AI-driven engineering

From coding assistance to verified delivery loops.

The shift is not from engineers to tools. It is from manual implementation to an operating model where humans specify intent, agents execute bounded work, and the harness proves what changed.

Explore the loop See the journey
01 Specs before prompts
02 Agents inside harnesses
03 Evidence before scale
3 maturity stages
5 loop steps
4 governance tests
1 measured workflow first
Journey

Three stages from autocomplete to agentic-scale delivery.

AI-augmented

Engineers use AI for explanation, refactoring, tests, and local implementation help. Productivity improves, but the process is still centered on individual prompting.

Primary artifact Prompt, code suggestion, local test.
Leader question Are teams learning the tool or changing the workflow?
Core principle

The model proposes. The harness disposes.

Delivery loop

Spec, plan, implement, validate, replan.

01

Constitution

Capture mission, architecture, constraints, coding standards, and non-negotiables.

02

Feature spec

Define outcome, edge cases, acceptance tests, and what must not change.

03

Agent plan

Ask the agent to decompose work before touching files or running tools.

04

Implementation

Delegate bounded tasks with clear permissions, context, and test commands.

05

Validation

Review diff, tests, trace, cost, rollback path, and the next replan.

Governance

Forward the constraint before the capability.

When to use agents

Use autonomy where evidence is inspectable.

Parallel agents can cover more ground, but they also consume more context and compute. The discipline is to name cost, failure modes, and stop conditions before the architecture becomes fashionable.

Clear outcome and inspectable artifacts.
Reliable verifier: tests, traces, or held-out review.
Bounded, reversible steps.
Explicit owner for escalation and release decisions.
Decision table

The delegation question moves from can it code to can we verify it.

Work type
Good first delegation
Required evidence
Refactor with no behavior change
Agent drafts, engineer reviews boundaries.
Tests unchanged, diff readable, performance stable.
Feature slice
Agent implements a bounded spec.
Acceptance tests, trace, screenshots, rollback path.
Architecture change
Agent explores options, humans decide.
Trade-off memo, dependency map, migration plan.
Course signals

The public curriculum is moving toward disciplined agentic delivery.

Agentic AI: reflection, tool use, planning, multi-agent workflows, evals, and production deployment Build with Andrew: building and refining web applications through prompting Spec-Driven Development with Coding Agents: constitutions, feature specs, validation, legacy support, and portable workflows