A coding agent can help a team move from a well-specified issue to a proposed code change. It may inspect a repository, edit a limited set of files, add or update tests, and draft documentation. That makes it a contributor to a development process, not an independent engineering team that can safely own a release.
Define a bounded assignment
- Provide the issue, relevant design notes, repository conventions, and a clear list of files or behavior in scope.
- Give the agent least-privilege access, no production credentials, and test data that contains no real secrets.
- Require a summary of changed behavior, assumptions, tests run, and unresolved questions.
The agent should work in an isolated branch or workspace. A human engineer checks the diff, challenges assumptions, and reviews security-sensitive changes. CI then runs formatting, static analysis, unit and integration checks, dependency and secret scans as appropriate. Only after CI and review should the change reach staging, where someone checks it in the real application context.
- Issue and constraints
- Agent change
- Tests and documentation
- Human review and CI
- Staging decision
The agent proposes a change; engineers and existing release controls decide whether it proceeds.
A hypothetical example
Imagine a product team maintaining an internal inventory app. It could ask a coding agent to add a filter to one list view, update a focused widget test, and revise the help page. A reviewer would confirm the filter's behavior, inspect the data access, and check the staging build before release.
Keep responsibility visible
Agents can misunderstand old conventions, produce tests that repeat their own assumptions, or make broad edits beyond the task. They should not approve their own changes, bypass required reviews, alter production data, or merge around CI. For migrations, authentication, payments, or personal data, use a named human owner and stronger review appropriate to the impact.
Track review time, accepted changes, defects found before release, test usefulness, rework, and escaped regressions. Compare with the team's ordinary baseline and include the time spent correcting agent work. More lines of code or more green test runs are not evidence of better software if maintainability and user behavior deteriorate.
Integrations without a fixed catalog
Slack, Telegram, WhatsApp, email, Jira, CRM, ERP and your own software: we build custom integrations with no fixed list or predefined number of connections. The scope depends on available APIs, permissions and each provider’s conditions.
Turn Jira tasks or requests from Slack, Telegram, WhatsApp and email into work for coding agents, then return pull requests, test results and review updates.
Teams can explore AI development services and private computing when repository or issue data must remain in an agreed boundary. Talk with Clutch: custom AI solutions start at €10,000, or scoped rental from €1,000 per month. Taxes may apply; written scope covers capacity, setup, hardware, and third-party costs.
FAQ
How much does an AI project cost?
The starting prices are €10,000 for a project and €1,000/month for monthly capacity rental. Your quote depends on scope, capacity, setup, hardware, external provider costs and applicable taxes.
What kind of company or team is this for?
It suits teams with a specific product or operational task, access to representative examples and a person who can approve the result. We first check whether AI is useful for that task; a conventional rule or software change may be simpler.
Will our data be sent to an external AI provider?
That depends on the agreed setup. We document data paths and access before choosing an external model or a private local setup.
How do you decide whether the AI is good enough?
We agree on representative inputs, acceptable outputs, failure limits and the human approval points before the pilot. Results depend on your data and workflow, so we measure against that agreed sample instead of guaranteeing a general accuracy figure.