AI Coding Taxonomy & Agent Experience

A rigorous, standardized vocabulary for reasoning about AI models, harnesses, attention dynamics, collaboration modes, and developer/agent experience, compiled from matt pocock’s AI Hero curriculum.

flowchart TD
    subgraph ModelLayer [1. Model & Inference Layer]
        M[Model / Parameters] -->|Inference| P[Next-token prediction]
        P --> TOK[Input / Output / Cache Tokens]
        P --> PR[Prefix Cache]
    end

    subgraph AttentionLayer [2. Attention Dynamics]
        TOK --> AR[Attention Relationship: ~N² pairs]
        AR --> AB[Attention Budget per Token]
        AB -->|Context Accumulation| AD[Attention Degradation]
        AD --> SZ[Smart Zone vs Dumb Zone]
    end

    subgraph HarnessLayer [3. Harness & Environment]
        H[Harness / System Prompt] -->|Manages| C[Context Window]
        H -->|Tool Call| ENV[Environment / Filesystem]
        ENV -->|Tool Result| H
        H -->|Permissions / Modes| SB[Sandbox]
    end

    subgraph LifecycleLayer [4. Context Lifecycle & State]
        C --> CL[Clearing]
        C --> HA[Handoff Artifacts: Spec & Ticket]
        C --> CP[Compaction / Autocompact]
        C --> MS[Memory System]
    end

    subgraph ExperienceLayer [5. Experience & Interaction Modes]
        DX[DX: Developer Experience] <--> AX[AX: Agent Experience]
        HITL[Human-in-the-loop] <--> AFK[AFK Unattended]
        VC[Vibe Coding] <--> HR[Human Review / Diffs]
    end

1. Model, Inference, and Attention Dynamics

• Model vs Harness: A model represents the frozen parameters (weights) tuned during training that performs stateless next-token prediction. It cannot act agentically alone. The harness surrounds the model with tools, system prompts, context-window management, permissions, and hooks.
• Inference & Token Accounting: Running a model generates output tokens from input tokens. Consecutive requests sharing prefixes leverage provider-side prefix caches, billing those tokens as discounted cache tokens.
• Attention Relationships: In a context of $N$ tokens, approximately $N^2$ attention relationships exist between token pairs. Because each token has a finite attention budget to distribute across the context, adding tokens dilutes attention.
• Attention Degradation & Smart Zone: As context accumulates, each token’s attention budget spreads across competing tokens, causing signal on critical relationships to shrink (attention degradation). This defines the smart zone early in a session versus the sloppier “dumb zone” later in extended sessions.
• Parametric vs Contextual Knowledge: Parametric knowledge is frozen in weights at the knowledge cutoff date; contextual knowledge consists of facts loaded directly into the context window.
• Sycophancy vs Hallucination: Sycophancy is confidently agreeable output shaped by RLHF preferences; hallucination represents confidently-wrong output divided into factuality (invented facts) and faithfulness (drift from loaded context).

2. Interaction Modes & Experience (DX vs AX)

• DX (Developer Experience): How easy a codebase and toolchain make it for humans to do good work (documentation, feedback speed, error clarity).
• AX (Agent Experience): How well the environment is configured for an AI agent to do good work—deterministic checks, deep module architecture, and easily retrieved context without prompt bloat.
• Human-in-the-Loop vs AFK:
  • Human-in-the-loop: Pairing with an agent in real time, reviewing diffs, and redirecting.
  • AFK (Away From Keyboard): Unattended execution where an agent works autonomously against automated validation suites.
• Human Review vs Vibe Coding:
  • Human Review: Reading and judging the actual code diff produced by the agent.
  • Vibe Coding: Accepting code without inspecting diffs, treating generated output as opaque.
• Automated Check vs Automated Review:
  • Automated Check: Deterministic pass/fail verification in the environment (tests, lints, typechecks, build).
  • Automated Review: Non-deterministic evaluation where another agent inspects code and exercises judgment.

3. Context Lifecycle and Handoff Mechanics

• Stateless vs Stateful: Models are stateless across requests; agents are stateless across sessions by default unless persistence mechanisms are added.
• Progressive Disclosure & Context Pointers: Loading only necessary context upfront while leaving context pointers (mentions pointing to external documents or skills) for on-demand loading.
• Clearing vs Compaction:
  • Clearing: Ending a session to restart with an empty context window (/clear).
  • Compaction: In-memory summarization of past history seeding a fresh session, trading fidelity for headroom.
• Handoff Artifacts (Specs & Tickets): Structured documents bridging work across session boundaries. A spec scopes multi-session architecture; a ticket scopes one bounded session.

Related Concepts

• context engineering
• smart zone
• context rot
• agent harness engineering
• agents md spec
• handoff artifacts
• progressive disclosure
• matt pocock