What is AI agent autonomy? Autonomy in AI agents describes the degree to which an agent can independently perceive its environment, reason about goals, select actions, execute them, and evaluate outcomes without human input at each step [1]. This is different from automation. A traditional automated system follows a fixed script—every action is pre-programmed. An…
What Is LangGraph? LangGraph is a library for building agent applications that need more than a single prompt-response cycle. Released by LangChain in January 2024 [2], it models agent workflows as directed graphs: nodes represent computation steps (calling an LLM, running a tool, making a decision), and edges define the flow between them—including conditional branches…
What is Swarm Intelligence? Swarm Intelligence (often referred to as swarm AI, collective intelligence, or swarm optimization) describes a field of artificial intelligence where complex, intelligent behavior emerges from a group of simple agents. Swarm intelligence is a specific design philosophy within the broader field of multi-agent systems (MAS). While some multi-agent systems rely on…
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…
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…