Principle 9orchestration

Represent delegated work as a system, not merely as a conversation

Where work involves multiple steps, agents, dependencies, or concurrent activities, it should be represented as a structured system rather than solely as a message stream.

Key Facts

Cluster: orchestration
Primary risk: Complex work is reduced to an unstructured narrative and becomes difficult to govern.
Related examples: 6 library examples
Doctrine library: Internal doctrine reference

Why does this principle matter?

A conversational log is not always an appropriate representation for operational complexity. Users need mechanisms to understand relationships, concurrency, and progression across delegated tasks.

Use task views, status layers, timelines, maps, boards, or orchestration frameworks where appropriate.

Separate conversational exchange from execution state.

Show dependencies and relationships across subtasks.

Make concurrent activity understandable at a glance.

What failure does this principle prevent?

Complex work is reduced to an unstructured narrative and becomes difficult to govern.

AI as interface embellishment: A conventional product is given a text input and labelled intelligent, without any meaningful change in operational model.

Simulated autonomy: The system appears autonomous in language or presentation but cannot act with meaningful independence.

Opaque execution: Work occurs in the background without adequate status, accountability, or recoverability.

How do I apply this in practice?

The implementation library contains inspectable, runnable examples that show this principle applied in real agentic product code. Study the source paths, library choices, and runtime assumptions — not just the pattern name.

Implementation example

Level 1: Augmented LLM — Single API Call

One model call with structured output, system prompt, and context. No loops, no tools. (script · advanced)

Inspect implementation

Implementation example

Level 2: Prompt Chains & Routing — Deterministic DAGs

Multiple LLM calls in a fixed sequence. Code controls the flow, not the model. (script · advanced)

Inspect implementation

Implementation example

Level 3: Tool-Calling Agent — Scoped Autonomy

The agent decides which tools to call and in what order, but only within a fixed set of well-defined capabilities. (script · advanced)

Inspect implementation

Key Facts

Related examples