The future of engineering: Agentic engineering 2.0

Agentic engineering is evolving from a deterministic model, where AI agents follow a pre-defined path (Agentic engineering 1.0), to a more autonomous system (Agentic engineering 2.0), where agents are given goals and determine their own paths to achieve them. In the 1.0 model, AI agents operate within a fixed pipeline, handling specific tasks like incident response or code review through a deterministic workflow designed by engineers. This model is predictable but limited in its ability to adapt to unforeseen situations. In contrast, the 2.0 model involves agents working towards outcomes with the freedom to choose their own methods, guided by context, tools, and guardrails. This approach, known as loop engineering, allows AI agents to act, observe, and adjust in a continuous cycle until a goal is met or intervention is required. The transition to this new model requires robust foundational systems, including context lakes, agent registries, and governance frameworks, which are currently being developed to support both existing and future workflows. The shift is not just about technological advancement but also about redefining the role of engineers from task executors to system designers, who set objectives and review outcomes rather than dictating every step of the process.

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