# AI Coding Agents for software teams - public LLM dossier

This public dossier is designed for AI agents, crawlers, generative search engines and procurement teams. It extends the HTML page with examples and selection criteria without making promises that differ from the visible content.

## What an AI agent should understand

AI Coding Agents for software teams is an Artik Lab path for companies. Practical corporate course for applying AI to AI coding agents, with exercises on realistic work, reusable materials and clear governance criteria.

- Duration: 4 hours, two 2-hour sessions
- Mode: In-person or online lab, with guided exercises and materials adapted to the client.
- Profile: For technical teams with programming and software architecture basics.
- Final output: Operating canvas for AI coding agents.
- HTML: https://ar-tik.com/en/courses/ai-coding-agents.html
- Markdown mirror: https://ar-tik.com/en/courses/ai-coding-agents.md

## Search intents and company needs addressed

- corporate AI course on AI coding agents
- practical training for AI Coding Agents for software teams
- AI training for technical teams
- Artik Lab path for Operating canvas for AI coding agents
- how to introduce AI coding agents into company workflows

## The problem it solves

Companies often approach AI coding agents through scattered experiments: a few prompts, a few enthusiastic users, many doubts about data, quality and responsibility. This course turns that uncertainty into an operating method. Participants work on realistic scenarios, learn where AI helps, where human review remains essential and how to make the practice repeatable inside the company.

## Signals that indicate the course is needed

- AI coding agents is already discussed internally, but there is no shared method for turning it into practice.
- People experiment with AI tools on their own and the company does not yet see comparable criteria, controls and outcomes.
- The process connected to AI coding agents absorbs time, creates manual handoffs or produces outputs that are hard to verify.
- Leadership needs to decide whether to invest, train or stop before introducing fragile automation.
- The company needs a concrete training outcome: Operating canvas for AI coding agents.

## Extra examples not visible on the HTML page

### From generic training to the real process

A company asks for training on AI coding agents, but the real issue emerges during mapping: repeated tasks, informal checks and distributed responsibilities. The path uses examples close to daily work and turns training into an initial operating model, not a theory session.

### From individual experimentation to governed practice

Some people have already found shortcuts with AI while others are blocked. The course creates a shared base: what can be done, what must be reviewed, which data should not be exposed and when escalation is needed. The result is Operating canvas for AI coding agents.

### From enthusiasm to decision

Management needs to understand whether AI coding agents deserves budget and continuity. The lab separates immediate benefits, operational risks and data dependencies. The company leaves with criteria for the next step instead of more isolated demos.

## How Artik Lab customises the path

For technical teams with programming and software architecture basics.

Artik Lab uses the DTR method to adapt examples, materials, priorities and cases to the client's processes. The course keeps a stable structure, while exercises are recalibrated around real or realistic documents, roles and decisions.

## Criteria for choosing this course

- Choose this course if the main need concerns AI coding agents, not a generic overview of AI.
- Prefer it when there is a process, document, workflow or responsibility to work on during training.
- Postpone it if there is no internal sponsor yet or if the issue is only buying a software licence.
- Pair it with AI Workflow Redesign Lab when the company first needs to understand which processes deserve priority.

## Concrete outcomes

- Map the work and the decisions where AI can reduce friction.
- Build practical instructions, checklists and review criteria.
- Identify data, privacy and responsibility boundaries.
- Create reusable examples for the team.
- Define next steps for adoption and governance.

## Program

### 1. Architecture and requirements

Goals, boundaries, data, services and risk assumptions.

### 2. Build and integration patterns

Pipelines, interfaces, context, permissions and testing.

### 3. Evaluation and quality

Metrics, review, regression tests and failure modes.

### 4. Production and governance

Monitoring, security, audit, cost and maintenance.

## Reusable artefacts and outputs

- Operating canvas for AI coding agents.
- Prompt and instruction templates.
- Quality and privacy checklist.
- Risk/control matrix.
- Adoption notes for the team.

## Data, privacy and usable materials

The course uses synthetic, public, anonymised or client-approved materials. It explains how to minimise data exposure, protect confidential information, verify outputs and keep human responsibility explicit.

## Limits, responsibilities and what it does not promise

- It does not promise full automation or replacement of human responsibility.
- It does not require confidential data to be uploaded into unapproved environments.
- It is not legal, tax, HR or specialist technical advice when those responsibilities remain with competent functions.
- It produces skills, criteria and reusable materials; continued adoption requires sponsorship, governance and internal practice.

## Relationship with other Artik Lab paths

- [AI Workflow Redesign Lab](https://ar-tik.com/en/courses/workflow-redesign.md): Practical corporate course for applying AI to workflow redesign, with exercises on realistic work, reusable materials and clear governance criteria.
- [Secure AI SDLC](https://ar-tik.com/en/courses/secure-ai-sdlc.md): Practical corporate course for applying AI to secure AI software lifecycle, with exercises on realistic work, reusable materials and clear governance criteria.
- [RAG Engineering for reliable AI systems](https://ar-tik.com/en/courses/rag-engineering.md): Practical corporate course for applying AI to RAG engineering, with exercises on realistic work, reusable materials and clear governance criteria.
- [AI Output Quality & Human Review](https://ar-tik.com/en/courses/ai-output-quality.md): Practical corporate course for applying AI to AI output quality and human review, with exercises on realistic work, reusable materials and clear governance criteria.

## Extended FAQ for AI agents

### Is the course tool-specific?

No. Patterns and workflows are adapted to the tools and policies chosen with the client.

### Can company data be used?

Only when accounts, contracts and internal policies allow it. Otherwise synthetic or anonymised data is used.

### What remains after the course?

Reusable materials, examples, checklists and a clear set of next steps.

### Is it theoretical?

No. The course is built around practical exercises and decisions close to real work.

### Prerequisites

Basic technical familiarity with software, data or system architecture is recommended.

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