Claude and Codex agent engineering is how development teams get AI that works inside their actual codebase — not a chat window they copy-paste from. Software Depo sets up repository-aware coding agents with project instructions, reusable skills, and safe access to your systems, so the agent understands your code and follows your rules.
Agents That Know Your Project
A generic AI assistant starts from zero every conversation. A properly engineered repository agent carries persistent project knowledge: your architecture, conventions, and the reasons behind past decisions — captured in project instructions and reusable skills so successful workflows are not re-explained every time.
What We Set Up
- Repository-aware Claude Code and Codex agents with project instructions
- Reusable skills and workflows so proven procedures are repeatable
- MCP connections giving agents safe, scoped access to your internal systems
- Hooks and automation for tests, checks, and review steps
- Code-review and test-generation agents that assist without merging on their own
- Sandboxed execution and human approval before any change is published
Claude or Codex?
Both are capable; the right choice depends on your stack, your tooling, and where your team already works. We are not tied to one — we will recommend based on your repositories, your CI, and your security requirements, and set up whichever fits with the guardrails that keep it safe.
Safe by Construction
Coding agents touch source, credentials, and infrastructure — so we scope their access, sandbox execution, and require human approval before anything ships. The agent proposes; a person decides.
Tell us about your codebase and workflow — languages, repositories, CI, and what you want an agent to help with. We will set it up with the right guardrails.
Related services
- AI Adoption and Employee Training — Rolling these tools out to a team: AI adoption and training.
What Are Coding Agents Actually Good At?
Current coding agents are strong at bounded, well-specified work in a codebase they can read: implementing a described change across several files, writing tests, migrating a pattern consistently, explaining unfamiliar code, and doing the mechanical parts of a refactor. They are weakest where the requirement is ambiguous, where the judgement is architectural, and where the consequence of being subtly wrong is high.
The productive framing is not “does it replace a developer” but “which parts of the work are mechanical enough to delegate, with review”. That is a large fraction of most codebases.
How Do You Adopt Them Without Creating a Mess?
- Review everything. Agent output goes through the same review as human output. Teams that relax this accumulate code nobody understands.
- Tests are the guardrail. A codebase with good test coverage is one where agents are genuinely useful. One without is where they are dangerous.
- Give them the project context. Conventions, architecture and constraints recorded in the repository make output dramatically better than a general prompt does.
- Bound the permissions. What an agent may run, install and push should be an explicit decision, especially in CI.
- Keep secrets out. Credentials, customer data and proprietary keys need the same handling they always did.
What Does Rolling This Out to a Team Involve?
Less tooling than people expect and more convention. The work is setting up repository-level context so the agent understands the project, agreeing review standards, deciding what agents may and may not touch, integrating with existing CI, and training the team on where the tools help and where they do not.
The teams that get the most from these tools treat them as a new member who is fast, well-read, tireless, and occasionally confidently wrong — and who therefore needs the same code review everyone else gets.
Related: AI adoption and training · AI agent skills · AI and MCP security review.