Agent Trust Glossary

A dynamic smoothing factor in exponential moving average (EMA) scoring that adjusts based on transaction volume and recency.

Carefully crafted inputs designed to manipulate, confuse, or exploit vulnerabilities in agent behavior and decision-making.

The assignment of responsibility for agent actions to identifiable parties, enabling consequences for failures or harmful behavior.

An agent audit trail is a chronological, tamper-evident record of every action, decision, and configuration change an AI agent has made throughout its operational lifetime.

The degree of independent decision-making authority granted to an agent, ranging from fully supervised to fully autonomous operation.

A specific function, skill, or domain of competence that an agent possesses and can perform.

The sequential linking of multiple agents where the output of one becomes the input to the next, creating multi-step workflows.

Agent compliance refers to the degree to which an autonomous AI agent adheres to applicable regulatory frameworks, industry standards, and organizational policies during its operation.

Agent configuration is the complete set of components that define an AI agent's identity and behavior, including its model, prompt template, tools, memory stack, and RAG sources.

The maximum amount of information (typically measured in tokens) an agent can process in a single operation, defining its working memory limits.

Agent credit washing is the practice of deliberately re-registering or reconfiguring an AI agent to shed a poor trust score and start fresh with a clean record.

The controlled process of safely retiring an agent from active service, including data archival, notification to consumers, and cleanup of integrations.

The process by which an agent assigns subtasks or responsibilities to other agents, creating hierarchical task distribution.

A series of delegated tasks passing through multiple agents, creating a hierarchical or sequential responsibility structure.

The process of finding agents that match specific functional requirements, quality standards, or capability criteria from available options.

Agent escrow is a trust mechanism where funds or resources in an agent-to-agent transaction are held by a neutral third party until both agents have fulfilled their contractual obligations.

Predefined backup behavior or alternative processing path activated when an agent's primary function fails or encounters unexpected conditions.

A collection of agents managed, deployed, and operated by a single entity, typically sharing infrastructure and governance policies.

The policies, processes, and controls that guide agent development, deployment, operation, and oversight to ensure responsible behavior.

The transfer of an ongoing task and its associated context from one agent to another, preserving state and continuity.

An automated probe that verifies an agent is responsive, functioning correctly, and meeting performance standards.

A periodic signal transmitted by an agent to confirm operational status and connectivity, enabling detection of failures or disconnections.

Agent identity is the persistent, verifiable representation of an AI agent that distinguishes it from all other agents and anchors its trust history across interactions and platforms.

Agent identity persistence is the ability of an AI agent's identity and associated trust history to remain continuous and intact across configuration changes, platform migrations, and operational interruptions.

The fraudulent act of one agent claiming the identity of another trusted agent to gain unwarranted trust or access.

Agent insurance is a financial product that provides coverage against losses caused by AI agent failures, errors, or unexpected behaviors during autonomous operation.

The ability of agents to interact, exchange data, and coordinate across different platforms, frameworks, and organizational boundaries.

The complete sequence of stages an agent passes through from initial development to eventual retirement, including registration, operation, updates, and decommissioning.

An agent marketplace is a platform where AI agents are listed, discovered, and engaged for tasks, with trust scores and verified credentials serving as the primary basis for selection.

The structural design of how an agent stores, retrieves, and utilizes information across short-term context, long-term knowledge, and persistent state.

A system that maps human-readable agent names to unique identifiers (SIDs), enabling discovery and addressing similar to DNS for domains.

A scoped naming domain that groups related agents and prevents naming conflicts, similar to namespaces in programming or DNS zones.

The degree to which internal agent operations, decision processes, and state can be monitored, measured, and understood by external parties.

The coordination of multiple agents to accomplish complex workflows, managing sequencing, data flow, error handling, and resource allocation.

Agent registration is the process of formally enrolling an AI agent in a trust scoring system by providing its identity, configuration details, and operator information to establish a scoreable record.

Agent reliability is the measurable consistency with which an AI agent completes assigned tasks correctly, on time, and within specified parameters across a sustained period of operation.

Agent reputation is the aggregate perception of an AI agent's trustworthiness, competence, and reliability based on its historical behavior and the evaluations of those who have interacted with it.

