Your AI Agent Doesn't Need to Be Smarter. It Needs Discipline

Description and use

Itqan enforces discipline in AI coding workflows—approval gates, evidence, trace logs, and convention detection—to produce shippable code with minimal rewrites.

Problem

AI coding agents produce code that is technically correct but not shippable because they skip essential engineering discipline—spec approval, context persistence, proof of correctness, and adherence to existing conventions.

Outcome

By embedding discipline into the AI workflow, Itqan turns AI agents from code generators into reliable engineering assistants that produce shippable code with minimal rewrites and clear traceability.

Workflow

• 1. Scan repo to detect stack, patterns, and conventions.
• 2. Present findings to developer for correction and confirmation.
• 3. Store corrected conventions on disk as a persistent rule set.
• 4. Await disk‑based approval gate before any implementation begins.
• 5. Generate a spec and plan, saving them as files.
• 6. Execute tasks in lifecycle phases: define, blueprint, construct, verify, inspect, release.
• 7. Log every clarifying question and answer in a structured file.
• 8. Run tests, capture output, and verify success before marking done.
• 9. Produce a changelog entry explaining why changes were made.
• 10. Store all artifacts on disk to survive session restarts.

Evidence

The gates were supposed to be a safety feature. They turned out to be a speed feature.

Source

Agents skip all of that by default. Not because they can't do it, but because nobody asked them to.

Source