Agent Client Protocol
Agent Client Protocol is a shared communication standard that lets client applications send tasks to AI agents and receive responses, progress updates and permission requests through a common interface.
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Clear filtersAgent Client Protocol is a shared communication standard that lets client applications send tasks to AI agents and receive responses, progress updates and permission requests through a common interface.
Agent collusion occurs when AI agents coordinate in ways that undermine an intended rule, evaluation, market, or oversight process.
Agent conversation state is the temporary record of messages, tool results, decisions, and intermediate context available within an AI agent session.
AI agent discoverability is the degree to which an agent can find, identify, and correctly understand a product, library, capability, or authoritative information source.
An AI agent environment is the bounded world in which an agent observes state, uses tools, interacts with others, receives feedback, and produces consequences.
An AI agent gateway is a shared control layer that mediates agent access to models, tools, data and observability services.
An agent handoff transfers an AI task, relevant context, authority, and responsibility between agents, people, sessions, or environments.
An agent handoff prompt gives a new AI agent the concise durable context needed to continue a task without reconstructing the previous session.
Agent infrastructure is the shared technical foundation that lets AI agents run, use tools, manage state, coordinate and be evaluated safely.
An agent iteration limit is the maximum number of work-and-revision cycles an AI agent may use for a task.
An AI agent memory audit reviews stored entries for relevance, provenance, accuracy, duplication, usage, scope, and expiry.
An AI agent persona is the relatively consistent style, role, priorities, and social behavior an agent presents within a context.
AI agent rate limiting restricts how many actions an agent may perform within a defined period.
An AI agent skill registry is a maintained catalog of reusable, versioned capabilities available to AI agents.
Agent skill reuse means applying saved task guidance again so an AI agent can perform similar work with less repeated instruction.
AI agent state detection determines whether an agent is available, running, waiting, blocked, failed, disconnected, or complete.
An agentic AI benchmark evaluates how well an AI system plans, uses tools, handles feedback, and completes multi-step tasks rather than only answering isolated prompts.
Agentic AI inference is model serving for AI agents whose long, multi-turn and tool-using sessions create variable context, latency and caching demands.
AI-assisted chip design uses AI agents or models to support semiconductor design, analysis, verification and team coordination.
An AI-native company designs its work, systems and responsibilities around AI capabilities from the beginning.