From Input-Output to Intent: Architecting the Future of Enterprise AI

I want to thank you all for the feedback on the piece I published last week. I started writing that piece as a way to settle down my own ideas for live architecture and as I went on experimenting them on Google Cloud, other critical aspects of deployment kept surfacing, demanding tweaks in the architecture, addition of connectors, setup of verification points and KPIs that pushed my thoughts towards the most efficient way to deploy Agentic AI in a scalable fashion, an AI architecture framework for enterprises of sorts

We're no longer talking about AI as a tool that simply takes an input and gives you an output. The next generation of AI is fundamentally different. These systems are moving from being reactive to being proactive, from executing commands to actively reasoning, planning, and executing actions on their own. This is the emergence of agentic AI, and it's poised to transform how we approach intelligent automation. For enterprises this is the ultimate departmental integration.

And here lies a catch I’ve seen before: deploying agentic systems in an enterprise environment requires more than just adopting the latest LLM models or "vibe-coding" techniques. Success demands architectural patterns that balance cutting-edge capabilities with organizational realities as one change in the marketing process might have huge implications for legal, marketing or logistical departments. That’s what this architecture aims to handle. It is not about IT but how agentic AI connects itself with the overall organization though the IT stack.

We're talking about a familiar set of topics I’ve written before: governance requirements, audit trails, security protocols, and, critically, ethical accountability. Amazing how the fundamentals do not change, they only apply to different situations!

Just as I’ve said time and time again, building a robust data governance framework is a journey, not a destination. The same holds true for architecting agentic AI. You can’t simply flip a switch and expect a fully autonomous system to operate safely and effectively within your business.

In my experience, organizations that succeed in implementing data products share the same trait:  they prioritize simple, composable architectures over overly complex frameworks. Allowing them to manage complexity while controlling costs and maintaining performance standards. The implementation must be measured, evaluated and constantly discussed with the teams, partners, internal stakeholders and users. 

It is the classic data digital transformation approach, acting as the background constantly evaluating when predictability and control take precedence versus flexibility and autonomous decision-making. It is on this background that the architectural framework will hang, 

This framework is a systematic maturity progression, designed to help organizations build competency and stakeholder trust incrementally before they ever attempt to advance to more sophisticated implementations. It anticipates emerging regulatory frameworks like the EU AI Act and others, making it a blueprint for future-proof development.

We start with The Foundation Tier which is the essential first step for any enterprise looking to deploy agentic AI, focusing on establishing trust and governance before attempting greater autonomy. It is defined by three key architectural patterns: tool orchestration, which restricts the AI to a curated set of approved APIs and data sources; reasoning transparency, which requires logging and auditing the AI’s decision-making process to ensure explainability and accountability; and the disciplined use of data lifecycle patterns, which guarantees that the data the AI consumes and generates is validated, well-managed, and ethically handled. This tier serves as the bedrock, proving the system’s reliability and building the crucial buy-in from stakeholders by demonstrating a controlled and auditable approach to AI development. This is also where most companies and consulting companies are as of today.

The second tier focuses on Workflows. It builds upon the foundation of trust established in Tier one, delivering significant automation by orchestrating multi-step, predefined processes. This tier is not about full autonomy but about creating highly efficient, repeatable AI workflows. It is characterized by five core patterns: Prompt Chaining, which links a series of deterministic LLM calls; Routing, which directs tasks to the appropriate sub-process; Parallelization, which enables concurrent task execution; the Evaluator-Optimizer pattern, which introduces a self-correction loop for improving output quality; and the Orchestrator-Workers model, which delegates complex tasks to specialized agents. These features allow automation of complex business processes while maintaining a predictable, auditable framework with clear human oversight, measuring the relevant KPIs and validating (or not)  efficiency and productivity gains.

The final tier is all about autonomy, and represents the advanced stage of agentic AI deployment, enabling systems with genuine, goal-directed planning capabilities. This tier is not about replacing human decision-making with fully autonomous systems, but rather about deploying Constrained Autonomy Zones that operate within a tightly controlled, well-governed environment. The core principle here is to allow the AI to dynamically determine its own approach to achieving a goal, while still embedding validation checkpoints and human oversight to maintain accountability and mitigate risk. This introduces flexibility and innovation, allowing the AI to learn and adapt, but always within boundaries that align with regulatory compliance and ethical standards.

A Tale of Context: Industry-Specific Implementation

One size does not fit all. The general framework I’ve outlined is a guide, but any specific implementation must be tailored to the unique guiding principles and utility functions of your industry: regulatory constraints, business objectives, tech stack limitations.

I know first hand that Financial Services, Healthcare, Gaming, Retail and Manufacturing have specificities that make implementation as different as day and night, so in the coming weeks my idea is to explore contextual engineering as a core feature of any AI deployment.

Finally I would like to share how lucky I am to have intellectual partners in this journey, like Subash Natarajan and Ahilan Ponnusamy whose work have inspired me and Naomi G. whose tech prowess always impresses me.