Everyone has their own tools and rules
Developers use coding agents differently, so useful practices stay private and risks stay inconsistent.
Your teams already code with AI. LRJI makes sure what reaches production stays verifiable.
LRJI helps teams set up AI-assisted delivery with coding agents: a real-scope pilot, explicit boundaries, automated checks, and enablement that keeps engineering judgment in control.
This is not an offer to build an LLM product. It is a delivery practice for teams using Claude Code, Codex, Copilot, or comparable tools, with evidence before integration and a measured approach to the gains.
When AI use needs a delivery practice
The question is not whether individuals can produce more code. It is whether the team can keep the changes understandable, reviewable, and safe to integrate.
Reviews no longer keep up with the volume
More generated changes arrive without an equally clear way to establish what has actually been checked.
Nobody knows what an agent may touch
The team has not defined the code, configuration, or infrastructure boundaries where an agent may write.
Secrets travel in prompts or configuration
Credentials and sensitive context need explicit controls instead of informal habits.
Leadership wants a measured gain
Speed claims are easy to make; a useful practice defines what to measure before promising an outcome.
What LRJI helps put in place
The work focuses on delivery controls that a team can understand, run, and improve itself.
- Write boundaries
- Define what an agent may change, what remains human-owned, and where a review or explicit decision is required.
- Checks before integration
- Use automated checks and review evidence so generated changes meet the same integration standard as any other work.
- Specification before delegation
- Give an agent a bounded objective, acceptance evidence, and a clear stopping condition rather than an open-ended prompt.
- Shared instructions
- Keep team guidance portable across tools so the method is not locked to one vendor or one person's configuration.
Guardrails that keep delivery accountable
Coding agents can accelerate implementation; the team still owns the decisions, the boundaries, and the proof.
Judgement is delegated to the model; evidence is not
Every change still needs observable checks and an integration decision that the team can explain.
Start with a real scope
A pilot earns its place by helping a current delivery problem, not by becoming a detached demonstration.
Make safe behaviour the easy path
Instructions, checks, and permissions should guide ordinary work without relying on memory or heroics.
Measure before claiming
The team agrees on signals for throughput, quality, review load, or rework before drawing conclusions about gains.
From current use to a transferable practice
The format is intentionally incremental: it establishes evidence on one useful scope before extending the method.
- 01
Diagnose current usage
Map tools, practices, risks, bottlenecks, and the delivery work where a pilot would be meaningful.
- 02
Pilot on a real scope
Set a bounded objective, write boundaries, acceptance evidence, and the checks needed before integration.
- 03
Generalize with guardrails
Turn what worked into shared instructions, CI checks, review habits, and security controls.
- 04
Hand over to the team
Transfer the practice, measurement, and decision points so internal ownership can continue.
Concrete delivery assets
The output is a working practice, not a slide deck about AI.
- A coding-agent usage charter for the team.
- Explicit write boundaries and escalation rules.
- CI checks and integration evidence for generated changes.
- A specification workflow before delegation.
- Shared instructions usable across coding-agent tools.
- A measurement dashboard for the pilot's agreed signals.
Proven on LRJI's own projects
The practice is proven on the projects of LRJI and Mohammed Hamdoune, not yet on client rollouts.
Agent pass
Agent-pass: controlled work before integration
LRJI's own harness checks, signs, and accounts for every agent commit before it returns to the integration branch.
29 ADRs
Emovanti: a SaaS delivered under those controls
Emovanti, Mohammed Hamdoune's SaaS, has more than 200,000 lines of test code and 29 architecture decisions; its pre-production environments are deployed and its beta is in preparation, with no production users.
14 years
Architecture judgement behind the guardrails
Fourteen years of software architecture and delivery inform the boundaries, checks, and decisions that make the practice useful.
Continue from the pilot
AI-assisted delivery is stronger when technical leadership and architecture decisions keep the practice connected to the real system.
FAQ
Does it replace my developers?
No. It equips developers and technical leads with a controlled practice. The team remains responsible for intent, review, architecture decisions, and integration.
Which tools does it support?
Claude Code, Codex, Copilot, and comparable coding agents can fit the practice. The method keeps instructions and controls portable rather than locking the team to one vendor.
What about the security of our code?
The pilot makes secrets, permissions, write boundaries, and checks explicit. It does not treat prompt habits as a substitute for security controls.
Bring the delivery scope where AI use needs to become accountable.
LRJI can help frame a real-scope pilot, the evidence it needs, and the guardrails the team can own afterwards.