## Why agent identity matters Traditional identity systems assume two categories: humans who authenticate with passwords or biometrics, and service accounts that authenticate with static API keys. AI agents break this model in four ways: 1. **Agents are autonomous.** They make decisions and take actions without a human pressing a button for each step. 2.…
The systematic practice of designing, managing, and optimizing all the information an AI model or agent receives during inference — including system prompts, tool definitions, retrieved data, conversation history, memory state, and structured outputs — to maximize performance within a finite context window.
Agent memory is the set of mechanisms that allow AI agents to retain, organize, and retrieve information across sessions. Learn how episodic, semantic, and procedural memory work in AI agents.
Agent governance is the set of policies, oversight mechanisms, and accountability structures that control how autonomous AI agents make decisions, access data, use tools, and interact with humans and other agents. It is the framework that ensures these systems operate within defined boundaries, remain accountable for their actions, and align with organizational objectives. Analogy: Think…
Why wallets matter for the agentic economy In a traditional digital transaction, a human enters a credit card number or clicks ‘Pay Now.’ In an agentic economy, an AI agent needs to initiate, sign, and settle transactions autonomously — often across multiple blockchains and payment rails. The digital wallet becomes the agent’s identity and authorization…
An AI agent is LLM-powered software that reasons, plans, and executes multi-step tasks autonomously using tools. Learn how agents differ from chatbots and copilots.
Learn what delegation is in AI agents, how it works through privilege attenuation, common patterns like orchestrator-workers, and why multi-hop delegation is a key unsolved challenge in agent security.
Orchestration is the coordination layer that decides which AI agent acts, in what order, with what data, and when to stop. Learn the five core patterns: sequential, concurrent, orchestrator-workers, handoff, and evaluator-optimizer.
How adversarial attacks work Machine learning models learn by finding patterns in data. When you show a model thousands of pictures of stop signs, it learns to recognize the visual patterns that define ‘stop sign’—the red octagon, the white border, the letters S-T-O-P. Adversarial attacks exploit this learning process by adding carefully calculated noise or…
The Universal Commerce Protocol (UCP) is Google’s open standard enabling AI agents to discover products, build shopping carts, and complete purchases across different merchants and surfaces.