The Agentic Shift: Navigating the New Frontier of Autonomous Software
Attending the “AWS | Agentic AI para Software Companies” event in Madrid was not merely a professional obligation; it was a profound opportunity to witness the shifting tides of our industry firsthand. As someone deeply embedded in the world of Data Engineering, Management, and Governance at Accenture, I have always believed that the true measure of a technology is its ability to scale within the rigorous constraints of the enterprise. Standing amidst the architectural marvels of Madrid’s business district, it became clear that we are no longer just discussing “chatbots.” We are architecting a future where autonomous agents act as the primary engine for business transformation.
A Visionary Connection: Learning from the Leaders
One of the primary highlights of the day was hearing from Eduardo Ordax, GTM Specialist GenAI at AWS. Having followed Eduardo on LinkedIn for quite some time, I have always admired his ability to demystify complex concepts. His session on the business impact of Agentic AI did not disappoint. What sets Eduardo apart is his unique ability to blend high-level strategic foresight with a refreshing sense of humour. By using memes and jokes to illustrate the current state of AI, he made the “Agentic Era” feel accessible and human.
However, beneath the levity was a serious leadership stance that resonated with my own professional philosophy. Eduardo’s vision is not just about the technology itself, but about how it integrates into the lifecycle of software companies. Consequently, I walked away with a sharpened understanding of how to lead teams through this transition—moving from a state of passive assistance to one of proactive execution.
The Reinventor’s Mindset: Agentic AI at Accenture
At Accenture, we pride ourselves on being “Reinventors.” We do not simply adopt technology; we weave it into the very fabric of how our clients operate. In my current role, where I manage data frameworks for a banking client with over 80 million customers, the stakes could not be higher. Whilst many see AI as a customer-facing tool, I see a massive opportunity for internal back-office automation.
For a bank of this magnitude, the complexity of data governance and manual reconciliation is staggering. By implementing Agentic AI, we can move beyond traditional Retrieval-Augmented Generation (RAG). Instead of a system that merely “finds” information, we are building agents that possess the individualised knowledge to solve specific needs for each of those 80 million clients. This shift is essential to discovering the real value we provide to our customers and the broader business ecosystem.
Multi-Cloud Strategy and the Power of Openness
My technical heart lies in Multi-Cloud strategies. Whilst my core speciality has historically been within Google Cloud, I have always maintained a deep appreciation for the AWS ecosystem, specifically for its remarkable openness. The sessions on Amazon Bedrock AgentCore and the Model Context Protocol (MCP) highlighted this perfectly.
The MCP is particularly revolutionary for an engineer. By standardising how models discover and use external tools, AWS is effectively solving the integration nightmare that often plagues large-scale SaaS architectures. Essentially, this protocol allows us to build bespoke agentic solutions that are not locked into a single vendor’s silo. For a global bank, this level of interoperability is non-negotiable. It allows us to optimise our infrastructure across multiple clouds whilst maintaining a unified layer of security and governance.
Securing the Autonomous Frontier
During the deep dives into SaaS Architecture and Security, the conversation turned toward the “Art of the Possible.” When dealing with autonomous systems, security is the foundation of trust. Eduardo P. Garcia’s insights into security architecture for agentic applications provided a vital blueprint for how we must protect sensitive financial data.
In the banking sector, we cannot afford “hallucinations” or unauthorised actions. Therefore, we are implementing guardrails that allow agents to self-reflect and assess their output based on the environment before providing a verified output. This iterative cycle—sense, plan, act, and reason—is what will ultimately allow us to work faster and more efficiently without compromising the rigorous standards of financial governance.
Conclusion: A Journey of Continuous Learning
I am immensely grateful for this learning opportunity and the warm welcome that the AWS team always provides in Madrid. The event was a reminder that the transition to Agentic AI is as much a cultural shift as it is a technical one. We must be willing to experiment, to find the “low-hanging fruit” in automation, and to continuously refine our approach to monetisation and value delivery.
The future of software companies in the Agentic era is clear: those who can successfully orchestrate multiple agents to solve complex, multi-step tasks will lead the market. As I return to my work at Accenture, I am more motivated than ever to apply these insights, ensuring that our clients—and their millions of customers—benefit from a safer, smarter, and more autonomous digital future.
I am always eager to connect with fellow professionals who are passionate about the intersection of Data Engineering and AI. If you are navigating these challenges or would like to discuss the future of multi-cloud architecture, please connect with me on LinkedIn to continue the conversation.