What are AI agents for software development?
AI agents for software development are AI tools that take a coding task, read the relevant code, make the change, run checks and report back, with more independence than an autocomplete assistant. Some work on request; autonomous ones pick up tasks from a backlog on a schedule. Their value depends on the tasks, rules and checks you give them.
Are AI coding agents safe to use on production code?
They can be, with controls. Give agents bounded tasks, limit what they may touch, make them follow the repository's rules, and accept work only when independent CI checks and tests pass. Keep a human approving every merge. Without those controls, agents produce code faster than it can be checked, which raises risk.
Do AI coding agents replace developers?
No. AI agents for software development change what developers spend time on. Agents handle well-defined implementation work; people still design the architecture and data model, decide what to build, review risky changes and own the code in production. Small teams gain the most, because AI coding agents for teams let a few senior engineers deliver more.
What is an agentic engineering workflow?
An agentic engineering workflow is a defined way of working where AI agents take tasks from a controlled backlog, follow written rules, record their decisions and must pass automated checks before work counts as done, with human decision points kept explicit. It turns agents from ad hoc helpers into a dependable part of delivery.
What does an AI code review agent check?
An AI code review agent reads each pull request before a human does, checking it against the architecture, the requirements and the house conventions, and flagging likely defects, missing tests and risky changes. It speeds up human review by catching the obvious early. It should support human approval, not replace it.
Which AI agent workflows has Muzammil built?
An autonomous scheduled build agent that advances the Omnitech CRM programme task by task, with a controlled backlog, runbook, source checkers and three CI jobs on MySQL and SQLite. I also used task-scoped agents and a review agent to build IMFlow360 with a two-person team.
What does Muzammil charge to set up AI agents?
A 60-minute Decision Diagnostic is $300. Hands-on setup and a pilot usually run on the Fractional CTO Plus plan at $2,800 a month for 40 hours, or the CTO + build plan at $4,800 for 80 hours. Ad hoc work is $75 an hour, with no long minimum term.
Which AI coding tools and models do you use?
I choose by your stack, risk and budget rather than one vendor. The controls (bounded tasks, repository rules, CI gates, a review agent and human approval) matter more than the tool, and they work with the main AI coding agents. Where code must not leave your network, self-hosted models are an option.
Can you set this up in our existing repository and CI?
Yes. The work goes into your repository and pipeline: agent instructions and runbooks in the repo, CI gates in your existing pipeline, and a pilot on your real backlog. Nothing depends on a platform of mine, so your team keeps running it after the engagement.
How do we start?
With a free call about your team, stack and how you use AI tools today. Then a $300 diagnostic or an assessment picks the first tasks and controls, and a pilot proves them on your backlog before the wider rollout. A monthly plan covers the setup, with no long minimum.
