For years, we've thought of Artificial Intelligence as something we add to existing software. It's often been a powerful layer on top, enhancing capabilities or automating specific tasks.
But what happens when intelligence isn't just a feature you bolt on, but rather a fundamental, native component of the system itself?
This isn't just an upgrade; it's a paradigm shift that fundamentally changes how we design, build, and interact with software.
When AI becomes baked into a system's core, rather than just painted on top, it forces a deeper, more profound rethinking of how we design software. This isn't about incremental improvements; it's about redefining the very primitives of system behavior.
This profound shift means:
- Agents over Workflows: Traditionally, our software is built on predefined workflows – rigid sequences of rules and steps. But in an intelligently native system, autonomous agents will replace these static workflows. Decisions won't be dictated by a script; instead, they will emerge organically and dynamically from the complex, ever-changing context of the system, allowing for far greater adaptability and responsiveness.
- Signals over Triggers: We're used to systems reacting to explicit, static triggers – a button click, a data entry, a scheduled event. Imagine instead a world driven by nuanced signals. This moves us from merely reacting to predefined events to anticipating probabilistic intent. Systems will perceive subtle cues, patterns, and probabilities, allowing them to act proactively and intelligently based on an evolving understanding of the environment.
- Memory over State: Current software largely relies on static state – data stored at a particular moment in time. But when intelligence is native, memory becomes the new state. History transforms from passive, archived data into dynamic, living context. The system continuously learns from its past interactions and observations, allowing its understanding and behavior to evolve in real-time. This isn't just logging; it's genuine, actionable recall.
- Feedback over Logging: The traditional method of understanding system performance and issues is through logging – recording events for later analysis. In an AI-native architecture, feedback takes the place of simple observation. Systems will be inherently designed to learn and adapt directly in production, continuously refining their models and behaviors based on the outcomes of their actions. This creates self-optimizing, resilient systems that improve with every interaction.
These shifts aren't minor adjustments; they completely redefine our design constraints. As AI continues to evolve, understanding the role of AI Agents and AI Coworkers is crucial for building, buying, or successfully implementing long-term AI solutions. They force us to ask a new set of fundamental questions that challenge decades of software engineering principles:
- What is the atomic unit of decision-making in a system that constantly evolves and learns? If behavior isn't rigidly coded, how do we define and manage the smallest, most impactful actions? Currently our agents look at these as predictions. We share how many predictions are made by each of the agents, its accuracy. Embedded in the software is insights on how each of these are measured.
- What does it truly mean to build for adaptability, rather than simply aiming for stability? Our current metrics and methods often prioritize predictable, stable outcomes. How do we measure success and design for systems that are inherently designed to change and adapt?
- How do we view data? I’ve been thinking a lot about data. In fact, if you’re interested, check out my 2018 article on The Evolution of Data, where I discussed how "workflow" was the old paradigm, and "intelligence" is the new frontier. We also see that data flow is the next major evolution in how we work. This is why
Ascendo AI
AI agents integrate ETL, expert data pipelines and the Goldilocks zone of looking at data
- When User Interface is not for data entry but for decision making and learning how is it different? The crux of what the user sees has to be more than a prompt. It has to go beyond action and response (current drawback to some of the agents out there!) to embed joint critical thinking with humans.
- How do we maintain traceability and control when system behavior isn't entirely deterministic? When decisions emerge from context and learning, how do we ensure accountability, debug issues, and manage compliance? AI Explainability is a key in audits, learning feedback, incorporating feedback and many more.
According to Foundation Capital, AI’s potential to transform software from a tool to a coworker represents a $4.6 trillion opportunity. This shift is not just about technology; it’s about creating a mindset and building products with a long-term vision.
Ultimately, the conversation is moving rapidly beyond the simplistic question of "what can AI do" and pushing us towards a much deeper inquiry: what must the system around AI become to truly harness its potential? While incredible advancements and remarkable work are happening at the model layer – with new algorithms and larger models emerging daily – there is just as much, if not more, complexity and innovation required in the architectural foundations to effectively integrate and harness this new form of intelligence.
The future of software isn't just about smarter algorithms; it's about building fundamentally smarter systems. This is the challenge and the opportunity before us.
If you're thinking about how AI will shape your organization, now’s the time to ask the right questions and plan for the future. Let's keep the conversation going!
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