, and where they overlap.

Specifically, we’ll be covering:

• What is generative AI?
• What is agentic AI?
• Use cases
• Benefits and challenges
• Tool stacks, resources, and solutions

Let’s start with the basics.

What is generative AI?

Generative AI is the more familiar term to most readers. This is a form of artificial intelligence that can create original content in response to a user’s prompt. For instance, generating text, images, video, or code.

Generative AI relies on what are known as deep learning models. These identify and encode patterns in huge volumes of training data to determine the relationships between units of language.

This enables generative AI models to interpret the context and intention behind human users’ natural language inputs and generate a response that reflects this, based on the patterns in their training data.

The key point of interaction here is called a prompt. This is what allows us to tell the model what we want it to do. This could be something relatively generic, like asking it a question on a particular topic.

Alternatively, we might provide more detailed prompts including some of our own data, along with details of a transformation we want it to apply, and a specific format we need the response in.

As we’ll see later, this means generative AI can be deployed in a range of workflow and process automation contexts.

Well-known examples of generative AI tools include the likes of ChatGPT, Claude, and Gemini.

However, it’s important to note that generative AI is capable of tasks beyond simply generating content. Crucially, most production models are able to store previous interactions as memory or context.

This opens up a range of additional uses, as we can essentially communicate with AI tools in a back-and-forth fashion, as though they were human colleagues.

What is agentic AI?

Agentic AI is a somewhat newer concept. This refers to AI-powered software solutions that are able to assess context, perform reasoning, and act autonomously in pursuit of a goal.

One way to think about this at a very high level is that we tell an agent what we want it to do, but it figures out for itself how to do it - using the tools and other resources it has available to it.

In more granular terms, this requires the following capabilities:

• Natural language processing to understand user inputs.
• Autonomous reasoning to identify underlying goals and identify the steps required to reach these.
• Contextual awareness and memory handling to allow the agent to assess the situation and learn from previous interactions.
• Tool and function calling, enabling the agent to execute, coordinate, and manage actions.
• Feedback mechanisms, observability, and other solutions to keep human users in the loop.
• Multi-agent orchestration capabilities, to coordinate tasks that require several AI agents working in tandem.

Crucially, several important aspects of this rely on generative AI. In particular, NLP, reasoning, and tool selection, as well as aspects of other capabilities.

As such, we can think of agentic systems as consisting of one or more LLMs supported by additional componentry that enables them to behave autonomously.

As we’ll see later when we discuss tool stacks, this often relies on purpose-built agentic AI frameworks to simplify the development, management, and deployment of agents, using existing generative AI models.

Ultimately, the goal is to facilitate sophisticated process automations by replicating actions that could otherwise only be taken by a human user.

We’ll explore this idea in more detail in the following section.

Use cases

To fully understand the relationship between generative and agentic AI systems, it’s important to have a clear idea of how and where each is most commonly used.

We’ve hinted at some of the important themes here already, but now it’s time to drill deeper into the key use cases for generative AI vs agentic AI.

Generative AI

As we introduced earlier, the primary use cases for generative AI largely surround generating content, media, or other responses based on a user prompt.

However, what’s helpful here is considering how we can use this capability in a business context.

That is, the use cases for asking ChatGPT to answer a question or draw you a picture in your day-to-day life are pretty self evident, but how can these tools be used to improve processes within enterprises?

One common configuration of this is to utilize generative AI to execute specific tasks or steps within broader workflow automation solutions. Essentially, this allows us to use calls to an AI model in much the same way we would a scripted automation step, responding to trigger logic.

This enables us to apply defined business rules on natural language inputs, rather than structured data, such as form submissions. As such, this is useful within a wide range of business processes.

