Coding Agents
Agents in the engineering process — tasks, context, an isolated environment, checks and review.
01 · The problem
An agent can complete a task end to end — read the code, make changes, run the tests, open a pull request. Yet in most teams it remains a “smart autocomplete”.
The model is not the problem. An agent needs three things that are usually missing: the context of your system, an environment where the project builds and runs its tests, and automated acceptance of the result.
02 · When you need it
- Developers use AI in the IDE but don't delegate whole tasks
- Agent output has to be substantially rewritten
- There is a steady stream of routine tasks — updates, migrations, tests, CI fixes
- You need to know which tasks can be safely handed to agents
03 · What we analyze
- Task classes and how suitable they are for delegation
- The state of repositories — build, tests, documentation, conventions
- How the agent will get context — rules, architecture, APIs, tasks
- The runtime environment — permissions, isolation, access to secrets
- The acceptance process — CI, review, accountability
04 · How we work
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step 01
Task selection
We choose task classes with clear acceptance criteria and measurable results.
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step 02
Laying the groundwork
Agent rules in the repositories, reproducible builds, tests as the definition of done.
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step 03
Pilot
The agent works on real tasks in an isolated environment, and a human reviews every pull request. We record what worked and what didn't.
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step 04
Embedding into the process
Task, agent, pull request, checks, review. Roles, limits, metrics.
05 · What you get
The exact set of deliverables is agreed after the diagnostic. I don't promise effect figures in advance — they depend on the starting point and are written into the plan as criteria.
- 01A list of task classes for agents with acceptance criteria
- 02Rules and context for agents in key repositories
- 03A working process from task to pull request
- 04Requirements for the runtime environment and security
- 05Pilot results — what works, what doesn't and why
Related services
Next step
Let's discuss your project
Tell me what is happening with your development and what you expect from AI. I reply personally and suggest a format — a diagnostic, an audit or a consultation.