LLM jailbreaking is adversarial prompting that bypasses AI safety guardrails. Learn about techniques, defenses, and why it’s critical for autonomous agent security.

How APIs work APIs follow a request-response model. A client application sends a request to an API endpoint (a specific URL), and the server processes the request and returns a response—typically in JSON or XML format. A typical API call: Client sends request: GET https://api.example.com/users/123 Server processes: Looks up user 123 in the database Server…

Definition A subset of machine learning that uses artificial neural networks with many layers — typically three or more hidden layers — to learn hierarchical representations of data. Each layer extracts increasingly abstract features from the input, allowing the model to discover complex patterns without hand-engineered feature extraction. ‘Deep’ refers to the network’s depth, not…

Agent governance is the system of policies, controls, and oversight structures that determine what autonomous AI agents can do, how they are monitored, and who is accountable when they act.

Agent compliance is the set of regulatory, legal, and governance obligations that organizations must meet when developing, deploying, or operating autonomous AI agents—covering risk classification, human oversight, audit trails, incident reporting, and accountability for agent actions.

Responsible AI is a set of principles and practices guiding the ethical design, development, and deployment of AI systems to ensure fairness, transparency, accountability, and safety.

Backpropagation is an algorithm for efficiently computing the gradient of a loss function with respect to a neural network’s weights by applying the chain rule of calculus in reverse—propagating error signals backward from the output layer through each hidden layer to compute how much each weight contributed to the overall error.

Explainability (XAI) is the ability of an AI system to provide clear, understandable reasons for its outputs, enabling humans to interpret the logic behind decisions or predictions made by otherwise opaque models.

Agent infrastructure is the production platform layer that provisions, manages, secures, and monitors AI agents once they move beyond prototype. It supplies persistent context and state, executes agent steps and tool calls, connects models to enterprise systems, and continuously controls, monitors, and audits agent actions.