Some of the key types of actions we might carry out in this way include:

• Data, extraction, transformations, and processing - Extracting key values from submissions or applying other transformations and formatting to facilitate downstream processing.
• Enrichment - Dealing with incomplete submissions by determining values for missing attributes.
• Translation - For submissions in foreign languages.
• Applying logic and rules - For instance, for automatic approvals or other request management workflows.
• Summarization - Providing key insights and action points from submissions to save time for human users.
• Categorization and triage - Applying categorization or prioritization logic to submissions.
• Routing, resource management, and queues - Determining which teams or colleagues a submission should be routed to, based on factors such as skills or availability.
• Analysis - Analytical actions such as predicting trends or performing other calculations.
• RAG use cases - Retrieving information from connected data sources to inform responses to user queries.

Additionally, many businesses deploy generative AI solutions that resemble private, governable versions of consumer tools, such as ChatGPT or Gemini.

The idea here is that, even if they’re not supposed to, many colleagues still utilize publicly available generative AI tools as a form of shadow AI. This introduces security risks, as colleagues may pass sensitive data to tools outside of our control.

So, proprietary or self-hosted models trained on relevant company data can be utilized to provide an alternative, offering AI-powered assistants to colleagues across the organization, in a more secure, effective manner.

Of course, today, a growing use case for generative AI is acting as a basis for agentic systems.

Agentic AI

With that in mind, we can also consider the situations where agentic AI is most useful. As we said earlier, one way to think about this is that agentic systems enable us to automate tasks that would likely otherwise require extensive human action and cognition.

These are characterized by more complex decisions that would be difficult to handle using traditional automation tools, like scripts.

As such, the core use cases are centered on replicating human-centric functions and workflows. Typically, this takes the form of determining which of a defined set of available actions to take in response to an input.

Many of the best examples of this surround internal services workflows, such as ITSM, HR, finance, or others.

In these cases, end users can make requests to an AI agent using natural language, in much the same way they would to a help desk.

Depending on the nature of the request, the agent could then signpost process documentation, take resolution actions, or escalate to a human colleague.

The most ubiquitous use case for agentic AI is undoubtedly IT service management. Part of the reason for this is that end users can simply describe the problem they’re facing, without needing to understand the underlying service portfolio, environment, or technology.

The agent can then determine what sort of service or CI the request relates to and take actions to provide a resolution without necessarily requiring a human service desk colleague.

Additionally, AI agents are often used for back-end processes and workflows. For example, we might have agents capable of monitoring our environment for incidents, identifying anomalies, and taking action to resume normal services.

For further details, check out our guide to agentic AI use cases .

Benefits & challenges

Now that we have a better understanding of the key usages for both generative and agentic AI, it’s also important to consider some of the practical implications of utilizing each.

More specifically, in order to achieve ROI from any kind of solution, we must have a realistic picture of the benefits we expect to see, along with the challenges, limitations, and issues we’re likely to encounter along the way.

Let’s break this down.

Generative AI

Generative AI has already begun to revolutionize almost all aspects of our lives, so it might seem a little odd to rehash its benefits. However, it’s still worth doing so in the context of business usage, especially when comparing standalone GenAI solutions to agentic systems.

Given the use cases we discussed earlier, some of the most obvious benefits we can point to include carrying out more sophisticated automation actions, with lower relative development overheads.

This is largely a result of not having to hard-code or script more advanced logic. Additionally, these types of generative AI solutions are highly maintainable, as it’s relatively easy to alter a prompt or swap out a model.

As such, compared to a more advanced agentic AI solution, relying on generative AI to power automation actions can be a relatively easy way to achieve huge efficiency gains within a wide range of use cases.

However, there are some important drawbacks here, too.

Compared to traditional automation techniques, one obvious one is that we’ll often be reliant on external LLMs for processing actions. As most commercially available models charge on a usage basis, this can quickly get expensive.

Additionally, generative AI tools in and of themselves lack many of the capabilities that agentic systems can offer. After all, agentic systems are largely built around generative models, with extra componentry to enable them the ability to act autonomously.

So, a non-agentic generative AI system will lack the ability to make decisions independently of humans. Unlike agentic systems, they’ll largely follow more fixed logic, which can limit their usability in more complex situations.

