GanyuanRan / Autoloom

Autoloom 
 Governance Engineering for Coding Agents 
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**AI coding with engineering judgment built into execution.**
Autoloom brings Aegis’s core governance methods into the coding runtime: question whether a change is needed, challenge assumptions before editing, and review delivery against requirements and execution evidence.
Spend less time reminding your agent to investigate first, avoid unnecessary complexity, and show what it actually verified.
## Reasoning at the points where decisions matter
| Method | Its role in the development workflow |
| --- | --- |
| **First principles and change necessity** | At task initiation, examine goals, facts, existing capabilities, and constraints to establish why a change is needed and whether an adequate solution already exists. |
| **Anti-entropy and structural complexity checks** | Before changes, examine duplicated responsibilities, unnecessary layers, and maintenance burden so added complexity addresses a concrete problem. |
| **Counterexample review and causal diagnosis** | Before changes, examine conditions that could invalidate a solution; after execution failures, require diagnosis grounded in observations and evidence. |
| **Implementation conformance and completion review** | Before recording results, compare task requirements, actual execution, and verification material, stating what is complete and what remains unverified. |
Autoloom delivers these method requirements at the relevant points, checks prerequisite ordering for key operations, and retains process records. Whether a method was applied well and a result is reliable still needs to be judged from code, execution results, and verification evidence.
Before an admitted project mutation runs, the conversation can show a collapsed Governance impact row describing how the relevant methods changed that concrete action. Its user-facing fields follow the request language, and the complete recorded details remain inspectable.
Recorded project goals, constraints, and key decisions stay with the project. Changes to those decisions present a specific diff for your review. You can also configure a test-first preference and code complexity thresholds to reflect your project's requirements.
## From engineering methods to execution
Aegis (GitHub) is our open-source AI engineering method pack. Autoloom integrates its core governance methods into development, covering change necessity, complexity, causal diagnosis, and delivery review.
Guidance loads as needed. The runtime maintains prerequisites for key actions, task state, and execution evidence—connecting governance to actual work and reducing repeated reminders and manual checks. Reliable delivery still depends on actual verification results.
## Built on DeepSeek Harness, with our own approach to development
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