Glossary

The glossary explains the words that Auga uses, and the words that it avoids.

It is for internal use while the words settle. Choose a group to filter the list.

Words

76 terms in 5 groups

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TermPlain meaningMore exactlyDo not useSeen on
Agent registerPlacesThe list of every agent, who holds each role for it now, and its history.The menu calls it Agents. Only register sources create agents. Uploaded files and bundles never do.inventory
Agent HealthPlacesA report on one time window of runs in one workflow: what changed, how sure we are, and what it may cost.The report grades seven dimensions. The total status is the weakest dimension, read at the low end of its likely range. It covers the whole workflow, so each agent's profile shows its own part under Health.Agents Health, Health, when the whole report is meant
Agent profilePlacesThe page for one agent: how it works, who holds each role, how it is doing, where Auga sees it, and its history.Its portrait shows real runs of the agent's workflow in 3D. Its Health section shows the agent's part of the latest Agent Health report, and leads to the report.–
Bundle AnalysisPlacesA place to practise with Auga on a simulated dataset where the root cause is hidden.It appears only when the uploaded file is a bundle. The answer stays sealed until you open it.Proving Ground
CoachPlacesThe place where a person reviews the decisions of agents.The wide menu says Coach your agents. The page title is Decision review. Coach is a working name.Review
CompliancePlacesThe place that shows the evidence for each article of a law.The first law is the EU AI Act. The page title keeps the name EU AI Act, because several laws will follow.AI Act, as a menu name
ConnectionsPlacesHow Auga is connected, how you can use it, and which systems receive evidence from Auga.––
Data boundaryPlacesWhat left your network, and what stayed inside it.The menu says Boundary.–
EvidencePlacesAll runs, the findings that explain them, and the cause chains.––
GlossaryPlacesThis page. It explains the words that Auga uses and the words it avoids.The source is glossary.json. It is for internal use while the words settle.–
ImprovePlacesThe changes to your agents, whether each change helped, and what Auga proposes to change next.––
IncidentsPlacesWhat has gone wrong with your agents, how sure Auga is, and who handles each incident.––
Intelligence ExchangePlacesSharing what your agents learned with other companies, without sharing the data.The menu says Exchange. Only graded findings are shared, never rows of data.–
LedgerPlacesThe signed record of decisions, reviews and actions. Each entry is linked to the one before, so any change can be seen.Hash-chained and signed with the guard's key. It is not a register of agents.journal
MandatesPlacesWhat each agent may do in each field of work, and what it needs for the next stage.––
Preview a stagePlacesA view of Mandates that shows what a stage would have done to real traffic, before an agent gets that stage. It sets nothing.Earlier called Mandate Simulator, which is also the name of the separate prototype it comes from.Simulator
ReportsPlacesCopies of the evidence that you can download, and the review of findings.The wide menu says Claim review & reports.–
RisksPlacesWhat could go wrong with an agent again, what it would cost, and what your company decided.––
What-IfPlacesA calculator for the value and the losses of all your agents, with a report for the board.––
ObservedStages and gradesThe agent only watches. Its proposals are logged, and nothing runs.The first of five stages.–
SupervisedStages and gradesThe agent acts, and a person reviews every action.––
ConstrainedStages and gradesThe agent acts within limits. Actions that cannot be undone go to a person.––
TrustedStages and gradesThe agent acts within limits, also for actions that cannot be undone.––
MandatedStages and gradesThe agent acts on its own, because an experiment showed that it works.The dashboard says proven by experiment. The word proven is listed for the team to decide.–
Field of workStages and gradesOne area where an agent can act. Each agent has a stage in each field of work.Tool use, Data handling, External communication, Payments, Escalation and Output accuracy.field, when it is unclear
GradeStages and gradesHow strong the evidence for a finding is, from F to A+.F: seen by one method. D: two methods agree. C: effect measured. B: survived the challenge tests. A: proven by experiment. A+: predicted and held.grade, for a status
StatusStages and gradesHEALTHY, WATCH or UNHEALTHY for a part of Agent Health. A status is not a grade.––
UpgradedStages and gradesThe stage of an agent went up in a field of work.An agent is upgraded one stage at a time, when every criterion of the next stage is met. The word comes from credit ratings. Sources: Fitch Ratings: Rating Definitions, Moody's Rating Terms.raised, moved up, promotion
DowngradedStages and gradesThe stage of an agent went down in a field of work.An agent is downgraded at once when a criterion is not met. The word comes from credit ratings. Sources: Fitch Ratings: Rating Definitions, Levels of Autonomy for AI Agents.lowered, moved down, demotion
UnchangedStages and gradesThe stage stayed the same in the period.Not affirmed: affirmed would say that a review took place.–
Held lowerStages and gradesA person keeps the stage below what the evidence allows.People may hold a stage down, but never raise it above the evidence.–
Moved, for the values of a signalStatisticsHow much the values moved compared with the first period.PSI and KS for numbers, Jensen–Shannon for categories.–
The moment the pattern changedStatisticsThe time when the data started to behave differently.Change point.–
