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AI agents

Work with the right agents inside a governed engineering model

GitHub Copilot, OpenAI Codex, and Anthropic Claude can all support modern software delivery. The value comes from using them with clear intent, shared context, validation, and team-level operating patterns.

Default way of working

Unless otherwise agreed, delivery work happens in GitHub Copilot

For client work, GitHub Copilot is the default execution environment unless we agree on another setup. That keeps the work close to your repositories, issues, pull requests, review process, and engineering governance.

Other agents can be used when they fit the task, but the default operating model is GitHub Copilot.

Supported agents

The tools we commonly work with

We help teams decide where each agent belongs in the workflow, how context should be prepared, and how output should be validated before it reaches delivery.

01

GitHub Copilot

GitHub Copilot supports developers across the editor, command line, GitHub, and agent-enabled workflows. It is especially useful when teams want agentic work to stay close to repositories, issues, pull requests, and platform governance.

02

OpenAI Codex

OpenAI Codex is a coding agent for software development work such as understanding code, planning changes, implementing updates, reviewing behavior, and debugging. It can be valuable for focused engineering tasks where a coding agent needs workspace context and verification steps.

03

Anthropic Claude

Anthropic Claude is an AI assistant and model family often used for analysis, reasoning, writing, coding support, and long-context work. It can help teams explore options, review specifications, reason through architecture, and support implementation workflows.

How we choose

Agent choice follows the work, not the hype cycle

Decision area
What we look for
Repository proximity
Can the agent work naturally with the source code, issues, branches, pull requests, and review process?
Context quality
Can the team provide the right specifications, repository instructions, architecture context, and constraints?
Validation
Can generated output be tested, reviewed, traced, and governed before it becomes delivery work?
Team adoption
Can the practice be reused by teams instead of depending on individual prompting habits?
Governance fit
Can security, compliance, quality, and platform controls be embedded into the workflow?

What we help define

A practical agent operating model

  • Which agent should be used for which kind of engineering work
  • How specifications and repository context should be prepared
  • How agent output should be tested, reviewed, and documented
  • How pull requests should show traceability from intent to implementation
  • How teams should reuse prompts, instructions, and workflow patterns
  • How governance should apply without slowing developers down

Official references

Product information changes, so we anchor decisions in current vendor guidance

Need a clear agent strategy?

We can help decide how GitHub Copilot, OpenAI Codex, and Anthropic Claude should fit into your delivery model.

Book a discovery call