Beyond traditional engineers using AI and non-technical "vibe coders," a third archetype is emerging: the "agentic engineer." This professional operates at a higher level of abstraction, managing AI agents to perform programming, rather than writing or even reading the code themselves, reinventing the engineering skill set.

AI's impact on coding is unfolding in stages. Phase 1 was autocomplete (Copilot). We're now in Phase 2, defined by interactive agents where developers orchestrate tasks with prompts. Phase 3 will be true automation, where agents independently handle complete, albeit simpler, development workflows without direct human guidance.

A major trend in AI development is the shift away from optimizing for individual model releases. Instead, developers can integrate higher-level, pre-packaged agents like Codex. This allows teams to build on a stable agentic layer without needing to constantly adapt to underlying model changes, API updates, and sandboxing requirements.

As AI agents take over task execution, the primary role of human knowledge workers evolves. Instead of being the "doers," humans become the "architects" who design, model, and orchestrate the workflows that both human and AI teammates follow. This places a premium on systems thinking and process design skills.

Enterprises face a major friction point with AI agents: business teams want easy no-code tools, while engineering teams need low-level code access for control and integration. Inkey's solution is a hybrid platform where a no-code visual builder generates a TypeScript SDK. This allows support or sales teams to build agents that engineers can then refine and manage as code.

Instead of focusing on foundational models, software engineers should target the creation of AI "agents." These are automated workflows designed to handle specific, repetitive business chores within departments like customer support, sales, or HR. This is where companies see immediate value and are willing to invest.

A new software paradigm, "agent-native architecture," treats AI as a core component, not an add-on. This progresses in levels: the agent can do any UI action, trigger any backend code, and finally, perform any developer task like writing and deploying new code, enabling user-driven app customization.

Legacy systems like CRMs will lose their central role. A new, dynamic 'agent layer' will sit above them, interpreting user intent and executing tasks across multiple tools. This layer, which collapses the distance between intent and action, will become the primary place where work gets done.

The future of software isn't just AI-powered features. It's a fundamental shift from tools that assist humans to autonomous agents that perform tasks. Human roles will evolve from *doing* the work to *orchestrating* thousands of these agents.