23 terms
Definitions in this topic
- Agentic AIAgent autonomyHow agent autonomy works, how to bound it, and when human approval is still required.
- Agentic AIAgent delegationAgent delegation explained: bounded tasks, retained ownership, and safe return conditions.
- Agentic AIAgent handoffAgent handoffs explained: transferring control and context between specialist agents or from AI to a person.
- Agentic AIAgent harnessAgent harness explained: the runtime that adds tools, memory, approvals, and a working loop around an LLM.
- Agentic AIAgent middlewareAgent middleware explained: shared logging, security, retry, and validation logic around agent and tool execution.
- Agentic AIAgent sessionAgent sessions explained: bounded execution history, identifiers, and resumable context.
- Agentic AIAgent skillsAgent skills explained: reusable procedures that give AI agents specialized knowledge without filling every prompt.
- ProductionAgent state machineAgent state machines explained: explicit workflow states, transitions, and recovery paths.
- Agentic AIAgentic AIWhat agentic AI is, how it differs from a chatbot or basic automation, and when a company should build one.
- Agentic AIAgentic workflowAgentic workflow explained for business leaders: how it differs from a rigid script, and where the human gate sits.
- Agentic AIAI agentWhat an AI agent is, how it differs from agentic AI and a chatbot, and what it needs to run safely.
- Agentic AIBackground agentBackground agents explained: asynchronous execution, status, cancellation, and safe limits.
- Agentic AICoding agentCoding agents explained: how they inspect repositories, edit code, run tests, and prepare multi-step changes.
- Agentic AIDeep agentsDeep agents explained: the planning, subagent, and filesystem layers that let an agent finish long, multi-step tasks.
- Agentic AIDeep research agentsDeep research agents explained: the plan, search, read, iterate loop, how they differ from RAG pipelines, and where their reports still need verification.
- Agentic AILong-running agentLong-running agents explained: durable state, checkpoints, recovery, and safe resumption.
- Agentic AIMulti-agent systemsMulti-agent systems explained: when splitting one agent into several actually pays off, and the patterns that make it work.
- Agentic AIOrchestrator-worker patternThe orchestrator-worker pattern explained: how delegation and result assembly work, and its main failure modes.
- Agentic AIParallel agentsParallel agents explained: when concurrency helps and how to prevent shared-state conflicts.
- Agentic AIPlanner-Executor PatternPlanner-executor pattern explained: how separating strategy from execution makes complex agent work easier to inspect and control.
- Agentic AIReAct AgentReAct agent explained: the reason, act, observe loop behind tool-using AI agents and the controls it needs in production.
- Agentic AISubagentsSubagents explained: what isolation actually buys you, and when delegating a task to one is worth the overhead.
- Agentic AISupervisor AgentSupervisor agent explained: how a central agent routes work, manages context, and controls a multi-agent workflow.