Tool use is not an operating model
Running Codex or Claude Code does not by itself define requirements, design responsibility, verification, or release authority.
Consulting
Software Engineering-Based
AI Coding Agent Consulting
Vibe coding and coding agents such as Codex and Claude Code are expanding, but using a tool alone does not create operational quality.We design lifecycle stages, responsibilities, quality criteria, and approval points so agents assist engineering work systematically.Codex and Claude Code are examples, not required products or partnerships; the operating model remains neutral across IDEs and agents.
Why it matters
Control the change before optimizing code generation
Coding agents assist development but do not replace product accountability.Sustainable delivery needs bounded work, evidence, and human decisions rather than tool adoption alone.
Unbounded change creates risk
Without explicit scope and stop conditions, an agent can touch unrelated files, dependencies, or deployment settings and increase regression risk.
Quality needs reproducible evidence
Teams need to know who approved what and which checks passed on the latest change, not only how quickly code was generated.
Principles
Design around stages, accountability, and evidence
Software engineering and AI consultants assess the customer's process and product risk, then define where an agent assists and where people remain accountable.
Stages and responsibilities first
Define inputs, completion criteria, Agent and human responsibilities, and entry conditions from requirements through operations.
IDE- and agent-neutral
Separate roles, Skills, evidence, and approval rules from a specific vendor so the model can work across coding environments.
Risk-based control
Adjust quality gates and approval strength to the product family, regulation, security exposure, and change impact.
Pilot before scaling
Start with a small pilot, measure the baseline and failure modes, then expand using observed evidence.
Lifecycle
Connect AI roles from requirements to operations
Each stage defines the required Skill and tools, completion criteria, human approval point, and condition for moving forward.Agents cannot widen scope or implement code without the required evidence.
01 Requirements and planning
Confirm sources, acceptance criteria, impact boundaries, and prohibited work; a Planner structures the work and verification plan.
02 Design and risk review
Review architecture, interfaces, data flow, and security impact; people approve high-risk design before implementation.
03 TDD and implementation
Create failing tests and completion criteria first; an Implementer makes the smallest change within approved files and commands.
04 Review and cross-check
Reviewer, Tester, and Security Reviewer independently examine intent, regression, test adequacy, vulnerabilities, and data exposure.
05 CI/CD and release
Confirm static analysis, complexity, coverage, dependency audit, and E2E on the latest head, then use only approved branches and deployment procedures.
06 Operations and improvement
Record decisions, commands, tests, and deployment evidence; stop and escalate under a fail-closed rule when evidence or authority is missing.
Agent & Skill Architecture
Separate responsibilities and standardize repeatable work
A Subagent is a role unit such as planning, implementation, testing, or security review that enables cross-checking.A Skill is a bundle of procedures and tool-use rules for performing repeatable work consistently.Each role receives only the minimum authority it needs.
Planner
Structures requirements, impact, risk, work units, verification, and approval points.
Implementer
Works test-first within the approved plan and minimum permissions without expanding the requested scope.
Reviewer
Checks requirement fit, design consistency, complexity, and maintainability independently from implementation.
Tester
Designs normal, boundary, failure, and regression checks and confirms reproducibility on the latest head.
Security Reviewer
Examines secrets, personal data, dependencies, external communication, permissions, and deployment changes before merge.
Task-specific Skills
Package procedures, checklists, tools, expected evidence, and stop conditions for repeatable engineering activities.
Guardrail & Human in the Loop
Limit what AI can change and place people where risk increases
Guardrails restrict the files, commands, data, external communication, merge, and deployment authority available to AI.Human in the Loop is not one final sign-off: people approve or stop work when requirements are fixed, high-risk design is chosen, dependencies are added, security or personal data is affected, and deployment or operations change.
Files and commands
Allow only the required repositories, directories, files, and commands; prohibit destructive operations and unapproved dependency changes.
Data and external communication
Protect secrets, personal data, customer data, logs, network access, external services, and information transfer.
Git and deployment authority
Define branch, commit, PR, latest-head CI, merge, QA, Production, and operations permissions separately.
