Glossary of AI-Native Development Terms

A quick reference for the terms used throughout this book.

A2A (Agent-to-Agent). Patterns and protocols for agents to advertise capabilities, hand off tasks, and exchange results - the connective tissue of multi-agent systems.

ACP (Agent Client Protocol). An open protocol for connecting coding agents to the editor a developer works in, so one IDE can host agents from several vendors. Complements MCP (agent to tools) and A2A (agent to agent).

Agent / Agentic AI. An AI system that plans, calls tools, and takes multi-step actions toward a goal with limited human input - as opposed to a model that only generates a response.

AGENTS.md. A cross-tool standard markdown file giving AI coding agents concise, action-oriented project context (build, test, conventions).

CLAUDE.md. A comprehensive project context file for AI agents covering architecture, standards, and business rules; read by Claude Code and other tools.

Context Engineering. The discipline of optimizing what information an agent can see - retrieval, memory, and structured documentation.

Context Window. The maximum amount of text (measured in tokens) a model can process at once.

Eval. An automated check that measures whether an agent achieved its declared intent - outcome attainment, constraint adherence, escalation correctness, cost-per-outcome - not raw model capability.

FinOps for AI. Tracking and governing AI spend as visible OpEx: cost-per-workflow, cost-per-outcome, and budget guardrails.

Hallucination. A confident, plausible-sounding output that is factually wrong or unsupported.

Intent Engineering. The discipline of aligning agents with business outcomes by specifying what must be achieved - objectives, success criteria, constraints, and stop rules.

Intent Specification. A short, executable contract for an agent: objective, success criteria, constraints, tool permissions, escalation triggers, stop rule, and owner. Versioned in source control and enforced by evals.

Lethal Trifecta. The dangerous combination of private-data access, exposure to untrusted content, and an external communication path - the conditions under which prompt injection can exfiltrate data.

MCP (Model Context Protocol). The open, industry-standard protocol for giving agents governed access to external tools and data sources.

Multi-Agent System. Several specialized agents collaborating on a task, typically coordinated by a supervisor and connected via A2A patterns.

Prompt Caching. Reusing processed context across requests to cut latency and cost for repeated, stable context.

Prompt Engineering. Optimizing the wording of instructions to a model - now table stakes rather than a differentiator.

Prompt Injection. An attack where untrusted content carries instructions the agent follows as if they came from the user.

RAG (Retrieval-Augmented Generation). Retrieving the most relevant chunks from a corpus and adding them to the prompt so the model answers from the right context.

Reasoning Model. A model (or mode) that spends extra tokens and time on extended thinking to solve harder problems.

Subagent. A specialized agent spawned by a parent agent to handle a unit of work in parallel, with its own context.

Vibe Coding. Prompting your way to working software without closely reading the output - useful for prototyping, risky for production without intent and verification.