Machine shops and similar manufacturing operations are not fazed by digitalization. High-tech machining is familiar ground, from basic NC programming through to multi-axis set-ups, to Industry 4.0 and now artificial intelligence. But the familiar state of AI is changing and a digitalization is entering a new dimension as generative artificial intelligence (Gen AI) evolves toward agentic AI. In the context of CNC machine programming, these technologies offer the prospect of significantly greater productivity, reduced programming effort, enhanced machining quality - and more autonomous manufacturing.
Numerous shops and manufacturers already understand how generative AI can update typical CNC programming workflows by automating many of the knowledge-intensive tasks currently performed by experienced programmers. Gen AI tools are already features of numerous manufacturing technologies - and will be featured at IMTS 2026.
Today, CNC programming requires interpreting engineering drawings, CAD models, geometric tolerances, material specifications, tooling constraints, and machine capabilities before generating optimized toolpaths and G-code. Generative AI models can be trained to do all this by referencing historical machining programs, engineering standards, tooling libraries, and manufacturing best practices to assist programmers throughout this process.
One important capability is automated program generation from engineering inputs. A Gen AI system can analyze CAD models, identify machinable features such as pockets, holes, slots, and contours, and propose machining strategies appropriate for the material, machine tool, and production requirements. Rather than creating each operation individually, programmers can review, modify, and approve AI-generated recommendations, and reduce programming time.
Generative AI also works as a conversational manufacturing assistant. Engineers and machinists can interact with the system using natural language to request tooling recommendations, cutting parameters, set-up instructions, fixture suggestions, or explanations of machining strategies. This capability helps capture and distribute “tribal” knowledge that is may be concentrated among a limited number of experienced personnel.
Additional applications include automatic documentation generation, machining process planning, set-up sheet creation, tool list generation, and program validation. AI can analyze CNC code for potential collisions, inefficiencies, excessive cycle times, or non-compliance with manufacturing standards before programs are released to production. By learning from previous jobs, the system can continuously improve recommendations and support standardization across manufacturing facilities.
Greater potential for autonomy
While generative AI provides valuable assistance, agentic AI introduces the potential for greater autonomy. Agentic AI systems are designed to pursue objectives, make decisions, coordinate tasks, and adapt to changing conditions with limited human intervention. In CNC manufacturing environments, AI agents could function as intelligent digital workers that manage portions of the programming and production lifecycle.
For example, an agent could receive a new part design, retrieve relevant manufacturing standards, evaluate machine availability, select appropriate tooling, generate machining operations, simulate toolpaths, estimate cycle times, and prepare a complete CNC program package for human review. Rather than responding to individual prompts, the agent executes a sequence of coordinated tasks to achieve a defined manufacturing objective.
Agentic AI also can initiate closed-loop optimization. By integrating with machine controllers, manufacturing execution systems (MES), tooling databases, quality systems, and sensor networks, agents can continuously monitor production performance. If machining conditions change, tools wear unexpectedly, or quality deviations occur, the agent can recommend or automatically implement process adjustments. This capability supports adaptive manufacturing environments that learn and improve over time.
Another significant opportunity lies in multi-agent collaboration. Specialized agents can focus on programming, scheduling, quality assurance, tooling management, and production optimization while sharing information across the manufacturing ecosystem. Such collaboration can reduce bottlenecks, accelerate new product introduction, and improve resource utilization across facilities.
So where would these agents be leading us? The long-term vision should not be replacement of skilled CNC programmers but the augmentation (or propagation) of their expertise. Human engineers remain responsible for oversight, validation, safety, and strategic decision-making, while AI systems handle repetitive analysis, knowledge retrieval, optimization, and coordination tasks. This human-AI partnership can improve consistency, reduce programming lead times, enhance manufacturing agility, and address workforce challenges associated with the shortage of experienced machining professionals.
As AI technologies continue to mature, generative AI and agentic AI are expected to become foundational components of next-generation CNC programming environments, facilitating smarter, faster, and more autonomous manufacturing operations.
