Engineering lab
AI Engineering Workflow Lab
Reusable AI-assisted delivery patterns with quality gates and human accountability
A reusable control plane for AI-assisted engineering work. It makes the working loop explicit: triage scope, generate implementation and test artifacts, review against acceptance criteria, and keep architecture, risk acceptance, and deployment decisions with humans.
Project outcomes
What changed in the work.
- 4 stages
- feature workflow from triage through review
- Human gates
- architecture and production approval remain accountable
- Reusable
- maps and manifests narrow the context for each task
- Role
- Workflow design, harness implementation, and quality-governance patterns
- Scope
- ai · architecture · testing · security · leadership · platform
- Status
- Engineering lab
The problem
Start with the constraint.
AI can accelerate research and implementation, but unbounded context and unreviewed output make correctness, cost, and security harder to control. Teams need a repeatable way to benefit from assistance without delegating accountability.
What I contributed
- Defined reusable harnesses for feature work, code review, documentation, bug identification, and usage analysis.
- Created a model registry and task-routing rules that distinguish lightweight triage from implementation and review work.
- Used functionality and connection maps to give a task only the system context it needs.
Sanitized architecture
The control plane is intentionally simple: versioned task definitions, maps, a model registry, and artifact manifests. It creates a disciplined handoff between analysis, implementation, testing, and review rather than hiding the workflow behind a black box.
Maps
Describe repository capabilities, entry points, dependencies, and risk hints.
Harnesses
Define staged tasks such as triage, scaffold, test generation, and review.
Model registry
Routes work by task type and expected complexity.
Human review
Owns requirements, architecture, acceptance, risk, and deployment approval.
Engineering decisions
Context, choice, and tradeoff.
Start with triage instead of a broad implementation prompt
Context: Large repositories make it easy for an agent to reason from irrelevant or stale context.
Options: Provide the whole repository, or identify affected modules and acceptance criteria first.
Decision: Use a short triage stage that scopes modules, selects the task model, and emits a structured spec.
Tradeoff: Adds a deliberate up-front step, but prevents aimless implementation and makes the plan reviewable.
Result: The implementation stage receives a narrower, explainable context window.
Generate tests and review artifacts as explicit stages
Context: Generated code can look plausible while missing failure behavior or acceptance criteria.
Options: Treat code generation as the end of the task, or require separate test and review outputs.
Decision: Model test generation and review as dependent stages with their own inputs and outcomes.
Tradeoff: More artifacts to inspect, balanced by a clearer quality gate.
Result: AI assistance is framed as a contribution to an engineering loop, not a substitute for it.
Keep consequential decisions with people
Context: Architecture, risk acceptance, and deployment have business and security consequences beyond a generated patch.
Options: Automate approvals, or make human ownership explicit in the workflow.
Decision: Require human review and acceptance after static analysis and automated tests.
Tradeoff: Less automation theatre, with a more defensible ownership model.
Result: Faster research and implementation without blurring accountability.
Quality & operations
How the work is made operable.
- Task manifests make expected inputs, models, and generated artifacts visible.
- Static analysis, tests, and review are distinct from implementation rather than optional afterthoughts.
- The harness writes workflow artifacts; it does not claim to autonomously ship software.
Lessons
What carries forward.
- Good AI workflows make constraints and review paths more visible, not less.
- Requirements and test design are high-leverage human work.
- The best route to lower AI waste is often narrower context and clearer acceptance criteria.
Want to discuss the technical decision-making behind this work?
Contact Kyle