AI agents are on everyone’s lips today, and not merely as a story about new technology but an actual tool for transforming and automating work processes that is being implemented everywhere right here and now. According to a recent study, 82% of executives plan to integrate AI agents within the next three years, which highlights a substantial switch to AI agents.
But what does this mean for the automation of work processes and established SaaS solutions that have long been the mainstays of businesses? It’s time to talk about it.
From “follow the rule” to “attain the goal”: strategic automation
First, we do a small throwback to how automation in general worked earlier and how it works now. Traditional automation works according to the logic of “if event → then action,” and there can be many similar chains in the automation process. The main task of automation is to bring the entire process to a preset goal. Each “if–else” is thought out in advance, as is the final goal.
However, in conditions of rapid change, such rigid logic ceases to be effective. AI agents, in turn, act differently since they adapt to the context, analyze the data provided to achieve a pre-established business goal, and do not merely simply execute pre-defined rules. This enables AI agents to effectively respond to unpredictable situations under constantly changing conditions.
Workflow control: complexity that requires autonomy
While all of this good on paper, how does this correlate with how marketing works? Modern marketing workflows have dozens of conditions, channels, branches, and scenarios. It is difficult for marketers to control and update them in a timely manner; consequently, there is significant room for errors.
AI agents come to the rescue because they can autonomously manage such complex and branched processes, adapt the sequence of actions in real time, and optimize each step. For example, systems such as Reteno have already implemented “One-from-many” blocks, where the agent independently selects the best email option based on user behavior; doing this manually is a big headache and can take time.
Content as a variable, not an artifact
Smart automation with AI agents is good, but remember that successful marketing is difficult without good content.
In a world of constant competition for user attention, content can’t be static. AI agents must not only manage logic but also generate, test, and modify messages for each channel. This involves interacting with several types of agents: content generation, segmentation, personalization, and A/B testing. In a single marketing campaign, each agent performs its function and has access to structured data for effective work and the generation of content relevant to the context. Those who can quickly adapt and generate quality content using AI tools can outperform those who exclusively rely on their own capabilities. However, it’s a pretty tough task even for AI to handle alone. That’s where subagents come into play.
Subagent architecture: role distribution instead of universal intelligence
As mentioned earlier, marketing workflows are a collection of complex conditions, processes, and scenarios that need to be covered. Such complexity of marketing requires not one “omnipotent” AI that will be the jack of all trades, but many different subagents that take control of their specific tasks. For example, a few of the possible main tasks for each individual subagent are listed below:
- email list segmentation based on various criteria;
- text generation for various scenarios, tones of voice;
- selection of sending channel and time for maximum deliverability and open rates;
- optimization through A/B tests to identify the most effective content options;
- analysis of the effectiveness of past marketing campaigns with the identification of patterns and regularities for generating adjustments in the current marketing course.
This approach reflects the architecture of Claude subagents, which can be used not only to solve technical problems but also in the marketing plane. At its core, the main agent delegates subtasks to specialized subagents, collects their results, and makes a generalized decision about what to do next.
A few such subagents already fully help marketers in Reteno, Stripo, and Claspo, where the foundations of this logic have already been laid.
- in the Stripo email design platform, there are AI blocks that are subagents for content generation (text, images, and much more);
- in Reteno, the omnichannel platform, there is a subagent that selects a message option for maximum relevance in personalized communication with the audience;
- in Claspo, the online popup builder, there are subagents that adapt popups to the behavioral patterns of the audience.
Why subagents are your go-to approach
When each process has its own subagent, it may appear too complicated; so why bother implementing these subagents? A few significant benefits impact your automation success, both now and in the long term.
Scalability
The world is changing, industries are transforming, and you should always be ready to adapt to these changes. Expandable automation capabilities are a guarantee that your workflows will not be disrupted in the future. This is the advantage of subagents. You can already have a set of subagents, where each controls its own task stack. Moreover, nothing prevents you from adding new subagents for new tasks if such a need arises. At the same time, implementation is easy and does not need the restructuring of an already established mechanism.
