The trust loop: connected tools feed an evidence-backed record, a person decides, and the verified outcome improves future proposals without removing human control.
Five questions every signal must answer
What happened?
A concrete event or change, tied to the affected customer, invoice, deal or account.
What did Hive check?
The source and the field behind each reading — up to six readings per signal.
Why does it matter?
Plain-language reasoning, the stake, and any known limitation.
Who decides?
A person with the right role, in the review panel. Nothing runs until it is approved or within granted autonomy.
What happened next?
A receipt in the audit log and, where it can be checked, the outcome.
Evidence, not assertion
Every reading on a signal names its source connector and field, carries a typed value (money, a count, days, a date, a ratio), and says how it was compared: “past your threshold”, “against your usual”, “corroborated”, “correlated” or “cited”. Where Hive compares against a baseline, it shows the baseline. Confidence is labelled honestly. A figure marked “Hive’s own estimate” is the model’s self-assessment; a figure marked “measured” comes from observed results. Hive does not present one as the other. Links to source records are always secure HTTPS links without credentials. They open the record in the source tool, where that tool’s own access rules apply.Say what could not be checked
If a source is unavailable, a permission is missing, or a piece of evidence expired, Hive says so. For example, if an invoice is overdue but support history cannot be read, the safe proposal is to review the account before contacting the customer — not to pretend there is no dispute. When Hive cannot see the conversation behind an email proposal, the review panel says Conversation context unavailable — review the source before sending. The same rule applies to silence. Zero alerts never automatically means all clear. The Coverage tab lists what Hive is watching, so you can tell “nothing happened” from “nothing was checked”.One situation, one record
Repeated detections of the same situation update one signal rather than creating a stream of disposable alerts: the row shows how many times it has been seen and when it was first raised. Related signals that fire together are grouped. In the Signal Cases pilot, this becomes a durable case with owner, comments, decisions and outcome kept together.Learning without losing control
Hive improves from your team’s feedback in ways you can see:- Dismissal reasons change future detection — muting a record, counting towards alert fatigue for a type, or flagging a data source. See Triage signals.
- Approvals and denials feed earned autonomy: after repeated approvals of the same kind of action, Hive can offer to automate it. Once someone accepts, it runs automatically within limits, and the kill switch halts it.
- Execution memory records rules, workflows and exceptions your team has taught Hive; new ones start in shadow. See Learned behaviours.
Autonomy is earned one action type at a time, surrounded by role gates, approval limits, audit receipts, the kill switch and rollback where possible.
Proof, not a rented model
Models can be swapped. A model saying it is confident is not proof that a business decision was right. What makes Hive’s signals more useful over time is the record built around the model: connected business facts and their sources, evidence about what Hive could and could not observe, your team’s decisions and corrections, the rules it has learned about how you work, and outcomes that show whether an action worked. That record belongs to your workspace.What is available today
Related
Signals overview
How Hive finds and ranks signals.
Autonomy and blast radius
How Hive earns the right to act on its own.
Safety and control
The kill switch, spend caps and policy.
Audit log
Every action’s receipt, and undo.