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Separate agents from deterministic code
Do not let an agent self-correct within the same prompt or skill. Use an external SDK to run a linter, type-checker, or unit test after the agent finishes its work. Feeding raw error logs back to the agent from a real compiler ensures it responds to objective reality rather than its own hallucinations.
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Assign specialized roles to different agents
Split complex tasks into scout, plan, and build phases. A scout agent gathers relevant documentation and context, while a plan agent maps out the strategy before the build agent writes a single line of code. This specialization keeps the context window clean and prevents the executing agent from getting overwhelmed by discovery work.
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Implement sandboxed parallelism
Avoid letting agents work in the same environment where they can interfere with one another. Spin up isolated sandboxes for specific tasks like bug fixes or feature requests. For critical issues like production crashes, run multiple agents in parallel sandboxes and merge the first result that successfully passes your automated test suite.
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Engineer the system instead of the application
Shift your focus from manual coding to building the agentic layer that manages the work. Your primary roles are planning at the start and validation at the end. By building a repeatable factory workflow, you create a system that can execute hundreds of tasks with minimal human intervention.
Why it matters
For solo builders and small teams, scaling is impossible if you have to babysit every AI output. These workflows transform AI from a temperamental assistant into a reliable assembly line. This approach allows you to ship faster and maintain higher code quality by using deterministic code as a guardrail for non-deterministic agents.