👁️
Advisor Agent — Session Overseer
A second AI watching the first AI work
P1Core Pattern
Summary
An advisor agent that monitors and evaluates the primary Claude session. Automatically receives full conversation history, acting as a real-time overseer for the working session. Designed to improve qualitative output by providing independent assessment.
Architecture Diagram
Technical Details
This is architecturally significant: it's not just delegation (subagents doing work) but supervision (an agent watching another agent's quality).
Pattern: worker agent does the task, advisor agent evaluates the work, improvements feed back into the next iteration.
This dual-agent quality pattern — separate executor and evaluator — is a production-grade approach to AI reliability.
Implementation Pattern
TypeScript (conceptual)
// Advisor pattern: separate executor and evaluator
async function advisedExecution(task: Task) {
// Worker does the task
const result = await workerAgent.execute(task);
// Advisor evaluates (receives full conversation history)
const evaluation = await advisorAgent.evaluate({
task,
result,
conversationHistory: getFullHistory(),
});
if (evaluation.quality < THRESHOLD) {
// Feed improvements back for iteration
return advisedExecution(
applyFeedback(task, evaluation.suggestions)
);
}
return result;
}Architecture Insight
This is architecturally significant because it separates execution from evaluation — the same principle behind code review, QA testing, and peer review. The advisor doesn't do the work; it judges the work.
Official / Public Basis
Advisor agent pattern found in source architecture. Separate from subagent delegation — this is supervision, not task decomposition.
Governance Concerns
Dual-agent quality pattern adds cost but improves reliability. Must balance overhead vs. quality improvement. Not every task needs an advisor — reserve for high-stakes operations.
LightHope Ecosystem Mapping
LightHope — m8002 (quality control) as an advisor agent pattern, automated code review, content quality assurance, teaching quality monitoring