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Principle 1delegation

Design for delegation rather than direct manipulation

Design experiences around the assignment of work, the expression of intent, the setting of constraints, and the review of results, rather than requiring users to execute each step manually.

Key Facts

Cluster
delegation
Primary risk
The product remains structurally manual, with AI functioning only as an overlay rather than as an operational capability.
Related examples
6 library examples
Doctrine library
Internal doctrine reference
Why does this principle matter?

In agentic systems, value is created when users can define the desired outcome and rely on the system to carry out appropriate actions within agreed limits. The interface should therefore support delegation as a first-class interaction model.

Enable users to define goals, constraints, and preferences with precision.
Make the scope of delegated authority explicit.
Provide clear controls for initiation, pause, redirection, and termination of work.
Treat prompt input as one mechanism among several, not as the entire experience model.
What failure does this principle prevent?

The product remains structurally manual, with AI functioning only as an overlay rather than as an operational capability.

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

Related examples