How strong the sign isStatisticsWeak, clear or strong.Gain in nats at the change point.–
Likely rangeStatisticsThe range where the true value probably is.A 95% interval, often a Wilson interval.95% sure
Far from the normal valuesStatisticsA value that is very different from the usual values.Robust z above 10.–
How much the outcome depends on a signalStatisticsHow much knowing the signal tells you about the outcome. It does not show a cause.Mutual information.causes
Clearly differentStatisticsThe likely ranges of two groups do not overlap.Separated.–
We cannot tell the groups apartStatisticsThe likely ranges of two groups overlap.Not separated.no difference
Runs that stayed on the old versionStatisticsRuns in the same hours that did not get the change. They show what would have happened without it.Concurrent control.–
Some periods laterStatisticsA link where one thing follows another after a number of periods.Lag n.lag
95% were fasterStatistics95% of runs or steps were faster than this time.p95. Half were faster than p50.–
1 year in 10 is worseStatisticsIn the simulation, 1 year in 10 is worse than this figure.P10.worst case
Fair shareStatisticsEach agent's share of a total, when all the agents worked together.Shapley value.cause
PeriodStatisticsA slice of time that the data is divided into.Bucket.–
Percentage pointsStatisticsThe difference between two percentages, for example from 16% to 100% is 84 points.pp.–
Difference: none, small, clear, largeStatisticsHow big a difference is, in four steps.Use these words only with their own thresholds, never for other measures.effect size, d
Accountable personGovernance and lawThe person who answers for what an agent does, and who decides about its risks and incidents.–responsible, when accountable is meant
Technical ownerGovernance and lawThe person who keeps an agent running and makes the changes.––
Human oversightGovernance and lawThe people who review the decisions that the policy sends to a person.EU AI Act, Article 14.–
DeployerGovernance and lawThe company that uses an AI system. The EU AI Act sets duties for it.Compliance is the deployer's conclusion. Auga shows evidence, not a verdict.–
Model providerGovernance and lawThe company that makes the AI model that an agent uses.Not the same as provider in the EU AI Act, which means the maker of the whole AI system.–
High-risk AI systemGovernance and lawAn AI system where mistakes can seriously affect people, as defined in the EU AI Act.Annex III lists the areas. Point 5(b) covers the creditworthiness of people. Your company classifies the system.–
Serious incidentGovernance and lawAn incident with serious harm, as defined in the EU AI Act.Whether an incident is serious is your company's decision. Auga records who decided, and when.–
Incident levelsGovernance and lawSomething changed, Harm suspected, Harm confirmed.Something changed: the data moved. Harm suspected: a graded finding links the change to a harm. Harm confirmed: a person or the real outcome confirms it.–
Risk acceptanceGovernance and lawA signed decision by a person to accept a risk for an agent and a field of work, until a date.An accepted risk never raises the stage of an agent.–
AttestationGovernance and lawA signed statement of the stage that an agent has, which other systems can check.–in force
Evidence packageGovernance and lawThe signed evidence about an incident or a risk, which a receiver can check without Auga.––
GRC systemGovernance and lawGRC is short for governance, risk and compliance. A GRC system is the software a company uses to manage these.Examples are ServiceNow, Archer, AuditBoard, LogicGate Risk Cloud and OneTrust. Auga has not been tried with any of them.–
Register sourceGovernance and lawAn OpenTelemetry Collector that your organisation uses in production or staging. Only these create agents in the register.––
Analysis dataGovernance and lawA file or bundle that someone uploads. It never creates an agent in the register.––
Declared, seenGovernance and lawDeclared means that a person registered the agent. Seen means that Auga sees it in a register source.Seen, not declared can be an agent that nobody governs.–
CompliantGovernance and lawAuga never uses this word. Compliance is the deployer's conclusion.–compliant
RunEveryday wordsOne execution of an agent's work, for example one loan application.The tab Traces & Spans and technical attribute names keep the word trace.trace, case, episode
StepEveryday wordsOne part of a run, for example one tool call.Span, in technical views.–
Failed runEveryday wordsA run with the outcome that you count as bad, for example a denial.A denial counts as a failed run, but a denial can be correct.bad, bad outcome, bad-rate
FindingEveryday wordsSomething that Auga found in the data, with a grade for how strong the evidence is.–claim, edge, hypothesis
SignalEveryday wordsA value that Auga records for each run, such as income.–variable, column, attribute
Cause chainEveryday wordsThe steps from a cause to an outcome, each with its finding.–chain
ChangeEveryday wordsA difference between two versions, for example of a model or a feed.–switch
RolloutEveryday wordsA controlled release of a change to more and more runs.––
LossEveryday wordsMoney lost because of something that went wrong.–damage, lost
VerdictEveryday wordsA person's decision on one agent decision: correct, wrong or harmful.–verdict, for a report or a change
ResultEveryday wordsWhat a report, a change or a check came to.––
StaleEveryday wordsOut of date. A stale score was not updated when it should have been.Listed for the team to decide, because some readers may not know the word.–

The source of this page is glossary.json. Change a word there, then run node scripts/glossary.mjs.