Defining the Right Way to Use AI: From Brain‑Like Models to Body‑Ready Agents

Rethinking the AI Metaphor

The article discards the popular "large model is a horse, Harness is a saddle" analogy and proposes a more accurate view: large models are like a brain, while the surrounding Agent system should be regarded as the body. The core problem is not model intelligence but the immaturity of the body.

Four Immature Body Components

3.1 Sensory System – Multimodal models can see and hear, but the input quality is unstable (e.g., PDF parsing errors, noisy web scraping, incomplete speech‑to‑text context). The analogy is a retina that has not fully developed.

3.2 Motor System – Tool calling provides "hands and feet," yet actions are unreliable: wrong parameters, UI mis‑clicks, environment mismatches, and missing feedback loops, akin to an unsteady neuromuscular junction.

3.3 Resource Scheduling – Large models consume high token and latency resources. Current agents either under‑provide context (broken reasoning chains) or over‑provide (prompt overload), reflecting a primitive "blood supply" system.

3.4 Autonomic System – Real bodies regulate heartbeat, breathing, etc. Agents lack automatic health‑checks, retry mechanisms, context compression, degradation strategies, and monitoring, relying on hard‑coded if‑else logic.

The Vacuum of Best Practices

Rapid AI progress outpaces methodological convergence. Just as cities needed traffic rules and infrastructure, AI needs stable engineering practices. Prompt engineering is likened to "asking for directions"—highly dependent on wording, model version, and temperature. Retrieval‑Augmented Generation (RAG) is compared to a static map that does not reflect real‑time conditions. Agent frameworks are described as "assembling prosthetic limbs" with inconsistent interfaces and state management.

Harness as the ICU

Given the immature body, Harness Engineering is not a saddle but an intensive‑care unit (ICU). Its core capabilities include lifecycle monitoring (token usage, latency, error rates), resource maintenance (context supplementation or compression), signal regulation (noise filtering, action risk constraints), and fault rescue (automatic failover paths).

AI‑Generated PPT: A Representative Scenario

The article uses AI‑generated presentations to illustrate the full agent pipeline: requirement input → research augmentation → outline generation → task decomposition → visual/content generation → editing & delivery. Each step maps to a body organ (e.g., document parsing = sensory system, outline = prefrontal cortex, task board = nervous system).

Iterative Convergence in the vivoPPT Project

Initially the system generated outlines and offered many templates, leading to instability because both content structure and visual choice varied. The team converged to a fixed‑template, content‑first approach, requiring richer raw material from users and a clear separation between outline and page generation.

Later, a domain‑specific language (DSL) was introduced as an intermediate representation, turning page generation into a structured, editable artifact and enabling reliable editing, validation, and export.

When rich‑text input was added, new challenges emerged (image semantics, hierarchical parsing). The solution added context extraction for titles, lists, tables, and image captions, reinforcing the need for a complete sensory system.

Emerging Best Practices

Research before writing

Outline before page creation

Task decomposition before parallel execution

Editable representation before delivery

These practices are not designed a priori; they emerge from repeated real‑world failures and refinements.

Looking Ahead

The article predicts that future discussions will no longer ask "whether to use AI" or "whether to use agents"—they will become default actions, like navigation today. The decisive shift will occur when we understand when to let the system think, act, use tools, follow workflows, or involve humans, turning AI from a collection of capabilities into a long‑term usable system.