Flexibility
Each business is unique, and the way it builds its processes, how it sets goals, and what resources it possesses to achieve these goals can’t be laid out into common frameworks. Due to the fact that each subagent is a separate small tool, each subagent learns and adapts to its tasks differently. All this takes into account the individual characteristics and needs of the business. By introducing subagents, you do not adapt to them, but they adapt to you.
Explainability
I hope you haven’t forgotten that people still participate in everything described above. At a minimum, the analysis of the results of each specific subagent’s work and the assessment of their effectiveness is the responsibility of people. The readability and explainability of the decisions made are what subagents stand out for. Each subagent can undergo a thorough analysis to ascertain which decision was made by which subagent and why. In this manner, you can determine whether the subagent correctly understands the goal set for it and in what ways it is attempting to achieve this goal.
SaaS as a platform for agents, not only for people
Everything described above—agents, subagents, and the results of their work—do not function in a vacuum. They need a platform, an environment in which all the operations take place.
We are all accustomed to SaaS platforms, each of which is aimed at solving specific problems, where the platform is a tool and the marketer is a craftsman who uses this tool to achieve a final result. However, with the advent of AI agents, the hierarchy is undergoing significant changes.
In this approach, SaaS is transformed into a coordination hub for agents and subagents. Platforms cease to be merely a set of functions, but create an environment where roles are clearly divided, interaction is structured, and the result is controlled and predictable. In essence, the AI agent becomes a new type of SaaS platform user. It does not click buttons but gives instructions on how the platform executes actions. Consequently, the requirements for SaaS platforms to suit people shift to the ones that suit AI agents; these requirements are listed below:
- the API should be the main interface because the agent or subagent needs to interact directly with the platform—without an API, two programs can’t interact;
- the logic of the SaaS system should be predictable and explainable;
- documentation for the SaaS product should be machine-readable (strict syntax, unambiguity, consistency, and machine accessibility);
- the content that subagents will interact with in the future should be modular and manageable, thereby ensuring that agents have something to work with.
Those products that adapt to AI agents will become the infrastructure of the future. Those that do not do so may lose relevance due to their high dependence on human inputs.
Will agent-based AI replace traditional SaaS applications?
Against the backdrop of such changes, the following question could arise: Is there a place in the future for traditional SaaS solutions? Will AI agents completely replace SaaS? I think not. SaaS solutions will remain significant and an option for building marketing workflows, but AI agents will change how we interact with SaaS. In the future, such solutions will become an “engine” driven not only by people but also by agents. Just like we once moved from CLI to GUI, we are now moving to interaction through instructions, such as “create,” “analyze,” “optimize,” and so on.
Further, traditional SaaS services can only survive by becoming part of agents’ infrastructure. Agents need logic, data, and access. All this can be provided only in a transparent format, machine-documented, and managed via API for full functionality.
Consequently, SaaS will transform from the “end-user product” we are all accustomed to into a “product for the agents.” This implies that the usual human interface will become optional, while predictability, reliability, and API openness will be at the top of the priority list. Thus, SaaS will need to be designed not only for people but also for machines, which is what AI agents are.
Final thoughts
As evident from the above discussion, AI agents are the next step in the evolution of workflow automation. With an open approach to problem-solving, flexibility, and scalability, agents will transform the manner in which we conduct marketing and how the already established SaaS solutions market will function, thus encouraging holders to sharpen their products for machine operation.
Like any industry revolution, this won’t happen overnight. Everything will happen gradually, including the shift in SaaS products and our perception of exactly how we should automate processes. All you need is to be open to new things, stay on the edge of innovations, and analyze those innovations that will best boost your business.
About the Author
Dmytro Kudrenko, the Founder and CEO of Stripo, is a dedicated email marketing enthusiast with over 15 years of industry experience and 25 years of experience as an entrepreneur. Dmytro is an expert in email marketing automation. Licensed specialist in Lead Management and Email Messaging, according to Meclabs, the world-famous company that runs numerous investigations in marketing. Speaker at multiple specialized conferences in the USA, Germany, Estonia, Portugal, and the Netherlands. Dmytro’s mission is to enhance the accessibility and utility of email marketing for businesses and their subscribers.
