AI & ML Tech Glossary

Agent permissioning enforces least-privilege access for tool-using AI agents by controlling which tools and actions are allowed, what data scopes apply, and when human approvals...

Agent self-reflection adds a critique-and-revise step to an AI agent’s loop, where the agent evaluates its own plan or answer against a rubric (constraints, evidence,...

An agentic tool gateway is a mediated layer between an AI agent and external tools that enforces authentication, authorization, validation, rate limits, and audit logging....

Agent-to-Agent Protocol (A2A) is a structured messaging pattern that lets AI agents delegate tasks, share results, and coordinate safely using shared schemas, retries, and traceable...

An agentic loop is the iterative control cycle of an AI agent: observe state, decide the next action, execute via tools or model calls, and...

Agentic tool arbitration is how an AI agent selects the best tool and action sequence when multiple tools are available. It considers capability, cost, latency,...

AI model governance is the policies and controls used to manage AI models across their lifecycle, including approvals, documentation, monitoring, and risk management. It makes...

Agentic memory retrieval is how an AI agent selects and loads relevant past information, such as prior steps, preferences, and tool outputs, into the current...

An agentic benchmark is a test suite for AI agents that evaluates multi step task completion, tool use, planning, and safety in interactive environments. It...

Agentic guardrails are controls that constrain an AI agent’s plans, tool use, and outputs. They add permission checks, sandboxing, budgets, validation, and monitoring throughout the...

Agent evaluation measures how well an AI agent completes real tasks over multiple steps, including planning, tool use, reliability, safety, latency, and cost, not just...

Agentic planning is how an AI agent breaks a goal into ordered steps, selects tools, executes actions, and revises the plan based on results. It...

AI red teaming is adversarial testing of AI systems, especially LLM apps, to uncover safety and security failures like jailbreaks, data leakage, harmful outputs, and...

An agentic workflow is a repeatable sequence of planning, tool calls, evaluation, and iteration that an AI agent follows to complete a goal. It makes...

An agent swarm is a multi-agent system where several AI agents collaborate in parallel—using role specialization, coordination, and cross-checking—to plan and execute complex tasks more...

An agent runtime is the execution layer that runs an AI agent’s loop, manages state and memory, integrates tools, and enforces policies, logging, and guardrails...