--- title: "AI Coding Agent Training for Engineering Teams" canonical: https://wavect.io/services/ai-coding-training/ language: en description: "Hands-on training for software teams on AI coding agents such as Claude Code, Cursor and Codex: on your own repository, with review practice, secret hygiene and a way to measure the effect." image: "https://wavect.io/img/general/bak/open_graph_preview.jpg" --- AI Coding Training # AI coding agent training on your own codebase, not on a toy demo. Your engineers already have access to coding agents. What they lack is a shared way of working: how to give the agent the right context, how to review what it writes, what must never reach it and how to tell whether it actually helps. We train that on your repository, with the tools your team uses. THE ENGAGEMENT ## Your repository. Your tools. Rules the team keeps using. 1. **Prepare the codebase** Agent instructions, conventions and a set of real tasks. 2. **Practise on real work** Scoping, context, review and failure cases, hands-on. 3. **Measure and adjust** Agreed signals, a pilot period and a follow-up review. DIRECT ANSWER AI coding agent training teaches a software engineering team to work with tools such as Claude Code, Cursor or Codex on its own repository. It covers context setup, task scoping, code review, security and secret hygiene, and how the team measures whether the agents help. Best for - Engineering teams that use coding agents individually and inconsistently, and want shared practice - Tech leads who need review and security rules before agent use spreads further Consider another approach - Business teams looking for AI use cases outside software development - An app built with AI tools that now needs an audit and fixes ## Training for engineers who ship code, not an AI strategy session This service is for people who write and review code every day. Finding AI use cases across business processes belongs to our [workshops](/services/workshops/). Rolling AI out across a company, with tooling, guardrails and internal automation, belongs to [AI Enablement](/services/ai-enablement/). If an app has already been built with AI tools and now breaks, [Vibe Coding Rescue](/services/vibe-coding-rescue/) audits and fixes it. Enforcing checks in the pipeline, so agent-written changes pass the same gates as everything else, belongs to [DevSecOps](/services/devsecops/). The training prepares the team for those gates; it does not replace them. ## Work on your codebase, because that is where agents succeed or fail An agent that writes a clean demo app can still get lost in a large, real repository. Most of the difference is context, not model intelligence, as we argue in [The Bottleneck Was Never Intelligence](/blog/ai-coding-agents-context-not-intelligence/). So the sessions run on your code or a sanitized copy of it. Agent instructions A maintained instructions file with build commands, conventions and no-go areas, written for the tools the team actually runs. Task scoping Small, verifiable tasks with a plan first. When to let the agent run, when to stop it and when to write the code yourself. Tests as the feedback loop Agents work better when a test or type check can say no. We look at where your suite gives fast, honest feedback and where it does not. Repository context Docs, module maps and repo maps, and whether they belong in Git. Our [Graft review](/blog/graft-review-agent-repo-map/) covers that question for one tool in detail. ## Review practice for code nobody typed The author of an agent-written change is still the engineer who opens the pull request. The team agrees what that means: how large a change may be, what the author must have run and read before asking for review, how reviewers read diffs that look plausible but were never reasoned through, and which areas always need a second human. We practise this on real changes from your backlog, including ones the agent gets wrong. ## Secrets, permissions and what leaves the machine Coding agents read files, run commands and send context to a model provider. The training covers secret hygiene (no keys in prompts, instruction files or logs), the permissions each tool gets on a developer machine, sandboxing for commands, prompt injection through repository content and dependencies, and which code your contracts allow to leave the company. Technical enforcement in CI belongs to [DevSecOps](/services/devsecops/); here the team learns the habits that keep the pipeline from being the only line of defence. ## Measure whether it helps, instead of guessing We do not promise a productivity percentage. Results depend on the codebase, the task mix, the tools and the people. Instead, the team picks a few signals before the training, for example cycle time for a task type, review rework, defects found after merge and tool cost per engineer, and checks them again after an agreed pilot period. If the agent does not help on some kind of work, that is a valid result. Model choice and cost routing for one popular tool are covered in [How to Use Claude Fable 5.1 in Claude Code](/blog/coding-with-claude-fable-5/). ## Prepare, train, review after the pilot Scope, format and fee are agreed before booking. Remote sessions work internationally; on-site sessions are quoted separately. 1. **Preparation** - Team size and experience, tools in use or under evaluation, repository access or a sanitized copy, and a short list of real tasks to train on. 2. **Hands-on sessions** - Working sessions on the agreed tasks, ending with a written team playbook, an instructions file and the signals to track. 3. **Follow-up review** - After the pilot period, a review of the signals, the playbook and the failure cases. Further sessions are agreed separately. // proof ## Where the material comes from We have no published client case for this training yet, and we do not claim one. The material comes from Wavect's own daily engineering practice: we build client software and this website with coding agents every day, and we publish what we learn on the blog, including the articles linked above. That is our own practice, not a promise of the same result for your team. ## Questions before you start ### Which coding agents do you cover? The ones your team uses or is evaluating, for example Claude Code, Cursor, Codex or open-source agents. The training is vendor-neutral. We have no partnership with any AI vendor and sell no licences, so we can also say when a tool is a poor fit for your work. ### Do you need access to our source code? Working on your real repository gives the most useful training. If that is not possible, we use a sanitized copy or a public project with a similar stack. Access, data handling and which agent may see which code are agreed before the first session. ### Will the team be a certain percentage faster afterwards? We do not promise that. The training sets up a way for your team to measure the effect on its own work, and the follow-up review looks at the numbers you collected. ### Does it work for mixed experience levels? Yes, if the group is planned for it. Juniors and seniors need different rules about when to trust an agent, so we agree the participant mix and group size during scoping. ### Do participants receive a certificate? This is practical training, not an accredited certification. If you have formal training requirements, tell us during scoping and we agree what documentation is possible. ## Choose your next step - [Find AI use cases across the business](/services/workshops/) - [Roll out AI across the company](/services/ai-enablement/) - [Add pipeline controls for AI-assisted code](/services/devsecops/) ## Structured Data ```json { "@context": "https://schema.org", "@graph": [ { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ], "employee": [ { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Christof Jori", "url": 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