Glossary

The practice of coordinating multiple AI agents (or a single agent with multiple tools) to accomplish complex tasks that exceed what any single agent can do alone. Orchestration encompasses: task decomposition (breaking a goal into subtasks), agent selection (choosing which agent or tool handles each subtask), execution sequencing (parallel vs sequential, dependencies), state management (tracking progress across agents), error handling (retries, fallbacks, escalation), and result aggregation (combining outputs into a coherent response). Common orchestration patterns include supervisor-worker, fan-out/fan-in, pipeline, and event-driven handoff.

A communication protocol, developed by Google, that enables autonomous agents to discover, negotiate with, and delegate tasks to other agents — without human mediation. Unlike MCP (which connects models to tools), A2A connects agents to agents. It supports capability discovery via Agent Cards (JSON metadata at /.well-known/agent.json), task lifecycle management (submitted → working → completed/failed), streaming updates via SSE, and multi-turn conversations between agents. A2A is designed for asynchronous, long-running tasks where agents collaborate as peers.

A design pattern where a human operator reviews, approves, or corrects an autonomous agent's actions at critical decision points before they are executed. HITL is the safety valve between full automation and full manual control. It can be implemented as: approval gates (agent proposes, human approves), escalation triggers (agent handles routine cases, escalates edge cases), correction loops (agent acts, human reviews and corrects), or oversight dashboards (human monitors agent activity in real-time). The key design decision is where to place HITL checkpoints — too many kills throughput, too few risks catastrophic errors.

An open protocol, originally developed by Anthropic, that standardizes how AI models connect to external data sources and tools. MCP provides a universal interface — similar to USB-C for hardware — allowing any AI model to call any tool through a consistent request/response format. It replaces brittle, per-tool API integrations with a single protocol layer. MCP servers expose capabilities (tools, resources, prompts) that MCP clients (AI models, IDEs, agent frameworks) can discover and invoke at runtime.