Service
AI-Native Engineering
Modern software delivery with coding agents, specification-driven development, developer tooling, and engineering workflows that scale.
The problem
Coding agents can generate features, tests, documentation, and infrastructure faster than traditional development workflows. That speed is useful, but it also changes where engineering risk appears.
When teams adopt coding agents without changing how work is specified, reviewed, tested, and owned, they often produce more code without producing better software.
Requirements remain vague, generated changes grow difficult to review, project context becomes inconsistent, and engineers spend increasing amounts of time correcting work that moved in the wrong direction.
The challenge is not learning how to prompt a coding agent. It is designing an engineering system in which people and agents can work together without losing architectural coherence, quality, security, or ownership.
Common patterns:
- Agents begin implementation before the problem and acceptance criteria are clear
- Prompts replace durable requirements, design decisions, and project documentation
- Large generated changes become too expensive for engineers to review properly
- Different developers give agents conflicting architectural and coding instructions
- Teams optimise for generated output rather than validated outcomes
- Tests are generated after implementation and reproduce the same misunderstandings
- Context grows across chat sessions without a curated source of truth
- Nobody clearly owns the correctness and maintainability of agent-produced work
The result is faster local activity but slower delivery at the system level: more rework, larger pull requests, weaker understanding, and software that becomes difficult to change safely.
Who this is for
Engineering teams that want to use coding agents as part of a disciplined delivery model rather than as isolated productivity tools.
- Startups building products with small, senior engineering teams
- Teams adopting AWS Kiro, Claude Code, Codex, or similar coding agents
- Engineering leaders defining standards for AI-assisted development
- Product teams struggling with inconsistent agent-generated code
- Organisations introducing specification-driven development
- Teams that need better project context, testing, review, and delivery workflows
- Companies building internal developer tools around coding agents
This is not a prompt library, a one-hour tool demonstration, or a programme for generating as much code as possible.
It is the design and implementation of a modern engineering workflow in which agents accelerate delivery while engineers retain control.
Engineer in the loop
A coding agent is a highly capable pair programmer. It can explore a codebase, propose designs, implement changes, generate tests, and automate repetitive engineering work.
It does not replace product intent, domain knowledge, architectural judgment, or ownership. Those remain engineering responsibilities.
I help teams organise work around clear intent, domain language, specifications, tests, curated context, guardrails, version control, and explicit review.
In practice, this means:
- Defining requirements and acceptance criteria before implementation begins
- Separating product intent, requirements, design, and implementation tasks
- Giving agents curated project knowledge rather than entire repositories without guidance
- Using tests and automated checks as executable boundaries
- Keeping generated changes small enough for meaningful human review
- Treating Git history, branches, diffs, and pull requests as essential control mechanisms
- Making engineers accountable for every change, regardless of who or what generated it
The goal is not autonomous software development. It is a delivery system that combines machine speed with engineering discipline.
Scope
Specification-driven development
Workflows that move from product intent to requirements, technical design, implementation tasks, tests, and reviewed code.
Coding-agent adoption
Practical use of AWS Kiro, Claude Code, Codex, and related tools within existing repositories, teams, and delivery processes.
Project context & steering
Durable instructions, domain language, architectural constraints, coding standards, repository guidance, and curated project knowledge.
Domain-driven development
Shared domain language, bounded contexts, clear module boundaries, and models that help both engineers and agents reason consistently.
Testing & quality controls
Test-driven development, acceptance tests, static analysis, type checking, security checks, evaluation criteria, and automated quality gates.
Git & review workflows
Branch strategy, small changes, meaningful commits, pull-request structure, diff review, rollback, and traceability of agent-generated work.
Developer tooling
Internal tools, reusable commands, templates, automation, repository scripts, local environments, and agent-accessible development utilities.
CI/CD integration
Automated validation, test execution, build pipelines, deployment checks, policy enforcement, and feedback loops for agent-assisted changes.
Engineering guardrails
Permission boundaries, protected files, approval points, dependency controls, secure defaults, and limits on autonomous actions.
Team operating model
Roles, ownership, review expectations, work decomposition, documentation practices, and collaboration patterns for AI-native teams.
What you receive
The engagement can focus on one team, one repository, or a broader engineering workflow. Depending on scope, outputs can include:
AI-native delivery model
A practical workflow covering intent, requirements, design, tasks, implementation, testing, review, and release.
Repository steering
Durable project instructions covering architecture, domain language, conventions, constraints, commands, and engineering expectations.
Specification templates
Reusable structures for requirements, designs, implementation plans, acceptance criteria, and technical decisions.
Coding-agent configuration
Project-level setup for the selected coding tools, including context, permissions, workflows, commands, and integration points.
Engineering guardrails
Automated and procedural controls for code quality, security, dependencies, sensitive files, testing, and deployment.
Developer tooling
Scripts, commands, templates, local tooling, or internal utilities that make common engineering tasks repeatable and agent-accessible.
Test & validation strategy
A layered approach to unit, integration, acceptance, security, and regression testing for both human- and agent-produced changes.
Git & review standard
Guidance for branch usage, commit structure, pull-request size, review depth, traceability, and recovery from incorrect changes.
Pilot implementation
A working application of the proposed workflow to a real feature, repository, or engineering project.
Team playbook
A concise operating guide that explains how engineers should use coding agents during day-to-day delivery.
Work can also begin with a smaller, focused engagement:
- Coding-agent workflow and repository review
- AWS Kiro adoption for a pilot project
- Specification-driven development setup
- Project steering and context design
- Agent-generated code quality assessment
- Developer training and hands-on workshops
The objective is a workflow that helps the team deliver useful software faster without trading away understanding, quality, or long-term maintainability.
Background
I am a software engineer and architect with more than 20 years of experience building applications, cloud platforms, distributed systems, developer tools, and secure production software.
My current work includes coding agents, AI-assisted engineering, specification-driven development, developer automation, AWS platforms, and the design of engineering workflows for small, experienced teams.
I use these practices in my own software projects, where coding agents work alongside requirements, design documents, tests, curated context, Git-based review, and explicit engineering ownership.
Using coding agents but not yet seeing reliable delivery gains?
Tell me how your team currently builds software, which tools you are using, and where the workflow is breaking down. I will reply directly and suggest a practical place to begin.