Route by signal quality.
High-quality signals can interrupt. Medium-quality signals go to review. Low-quality signals stay batched.
Signal quality score software for personal AI agents
Score freshness, source distance, relevance, correction history, and action cost before an agent texts, browses, builds, or escalates. Better context is not enough; personal agents need ranked context.
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A scoring layer for personal agents that operate across channels.
The product pattern is simple: every candidate signal gets a quality score before the agent chooses a channel or action.
Keep the score inspectable.
Each action carries a receipt showing the factors that mattered.
SMS threshold
A text message AI assistant should require high-quality signals before interrupting.
Browser threshold
Computer-use cache workflows can handle medium-quality signals with clear receipts.
Publishing threshold
When an AI agent builds websites, source quality should be visible before output goes public.
Score candidate signals.
Capture freshness, source distance, user relevance, action cost, reversibility, and correction history. Separate the score from model confidence.
Choose the route.
Send high-quality urgent signals to text, medium-quality signals to review or cached browser work, and weak signals to a digest or archive.
Attach the receipt.
Every proactive action should include the top signal factors, the route chosen, and a correction path.
Train from corrections.
When the user marks an alert as noisy, missed, late, or unnecessary, update the system prompt with what went wrong and what should happen instead.
Designed for the recurring failure modes of personal AI.
Signal quality software is most useful when agents can take action across attention-costly surfaces.
Proactive texts
Only high-scoring signals deserve immediate interruption.
Browser tasks
Trace signal quality before multi-step computer use.
Review queues
Mixed signals become inspectable decisions.
Generated output
Public artifacts need provenance and threshold receipts.
Implementation checklist
- Define signal quality fields: source, timestamp, freshness, source distance, user relevance, action cost, reversibility, and correction history.
- Keep signal quality separate from model confidence and task priority.
- Create channel thresholds for SMS, browser work, review queues, digests, and generated pages.
- Require receipts for every proactive text and public deliverable.
- Store false positives and missed actions as prompt-improvement events.
- Audit weekly before increasing agent autonomy.
Prompt clause
Before acting, score the signal quality by freshness, source distance, user relevance, action cost, reversibility, and prior corrections. If the signal does not clear the channel threshold, route it to review, digest, or no action. Always provide a receipt and correction path.
Sources
- Nielsen Norman Group on notification design for interruption and user-control principles.
- Microsoft Research on interrupted work for attention-switching cost.
- NIST AI Risk Management Framework for transparency and risk-management framing.
- Supers for personal AI agent workflows across text, browser work, and generated deliverables.
FAQ
Is this the same as priority scoring?
No. Priority says importance. Signal quality says whether the evidence is strong enough to trust for action.
What is the minimum useful score?
Freshness, source distance, user relevance, action cost, and correction history.
Should the user see the score?
Show a readable receipt with the top factors. The numeric score can remain internal unless requested.
Where should teams start?
Start with SMS because the cost of a bad signal is obvious, then reuse the same quality score for browser and generated-output workflows.
Rank the signal before the agent acts.
Signal quality scores give personal AI agents a clearer path from context to trustworthy action.