Correction is normal
People should be able to redirect an agent without fighting the previous answer. A correction should update the active task rather than be treated as an inconvenience.
LegionASI / Alignment
Alignment is how a system stays faithful to the work.
We use alignment in a practical engineering and governance sense: an AI system should understand the user’s goal, respect explicit constraints, remain open to correction, and avoid taking authority it was not given.
From intent to action
A helpful suggestion is not a new instruction.
Multi-agent systems create more opportunities for useful specialization and more opportunities for intent to drift. Alignment work follows the request as it moves through the conversation, an orchestrator, a specialist, a model, and—where enabled—a tool.
Follow the goal
Agents should identify the outcome the user is asking for and keep that outcome in view instead of optimizing for a locally attractive answer.
Respect constraints
Explicit limits about scope, format, timing, data, audience, or actions are part of the task. They should survive delegation and summarization.
Preserve context carefully
Conversation history can clarify intent, but old context should not silently override a current correction or become permission for an unrelated action.
Separate proposals from authority
An agent may suggest a next step, recruit another specialist, or identify a risk. Those suggestions do not authorize the system to take consequential action.
Operational alignment
Design for correction, disagreement, and stopping.
Conflicts are surfaced
When agents disagree about facts, scope, or the right next step, the disagreement is useful information. It should be made visible and resolved with evidence or human direction.
Humans remain in the loop
People retain responsibility for decisions with material consequences. The system should support review, not create a false impression that the machine has assumed responsibility.
Actions are auditable
Users and operators need a reasonable account of what was requested, what an agent proposed, what was executed, and where an approval was required.
No silent authority growth
Agents should not use a broad interpretation of helpfulness to gain access, contact new parties, change durable state, or control external systems without authorization.
Governance follows capability
As capability and autonomy increase, so should evaluation, monitoring, permissions, escalation paths, and the clarity of the human decision owner.
An engineering problem
Alignment is observable in behavior.
We look for practical signals: does a system preserve constraints across delegation, distinguish evidence from speculation, ask for missing information, accept correction, handle conflicting agents, and stop at a permission boundary? These behaviors can be evaluated even when deeper philosophical questions about intelligence remain unsettled.
LegionASI does not present alignment as a solved property or a permanent label. It is a continuing practice of system design, evaluation, human governance, and honest communication about what the software can and cannot do.