On one thing, experts agree: when it comes to AI-based agents, we’ve only seen the tip of the iceberg. While the tangible impact of Generative AI is already evident in areas such as content creation, communication automation, and document processing, it’s clear that its potential extends far beyond what is currently visible. The idea that software agents can tackle complex challenges and design their own solutions fuels the imagination of those who envision an unprecedented future of intelligent automation.
After the initial wave of experimentation, let’s look at how companies are now integrating these tools into their operational workflows—and what the near future might bring.
For those approaching this topic for the first time, it’s essential to understand what falls within the scope of agentic AI and what instead belongs to more traditional approaches. Except for specialized professionals, there’s still some confusion in the market—confusion that can easily affect expectations and outcomes.
This is the baseline: direct interaction with a language model, leveraging its reasoning skills and ability to generate coherent outputs. However, the model does not act independently or trigger external tools to achieve a goal. It’s a powerful system and the foundation of AI agents, but its boundaries are clear: it responds, it doesn’t execute. Some extensions—such as web search or the use of specific tools—are only now beginning to enter everyday practice.
Here, automation becomes richer: developers build workflows to provide generative models with context, data, and additional actions. Typical examples include extracting information from emails or calendars (to enable reasoning), updating spreadsheets, monitoring websites, or semi-automated content production. The model remains at the center, but the surrounding ecosystem begins to move.
This is not just the latest trend—it’s the most advanced stage. Here, workflows exist, but the agent decides how and when to execute them. It has objectives, autonomy, and decision capacity. A customer care agent, for example, might execute predefined actions and interface with known systems (e.g., CRM or email); it can also decide which customer to prioritize or what discount percentage to apply. Another agent could repurpose long-form YouTube videos into subtitled and dubbed shorts, autonomously choosing which content to adapt, what tone of voice to use, and when to publish for maximum visibility. It doesn’t just execute: it acts, decides, and optimizes.
One of the key strengths of Agentic AI is its versatility: these software agents can be integrated into virtually any business sector and function, from customer experience to logistics, administration, and even software development. Integrating them safely into operational processes requires awareness of their limitations, including algorithmic bias, data privacy, and hallucinations.
Unlike traditional chatbots, AI agents don’t just query a database and return answers—they can make decisions based on context and company policies. They can choose which tools to use (CRM, FAQs, ticketing systems) and in what order, assess case priorities, or even negotiate directly with customers within well-defined business constraints.
In sales and marketing, AI agents can replace manual workflows, deciding how to act based on data. Unlike LLMs that only generate content on demand, agents can monitor campaigns, identify opportunities, and propose corrective actions with minimal human input.
A concrete example: a social media marketing agent could analyze real-time trends, decide (with supervision) which content to promote or adapt, adjust tone of voice by audience segment, schedule posts across different channels, and suggest day-by-day budget optimizations.
In sales, an AI agent could scan CRMs, emails, and intelligence tools to identify hot leads, propose an offer via email, or escalate to a human salesperson—updating CRM data automatically.
In the legal domain, AI agents can go far beyond searching for clauses or summarizing documents (typical LLM use cases). An agent could monitor contracts and policies for compliance with evolving regulations in real time, decide when to flag risks, and even generate a new contract proposal aligned with updated standards.
In finance, AI agents could monitor invoicing in real time, cross-check data from ERP and CRM systems, and decide whether an invoice is valid or should be flagged, issuing alerts or proposing corrections.
Beyond checking contract consistency, they could also update dashboards, forecast cash flows, and reschedule payments based on available liquidity—while respecting thresholds and business rules.
In software development, AI agents can go beyond suggesting and writing code. They can coordinate development cycles, deciding when to perform tests, refactoring, or deployment.
A well-configured agent could take a feature request, search existing models, propose compatible code, and create test cases—leaving human developers to supervise, validate integration, and handle final releases.
Another emerging use case is employee training. Unlike static e-learning platforms, an AI agent can personalize training materials based on role, level, and performance, adapting pace and content to individual needs. Functionally, it’s not so different (except for the purpose) from recommendation engines used by major consumer platforms like Amazon or Netflix.
No one can predict the future with certainty—not even AI. But one fact is clear: investments in AI are growing at an extraordinary pace because companies are convinced they’ve identified the next big thing for their business. Chances are, they’re right.
Software agents will continue to evolve rapidly and structurally. We’ll see improvements in model reasoning capabilities, deeper integration with external tools, and a steady push toward increasingly pervasive forms of automation, aligned with the hyperautomation paradigm. AI agents are indeed one of the building blocks of this vision: autonomous, adaptive, connected.
Looking ahead, some up-and-coming applications include:
As the field evolves, so does the need to balance efficiency, control, and accountability—concepts that are moving beyond the realm of ethics into everyday business practice. Regulations such as the AI Act will require companies to structure their agentic systems more carefully, assessing their impacts, limitations, and scope for human oversight.