Requirements and high-risk design
People confirm acceptance criteria and approve shared architecture, data, authentication, and authorization changes.
Dependencies, security, and privacy
Review need, license, vulnerabilities, data flow, and ownership before allowing packages or external access.
Stop and escalation
Stop on ambiguity, policy conflict, failed checks, secrets, or work outside authority, then escalate to the accountable person.
Quality & Security Gates
Set strict criteria according to product risk
We do not impose or guarantee one number for every customer.As a strict recommended example, Synetics proposes TDD, aiming for 100% statement and branch coverage, and no exclusion from required verification.Actual completion criteria are agreed according to product risk, regulation, change scope, and customer policy.
Define completion criteria for the product's risk, regulation, and repository policy
Use TDD and a strict example of 100% statement and branch coverage with no exclusion
Automate complexity, static analysis, type checks, code review, and security rules
Audit dependency vulnerabilities and licenses and block unapproved packages
Run unit, integration, E2E, accessibility, and CI/CD checks on the latest head
Fail closed and escalate instead of merging or deploying when checks, evidence, or authority are missing
Pilot & Enablement
Start with a small pilot and role-based training
Training covers the coding-agent workflow, Subagent and Skill design, permitted and prohibited AI work, review and approval, and failure handling in a real repository, not only prompt usage.
Current-state assessment
Assess the development process, product risk, repository rules, tools, quality indicators, and approval structure.
Pilot design and execution
Apply Agents, Skills, guardrails, and Human in the Loop to one or two representative tasks and measure quality, lead time, and failure patterns.
Role-based training and coaching
Developers, reviewers, QA, security, and leaders practice their responsibilities, evidence, approval, and escalation paths.
Operational adoption and scaling
Improve Skills and policy from pilot results and agree on product-specific scope, ownership, and the rollout roadmap.
Deliverables
Leave standards and evidence the organization can operate
The engagement connects guidance to repository policy, Agent and Skill configuration, approval checklists, training, and pilot operation.
AI coding operating model
Lifecycle AI roles, human accountability, inputs, outputs, and completion criteria.
Role and responsibility matrix
Agent and human ownership for implementation, review, testing, security, approval, and escalation.
Agent and Skill design
Purpose, tools, evidence, authority, and stop conditions for Subagents and repeatable Skills.
Repository instructions and guardrails
AGENTS.md or equivalent policy, branch and commit rules, commands, data, network, merge, and deployment boundaries.
Review checklists and training
Risk-based Human in the Loop checklists plus role-specific education, workshops, and coaching.
Pilot evidence and rollout roadmap
Observed results, failures, improvements, remaining risk, and a measured expansion plan without guaranteed speed or cost claims.
FAQ
Common questions about engineering-based AI coding agents
We review the current process, product risk, accountability, and pilot objective before selecting tools.
Is a specific coding agent such as Codex or Claude Code required?
No. Codex and Claude Code are examples, not required products or partner offerings. We select tools for the customer's environment while keeping lifecycle stages, roles, Skills, guardrails, and approval evidence independent from a vendor or IDE.
Must we replace the existing development process?
No. We first assess existing requirements, design, review, testing, CI/CD, and approvals. Proven practices remain, and agent assistance is introduced in a small, suitable part of the workflow.
When do people intervene in Human in the Loop?
People do not wait for one final approval. They approve or stop work when acceptance criteria are fixed, high-risk design is chosen, dependencies are added, security or personal data is affected, and merge, deployment, or operations change.
How are security and code quality controlled?
Guardrails restrict files, commands, data, external communication, and deployment authority. TDD, coverage, static analysis, dependency audit, independent review, and latest-head CI are combined, and work stops when checks or evidence are insufficient.
How does a pilot begin?
We select a small but representative task and measure the current baseline. After applying Agents, Skills, approval rules, and evidence capture, we compare quality, lead time, failures, and rework, then agree on improvement goals and expansion from actual results.
Assess the current process and product risk, then validate the Agent, Skill, guardrail, and Human in the Loop operating model in a small pilot.