Agent Device
CLI to control iOS and Android devices for AI agents
What you get
Your conventions, architecture rules, and review checklists turned into agent instructions.
Changes flagged when behavior shifts or coverage is missing.
Weak abstractions and architectural shortcuts called out with suggested fixes.
Tuned severity and suppression, so the agent earns trust instead of spamming nitpicks.
Reviews grounded in your codebase and history, not generic lint advice.
Time-to-merge and human review effort tracked before and after.
Scope → Build → Scale
We select a product workflow, define where agents act, and agree the controls and success measures.
We connect context, agents, and verification to your tools, then run the workflow on your product backlog.
We document the workflow, transfer ownership, and extend it across teams and products based on results.
Case studies

Code Review Agents
Agent review on every PR, human judgment where it counts.
Their engineers proved to be a crucial part of our mobile app relaunch project.

We’ll connect you with the right expert within 24 hours.
Still have questions?
No. It clears the mechanical layer - standards, regressions, test gaps - so the human pass is about design and intent, and takes a fraction of the time.
Linters check syntax rules; generic AI review knows nothing about your architecture. These agents are tuned to your conventions, your patterns, and your definition of done.
Yes - that is the core of the setup. Review checklists, architectural decisions, and team agreements become executable instructions, versioned in your repo.
Your choice: hosted models under enterprise terms, your cloud, or fully self-hosted inference for restricted environments.
Time-to-merge, human review minutes per PR, and post-merge defect rates, compared against your pre-agent baseline.
Initially we do, based on accept and reject signals from your reviewers. After handover, tuning is a documented process your team owns.