The AI Coding Dictionary: Core Terminology for Agentic Software Development

Linkaihero.devResource

Published by aihero.dev, The AI Coding Dictionary provides clear definitions of essential concepts in AI coding across seven structured categories.

Key points

  1. Model mechanics: Outlines model parameters, token types (input, output, cache tokens), inference, next-token prediction, and the harness.
  2. Sessions and context: Explains context windows, turns, system prompts, statefulness, and how agents operate over session boundaries.
  3. Tools and environment: Details interaction with filesystems, MCP (Model Context Protocol), sandboxes, permissions, and tool calls.
  4. Failure modes: Categorises hallucination types, sycophancy, knowledge cutoffs, attention degradation, and drift from the 'smart zone' into the 'dumb zone'.
  5. Handoffs: Describes strategies for carrying context forward, including compaction, autocompact, specs, tickets, and handoff artifacts.
  6. Memory and steering: Clarifies mechanisms like AGENTS.md, context pointers, progressive disclosure, modular skills, and subagents.

Why it matters

As development workflows shift from basic completions to autonomous coding agents, consistent terminology is vital. The dictionary establishes precise concepts for practitioners navigating agentic toolchains.

Analysis

The glossary breaks down agentic development with technical clarity, strictly differentiating the stateless model (which only performs next-token prediction) from the surrounding harness that manages tools and context. Crucially, it highlights session failure modes—such as attention degradation as the context window expands—and practical handoff techniques to mitigate loss. Concepts such as the 'software factory' and 'dark factory' illustrate how unattended agent sessions and automated checks are formalising modern software workflows.

Found in

Summary generated by AI (Gemini 3.8 Flash, 28 September 2026). It may contain errors – the original source is authoritative.

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