---
id: "framework-strategic-ai-delegation"
type: "framework"
source_timestamps: ["00:01:59", "00:02:52"]
tags: ["delegation", "code-architecture"]
related: ["concept-tactical-vs-strategic-programming", "concept-ai-harness", "action-shift-to-strategic"]
---
# Strategic AI Delegation Framework

## Purpose

To effectively delegate tactical programming to AI agents, developers must adopt a strategic framework that shifts effort *away* from writing implementation details and *toward* upfront design. This creates the [[concept-ai-harness|harness]] in which agents can operate safely and efficiently.

## Steps

1. **Design the hard architectural parts of the system upfront** before any code is written.
2. **Scope tasks into discrete, easily understandable units** for the AI (one task per agent, one PR per task).
3. **Define clear interfaces and boundaries between modules** in the codebase so AI changes localize cleanly.
4. **Create test seams and comprehensive test scenes** to validate AI output automatically — the harness's quality gate.
5. **Maintain documentation that points the AI to the correct context** for making changes (READMEs, ADRs, agent skill docs).

## Why each step matters

- **Upfront architecture** prevents an infinite fleet of tactical agents from generating massive amounts of poorly-shaped code.
- **Scoped tasks** enable the queue-based execution model — see [[concept-agentic-queues]].
- **Clear interfaces** let agents make localized changes without cascading regressions.
- **Test seams** give the agent a verifiable goal and the human a verifiable artifact to review.
- **Context-pointer docs** save the agent from blind exploration and reduce hallucinated workflows.

## Outcome

The developer is positioned to leverage an *infinite fleet of tactical programmers* (AI agents) — without micro-managing each line. The corresponding action is [[action-shift-to-strategic]]; the prerequisite mindset is [[prereq-strategic-programming]].