Generative AI and Agentic AI

Generative AI learns statistical patterns in training data and creates new content such as text, images, code and biological sequences. Agentic AI pursues an objective across multiple steps by planning, using tools and adapting to results within bounded autonomy. An agent may use a generative model, but generation and goal-directed action are distinct capabilities.

How generative AI creates content

Generation has two stages: learning a distribution from examples and producing an output from that learned distribution.

• During training, data are encoded into units such as tokens or numerical representations. Model parameters are adjusted through repeated prediction and error correction, capturing statistical relationships in the data.
• During inference, a prompt conditions the learned probability distribution. The model repeatedly selects or samples likely elements, recombining learned patterns into an output that can be novel.
• The process varies by architecture: language models commonly predict tokens sequentially, while diffusion models iteratively remove noise to form images.

Biological sequences as an example

Models can represent DNA, RNA or proteins as sequences and learn motifs and long-range dependencies by predicting missing or subsequent elements.

• DNA tokens may represent A, C, G and T or longer segments; RNA uses U instead of T, while proteins are represented through amino-acid sequences.
• Generation may be conditioned on prompts, known sequences or desired properties, and the model samples or selects likely subsequent tokens.
• Computational plausibility does not establish biological function, safety or viability; generated sequences require rigorous screening and experimental validation.

Agency, cyber risks and safeguards

Agentic AI is generally a broader system combining models with planning, memory, feedback and external resources.

• An agent decomposes an objective into tasks, selects actions, observes results and may revise its plan.
• Autonomy is bounded by permissions, available tools, programmed constraints and human oversight. Agents may access databases, application programming interfaces or software environments, and multiple specialised agents may coordinate.
• Generative AI can scale deceptive content, impersonation and insecure code. Agentic AI adds execution risk because prompt injection or manipulated data may trigger cascading actions through connected systems.
• Credentials, memory, plug-ins and external interfaces expand the attack surface. Controls include least-privilege access, tool allow-lists, logging, monitoring and human approval for high-impact actions.
• These systems can automate defensive triage and response, but accountability remains with human organisations.