Where does agent reliability actually come from?

Drawing on Norman's concept of cognitive artifacts, this paper argues that the most consequential design choices in LLM agents are about externalization — relocating cognitive burdens from the model's internal computation into persistent, inspectable, reusable external structures. A shopping list doesn't expand memory; it changes recall into recognition. The same logic governs agent design.

Three dimensions of externalization address three recurrent mismatches:

1. Memory externalizes state across time. The context window is finite and session memory is weak. Memory systems transform recall into recognition — the agent retrieves past knowledge from a persistent store rather than regenerating it from weights. This solves the continuity problem.

2. Skills externalize procedural expertise. Long multi-step procedures are rederived rather than executed consistently. Skill systems transform generation into composition — the agent assembles behavior from pre-validated components rather than improvising each step. This solves the variance problem.

3. Protocols externalize interaction structure. Interactions with tools, services, and collaborators are brittle when left to free-form prompting. Protocols transform ad-hoc coordination into structured contracts (e.g., MCP). This solves the coordination problem.

The harness is not a fourth dimension — it is the engineering layer that hosts all three and provides orchestration logic, constraints, observability, and feedback loops. The progression is: weights → context → harness, paralleling the human history of cognitive externalization (speech → writing → printing → computation).

Critical system-level couplings:

• Memory expansion competes with skill loading for scarce context budget
• Protocol standardization can constrain how capabilities are packaged
• Skill execution generates traces that become memory; memory retrieval influences which skills and protocols are chosen

This reframes the question from "how capable is the model?" to "what burdens have been externalized so the model no longer has to solve them internally every time?" The base model may remain unchanged; what changes is the representation of the task.

This connects to Why do production AI agents stay deliberately simple? — the externalization framework explains why custom harnesses outperform: they externalize the right cognitive burdens for their specific domain. It also extends When should human-agent systems ask for human help? — Magentic-UI's mechanisms (co-planning, action guards, memory) are specific instances of the three externalization dimensions.

The "From Model Scaling to System Scaling" paper sharpens this into an explicit framing: model scaling (bigger models, more data, higher benchmark scores) versus system scaling (designing the auditable, persistent, modular, verifiable architecture around the model). It treats the harness as a first-class object of design, evaluation, and optimization, decomposing it into a foundation model, memory substrate, context constructor, skill-routing layer, orchestration loop, and verification-and-governance layer — a finer-grained partition of the same memory/skills/protocols externalization. Its central demonstration is that comparable models projected onto different harnesses (Claude Code, OpenClaw, and the released CheetahClaws reference harness) produce qualitatively different agents, making the harness "now a primary source of practical capability." This is direct evidence for the claim that reliability comes from the surrounding system, not from a larger model alone.