Agentic AI is Reshaping Software Development and Payment Integration

Historically, generative AI made a leap roughly once a year. Now, we’re seeing major upgrades every few months — or even weeks. In just the past two quarters, we’ve seen several significant model drops from OpenAI, Google, Anthropic and xAI, and we’re already bracing for OpenAI’s upcoming GPT-5 release. The pace is relentless, with three new models dropping during the course of writing this article (including ChatGPT 5). However, what’s more important than any one release is what it all points toward: A foundational shift in how software development and payment integration work.

Despite everything generative AI is already doing to reshape business and daily life, we may not just be on the verge of the next breakthrough — we may already be inside it. And that breakthrough is agentic AI: systems that can reason and take actions autonomously on the user’s behalf without constant prompting.

There’s still a long way to go before agentic AI truly ushers in these seismic changes, but the early tremors are already being felt. So, what are AI agents, how do they impact the software development process, and why are they important to embedded payments?

AI agents are tools that can act on their own to complete an ongoing task, without the need for the user to continuously prompt them. The common AI tools we’re all familiar with today, like ChatGPT, are completely reactive. They wait for a prompt, respond and then wait for another prompt. 

AI agents are proactive. They can take in information, analyze it and determine if the conditions for acting have been met. Then they go to work without any additional user intervention.

Imagine you wanted to find the best deal on a flight. If you were to ask a Large Language Model (LLM) like ChatGPT, it would search in the moment, provide you with the results and that would be it. If you wanted to recheck later, you’d have to ask again. But, if you gave the job to an AI agent designed for that task, it could not only do the upfront research, but it could also regularly rescan for prices, analyze the data it finds and decide on its own whether or not it’s found a deal worth notifying you about. Eventually – possibly even now – it could even buy the ticket on your behalf.

That’s a huge leap forward. But it’s also not quite here yet with a long tail of gotchas and edgecases. The defining characteristic of agentic AI is that it doesn’t require constant user intervention. Right now, the AI agents being tested by the biggest AI companies, like OpenAI’s Operator and DeepSeek-R1, still require a lot of hand-holding. But we’re close, and the realization of agentic AI is definitely something worth getting excited about.

Agentic ecommerce, like that flight tracker, is a big topic in payments because of its potential to reshape how people shop and engage with businesses online. But, the application I’m most interested in is how AI agents are going to change the way we develop software.

Until just earlier this year, we relied heavily on AI copilots that augment the human coder. These AI development tools were valuable, but fairly limited. For example, AI auto-complete detected the code a developer started entering, predicted where they’re going with it and finished the code snippet automatically. That offers huge speed and productivity gains, but it has the same limitation as so many other AI tools — it requires a human to get the process started again and again. And the coding capabilities weren’t quite there for larger scale problems.

Now, we’re using actual agentic AI systems for software development, where AI is starting to look more like a junior engineer than just an advanced auto-complete tool. Research systems like Google Jules and OpenAI Codex are pushing us closer to high-autonomy coding, especially with the integration of industry standard protocols like Model Context Protocol (MCP) that make it possible for AI agents to autonomously interact with existing tools, systems and data.

In the not-too-distant future, agents may be able to reliably write all necessary code, test it, identify failures, debug their own work, retest, refactor and refine until everything works. That will allow it to complete the cycle autonomously, leaving human engineers to review the work and make the decision to go live. But, even if we only get to a fraction of that level of autonomy, the productivity gains realized will be mind-blowing.

That shift is going to be enormous for the entire software industry. It’s going to free up human engineers to focus on things like architectural decisions, customer experience, performance and innovation, instead of spending time figuring out how to make code work. That realignment — letting humans do the creative and analytical things humans do best — is going to usher in incredible improvements in the quality of design and the benefits we can gain from software.

Once again, it’s important to note we’re not there yet. For example, Google Jules has demonstrated some incredibly impressive capabilities. But it only recently opened up to public beta, and it currently carries strict limits on the number of tasks that can be performed and the time each task can take. OpenAI Codex is also still in the research demonstration phase. How these tools will scale, how reliable and resource-hungry they’ll be under heavy demand, and what they’ll cost when they’re fully deployed is all yet to be seen. And there will be growing pains.

We also need to think about what happens when AI coding agents are finally widely available and highly reliable. I mentioned above that agents are starting to act like junior engineers. And it’s probably only a matter of time before they can perform most of the same duties with an extremely high level of autonomy. But it’s important to remember that AI agents aren’t engineers. They’re just tools, and they’re not designed to replace people. Companies that lose sight of that may gain in the very short term, but they’ll suffer in the long term.

Junior engineers are young senior engineers, and it’s they who will grow into the senior roles that provide the vision and guidance for AI agents in the future. So, it’s important to avoid thinking of AI agents as a low-cost replacement for junior roles. While it might save some money upfront, losing a generation of junior engineers would be devastating to our ability to innovate in the future.

In the payments context, agentic AI stands to help SaaS companies integrate embedded payments not only more quickly and easily, but also in more creative and potentially comprehensive ways.

For most SaaS companies, the big barrier to payments is a lack of experience. Payments is a complex space, and payment systems are specialized in nature. Integrating embedded payments into software means potentially redirecting time, money and mental bandwidth away from core development in order to learn the basics of a new industry. 

To avoid that, many SaaS companies opt to use no-code and low-code solutions, gaining access to embedded payments without the extra development load. But the trade-off is a loss of customization and flexibility.

With agentic AI, more software companies will be able to build highly customized payments integrations that prioritize the user experience above all else. Instead of opting for a no-code or low-code solution, they’ll be able to connect their agents to a payment platform’s application program interfaces (APIs) and software development kit (SDKs) by using protocols like MCP. They’ll also let the agent build ideally suited payment features that today would require too many resources. 

No-code and low-code will still be valuable options, but smaller independent sales vendors (ISVs) will have more choice in when to use them and when to go fully custom. That’ll ultimately mean more profitable payments monetization and a smoother payments experience for end users.