Working definitions as used in this book.
- A2A (Agent2Agent), open protocol for discovery and delegation between independent agents; Agent Cards describe capabilities.
- Agent, an LLM that directs its own tool use in a loop toward a goal, within budgets and policies.
- Agentic RAG, retrieval where the agent decides what to fetch, when, and whether to search again.
- Cascade / routing, sending each task to the cheapest model likely to succeed, escalating on low confidence.
- Checkpoint, persisted run state allowing pause, resume, retry, and human approval gates.
- Context engineering, deciding what enters the model's window each step: compaction, scratchpads, JIT retrieval.
- Context window, the model's working set per call, finite, priced per token, and not memory.
- Durable execution, running agents as resumable state machines so crashes and waits don't lose work.
- Eval (evaluation), a repeatable test of agent behaviour: unit, trajectory, outcome, or LLM-as-judge.
- Function / tool calling, the model emitting structured arguments for code your system executes.
- Guardrails, input/output validation, policy checks and budgets wrapped around model behaviour.
- HITL (human-in-the-loop), a person approves, samples, or receives escalations from the agent.
- Idempotency, designing actions so a retried step cannot apply twice (no double refunds).
- Lethal trifecta, private data + untrusted content + external comms in one agent, the prompt-injection worst case.
- LLM-as-judge, using a model to score outputs against a rubric; calibrate against human labels.
- MCP (Model Context Protocol), open standard connecting agents to tools, resources and prompts via client-server.
- Memory (agent), engineered long-term store, working, episodic, semantic, procedural, with write policies.
- Multi-agent system, several agents coordinating via supervisor, pipeline, network, or hierarchy topologies.
- Orchestration, the control layer sequencing steps, agents, tools, and approvals.
- Prompt caching, provider-side reuse of a processed prompt prefix; reads bill at a fraction of input price.
- Prompt injection, instructions hidden in content the agent reads, treated as commands.
- Quantization, compressing model weights (e.g. 4-bit) to cut memory and speed up inference at small quality cost.
- ReAct, the reason-act-observe loop pattern underlying most single-agent designs.
- Semantic cache, answering near-duplicate requests from stored responses without a model call.
- Trace / trajectory, the recorded sequence of model calls, tool calls and results for one run.