Agentic AI 101: How autonomous systems are driving software breakthroughs

Autonomous agents go beyond simply following a set of rules, they’re able to mimic human logic and reasoning. AI agents can execute complex, multi-step tasks by interacting with their environment and gathering and processing data—all with little or no human oversight. And instead of being restricted to predefined rules, they can break down tasks into subtasks and critically evaluate them.

So, what does this look like in practice?

If an AI agent was tasked with booking a vacation, it would search for everything ranging from the destinations, flights, and hotels aligned to the user’s needs. Using this data, it can then assess whether it has met its end goal. If not, the beauty of AI agents is that through reasoning they will keep iterating until they reach the goal they were set out to achieve.

Given the powerful opportunity it presents for driving efficiencies and reimagining user experiences, it’s not surprising to see that agentic AI is projected to skyrocket from approximately USD $5.1B last year to USD $47B by 2030. Similarly, Gartner predicts that over 33 percent of enterprise applications will employ AI agents by 2028. While agentic AI is still in its infancy, businesses are already driving tangible impact and transforming how work gets done—and it’s only set to amplify.

What makes this AI shift so radical is the ability to leverage multiple agents at the same time that work together to divide tasks and level up efficiencies and outputs. Within these multi-agent systems, each agent has a unique role and contributes specialized knowledge (taking on roles such as a software engineer, project manager, or analyst). Coordination between them is handled by a single orchestration agent.

The result is more sophisticated decision-making and streamlined workflows that remove routine tasks and error-prone manual steps from human workloads. Efficiencies gained through agentic AI can also help to address the challenges of an aging society and a shortage of skilled workers. From boosting workplace productivity to driving research and development, multi-agent systems have huge potential to free up people to focus on strategic and creative tasks across all industries. Businesses like Formula 1 have already reduced issue resolution time by 86 percent with the help of comprehensive AWS tools and capabilities to securely build multi-agent workflows.

Agentic AI is also starting to transform the way we think about product design and iterate on new ideas. Lovable is one example of how it is radically changing the way software is imagined and created. Non-technical users can create full-stack web applications using natural language, enabling anyone to bring ideas to life without coding expertise or undergoing complex development processes.

What’s more, hyper-personalization through agentic AI is creating unmatched user experiences that are closing the gap between current customizations. And, because it is standardized, companies can bring these experiences to life while maintaining low operating costs. For software-as-service (SaaS) companies with a broad product portfolio, agentic AI can empower them to create holistic workflows across their applications—stitching together their offering in a way that’s more appealing and effective for customers. Unlike AI assistants that respond to direct prompts, AI agents harness the context of the software development infrastructure to drastically streamline how code is deployed, tested, and iterated.

The promise of specialized multi-agent workflows is huge, but there’s a lot to consider before you start building. In a rapidly evolving field where protocols and standards are still emerging, being aware of the challenges in bringing them to life can help you prepare for success.

The first port of call should always be your customers. By working backwards from their needs, you can determine applications that will deliver real value. Once you’re clear on how agentic AI can improve the customer experience, other considerations for future-proofing your strategy should include:

• Will agents be able to access the right sets of data to perform specialized tasks, and do we have the right data integration capabilities?
• Are there clear security frameworks in place to support safe and responsible AI?
• Can we quickly iterate and adopt new technologies as they emerge with current infrastructure?
• Do we have the right expertise in place or support available to adapt to rapid developments?

I've been working in the IT industry for decades and agentic AI is far outpacing anything I’ve seen in the past. With that in mind, an agile and nimble approach is critical to building intelligent agents that stand the test of time. When there are numerous factors to consider, from data interoperability to security and governance, choosing a partner with proven experience in the field can help you seize the opportunity quickly.

With the right support and technologies, software companies that are curious to explore this opportunity can drive business value quickly, whether it’s through custom development, ready-made solutions, or a combination of both.