Context, Guardrails and Tests: Running AI Coding Agents Well

AI coding agents make writing code cheap. The trouble is that the rest of the delivery process stays exactly as slow as it was, and an agent's output is only as good as the conditions around it. For a product team, three areas decide whether that speed turns into shipped software: context, guardrails and verification.

Treat context as a limited resource

A larger context window invites a bad habit: feeding an agent everything. In practice, quality tends to drop as a session fills up, and long sessions get compacted automatically, which can quietly discard details that mattered. More material is not better material.

Habits we favour:

• Start a fresh session for each finished task rather than carrying old conversation forward.
• Split large jobs across sub-agents, each of which has its own context window.
• Point the agent at an existing service or component as a reference, so that authentication, logging and error handling follow the patterns already in the codebase instead of a written description of them.
• Switch off tool servers a task does not need, because every tool description takes space in every request.

Rules files deserve the same care. Instructions written to compensate for a weaker model can hold a stronger one back, so review them whenever the model changes, and let the team's own corrections shape them instead of copying a generic template.

Use guardrails that enforce, not instructions that hope

Writing 'never delete files' in a rules file is a request, not a guarantee. Hooks that run before a tool call let a team inspect what an agent is about to do and block it deterministically. The same mechanism can run a linter every time a file is touched. Anything that must always happen belongs in enforcement, and anything that is merely preferred belongs in the rules.

Planning is the other early control. Ask the agent for a plan in a read-only mode, then correct its assumptions before any code changes. Agents tend to be helpful to a fault, adding authentication, logging or features nobody asked for. Reviewing the plan aligns the human with the agent, and it is far cheaper than untangling a large diff. Narrow, decomposed tasks give better output than a vague request for a whole product.

Move the bottleneck deliberately

When code is quick to produce, the constraints shift to either end: specifying clearly what to build, then verifying that it works. Verification needs a solid suite of unit and integration tests, so the agent can tell when it has drifted and knows what done means. It also needs a working development environment and a healthy CI/CD pipeline. Without them, faster code simply queues up behind the same gates. Giving an agent a browser to check its own work removes another manual step, though that access should be granted with care.

What this means for web development teams

The habits that make AI-assisted development reliable are ordinary engineering discipline applied earlier: clear specifications, tight scope, automated checks and human review of what ships. Teams that invest there get faster delivery. Teams that only speed up typing get faster piles of code to review.