Agentic AI

As we’ve alluded to already, the main benefit of agentic AI is that we can more closely replicate human interactions within workflows. That is, creating automation rules that reflect human decision-making and actions.

From a business perspective, this can have widespread benefits. Naturally, agentic AI has the potential to offer huge efficiency gains. One angle to this is reducing the labor burden incurred for internal processes.

By the same token, agentic systems enable us to scale our operations, handling higher volumes of transactions, while also providing faster, more consistent resolutions to end users.

Compared to other automation tools, agentic AI also introduces the benefit of enhanced knowledge retention. As agents learn from outcomes of previous requests, this helps us to de-silo institutional knowledge.

However, there are some hugely important challenges we’ll need to overcome too.

For one thing, agentic AI is still a relatively novel technology. So, many businesses are rushing to implement solutions but finding themselves struggling to establish or even measure ROI.

Part of the problem here is that a huge number of vendors have come to market, alongside workflow-specific platforms introducing their own agentic functionality. As such, many teams face difficulties with agent sprawl, where they purchase multiple, overlapping solutions.

Take a look at our guide to low/no-code AI agent builders to learn more.

AI agents also pose huge challenges at a cultural level, including overcoming internal resistance to change. As many colleagues will naturally be worried about the potential impact of agents on their roles, it’s vital to handle this with clear, open communication.

Tool stacks, resources, and solutions

Lastly, in order to effectively put what we’ve learned so far into practice, it’s vital to understand the tools and other resources we’ll need to account for in order to launch real-world solutions.

Generative AI

As you might expect, the most important decision here is model selection. Specific LLMs are more effective at certain tasks, with common benchmarks testing their effectiveness on things like language or code generation.

Our task is to choose the model that offers appropriate capabilities within a pricing structure that suits the needs of our use case.

On top of this, in order to create the kinds of solutions we described earlier, we’ll need to consider the following elements:

• Model access platforms - services that provide API or UI-based access to interact with models.
• Prompt engineering tools - enabling us to test and refine the prompts that we’ll use to instruct LLMs within workflows.
• Integration and automation tools - allowing us to define logic for when a request to a generative AI model should be triggered and how its response should be handled.
• Data stores - any relevant information or data sources that will be used within our workflow.
• Vectorization tools - enabling us to vectorize data so that it can be used by the model.
• End user interfaces - app screens that enable users to interact with our model by submitting data or triggering workflows.
• Evaluation, observability, and monitoring tools - to make it possible to audit and assess the system’s behavior.

Depending on our more granular needs, which of these we need to prioritize might differ. For instance, if we’re primarily using a COTS model to action specific processes or logic within existing workflows, we might largely rely on native functionality within automation platforms.

Take a look at our guide to connecting Postgres to an LLM to see an example of how this works in practice with Budibase.

Agentic AI

Agentic AI systems will require most of the same capabilities as a strictly generative solution, with additional elements that enable us to create autonomous solutions on the basis of this.

As before, LLMs form the core of this. Whether we rely on one model or several, we’ll need to make the same decisions to balance capabilities and costs.

In addition to this, real-world AI agents typically involve the following components:

• AI agent frameworks - offering tools to coordinate agent behavior, including task decomposition, memory access, tool routing, and multi-agent collaboration.
• Task and workflow runners - for tracking and managing task execution, helping to ensure that actions are carried out reliably and in the right order.
• Knowledge and memory stores - to handle short-term and long-term memory.
• Tool interfaces and integration systems - enabling agents to interact with third-party platforms and services for tool calling, as well as things like authentication and identity management.
• Monitoring and observability - providing visibility and oversight of agent behavior.
• Multi-agent coordination - tools that enable us to develop and manage more complex solutions, comprising multiple, function-specific agents.

Another consideration here is how much abstraction is suitable for our needs, or how much we want to custom-build. So, many teams rely on all-encompassing, low or no-code platforms that offer capabilities within each of these themes.

What’s critical, though, is understanding the respective roles of each of these components of an agentic system and how they interact.

To learn more, check out this resource on AI agent architecture .

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