How to get the most out of agentic AI

Agentic AI is the next big leap in automation – AI systems that don’t just respond, but act. Here’s what it means for printers, how it differs from traditional AI and how it could transform both your print room and your business operations.

Artificial intelligence has already transformed the print sector in countless ways – from online quoting systems and predictive analytics to workflow automation. But we’re now entering a new phase of AI development: agentic AI.

Unlike the large language models (LLMs) that most of us are familiar with – such as ChatGPT – agentic AI doesn’t just respond to prompts; it acts. It takes initiative, sets goals and executes tasks with minimal human supervision.

For printers, this could mark one of the biggest operational shifts since the introduction of digital presses. So, what exactly is agentic AI, and how can printers start using it to improve efficiency, reduce costs and enhance customer service?

In simple terms, agentic AI refers to artificial intelligence systems capable of autonomous action toward specific goals. Rather than waiting for human input to generate text or analysis, these AI ‘agents’ can make decisions, perform actions and coordinate with other systems or agents to get results.

A large language model like ChatGPT can tell you how to optimise your print schedule if you ask it the right question. An agentic AI, on the other hand, could analyse your schedule, identify bottlenecks and reorder jobs automatically – all without waiting for you to intervene.

Large language models (LLMs) – the technology behind conversational tools like ChatGPT and CoPilot – are designed to generate human-like text. They’re powerful for communication, content generation and reasoning within a given conversation. However, LLMs are reactive. They need prompts, and they don’t take real-world actions unless connected to other systems.

Agentic AI takes that foundation and adds the ability to plan and execute tasks independently, monitor progress toward goals, make decisions based on changing data and collaborate with humans or other agents.

In practical terms, an LLM might tell you how to restock ink when it’s running low. An agentic AI would notice the ink levels, check supplier availability, compare prices and place the order automatically.

For print businesses – particularly SMEs that rely on time efficiency and cost control – the arrival of…