The strategy for automatically re-attempting failed operations, including conditions, delays, and limits to balance reliability with resource consumption.

An isolated environment for testing and running agents with restricted access to production systems, data, and external services.

Agent scoring methodology is the systematic framework of criteria, weights, data sources, and algorithms used to calculate a quantitative trust score for an AI agent.

A maximum time limit for agent operations, after which execution is terminated to prevent indefinite resource consumption or user experience degradation.

An agent-to-agent transaction is an autonomous exchange of value, services, or information between two AI agents without direct human involvement in the transaction execution.

The visibility provided into how agents make decisions, what data they use, and why they produce specific outputs.

Agent trust is the measurable confidence that a counterparty can place in an AI agent's ability to perform tasks reliably, securely, and within expected parameters.

Agent verification is the process of confirming that an AI agent is what it claims to be, including validating its identity, operator, configuration, and capabilities through independent checks.

Economic activity conducted between autonomous AI agents acting on behalf of humans or organizations, including negotiations, purchases, and service delivery.

The obligation to explain and justify algorithmic decisions, particularly those affecting individuals, with mechanisms for recourse and remediation.

A centralized entry point that routes, authenticates, rate-limits, and monitors API requests from agents to backend services.

An AI system capable of perceiving its environment, making decisions, and taking actions independently to achieve specified goals with minimal human intervention.

A trust assessment approach using Bayesian probability to update trust beliefs based on observed evidence, balancing prior assumptions with new observations.

A behavioral fingerprint is a statistical signature derived from an AI agent's patterns of behavior, decision-making tendencies, and output characteristics that uniquely identifies the agent beyond its configuration.

Systematic evaluation of agent decision-making to identify unfair treatment or discriminatory outcomes based on protected characteristics or unintended factors.

The degree to which an agent stays within allocated spending limits, resource quotas, or cost constraints during operation.

Restricting agent actions to explicitly defined boundaries, preventing access to functions, data, or resources outside authorized scope.

A reasoning approach where agents explicitly articulate step-by-step thinking processes before reaching conclusions, improving accuracy and explainability.

The percentage of transactions reversed due to disputes, fraud claims, or customer dissatisfaction, indicating reliability and customer satisfaction.

An error-handling mechanism that automatically disables failing service connections after threshold violations, preventing cascading failures and resource waste.

Automated handling of insurance or warranty claims by agents, including validation, assessment, approval decisions, and payment initiation.

AI-assisted tools that help healthcare providers make medical decisions by analyzing patient data, suggesting diagnoses, or recommending treatments.

A code agent rating is a specialized trust assessment for AI agents that write, review, or modify software code, evaluating their reliability, security awareness, and code quality across programming tasks.

The cold start problem in agent trust is the challenge of assessing the trustworthiness of a newly registered AI agent that has no transaction history or behavioral data to evaluate.

Ongoing surveillance and verification that agent operations adhere to regulatory requirements, internal policies, and contractual obligations.

Component-aware scoring is a trust assessment approach that evaluates each individual component of an AI agent's configuration separately, then combines these assessments into a composite score that reflects the full system.

Agents designed with modular interfaces and standardized communication protocols enabling them to be combined and reconfigured for diverse workflows.

A composite score is a single numerical trust rating derived from the weighted combination of multiple dimension scores, designed to provide a quick, overall assessment of an AI agent's trustworthiness.

A statistical range indicating the likely bounds of an agent's true score, with wider intervals reflecting greater uncertainty due to limited data.

A categorical classification (Low/Medium/High) indicating how reliable an agent's score is based on transaction count and performance variance.

A configuration fingerprint is a SHA-256 cryptographic hash computed from an AI agent's complete configuration -- its model, prompt template, tools, memory stack, and RAG sources -- that uniquely identifies a specific agent build.

The process and systems for obtaining, recording, honoring, and revoking user consent for data collection, processing, and sharing by agents.

AI-driven filtering and review of user-generated content to identify and remove harmful, illegal, or policy-violating material.

The maximum input size (typically measured in tokens) that a language model can process in a single operation, determining how much information it can consider.

Ongoing, real-time observation and analysis of agent behavior, performance, and compliance rather than periodic spot checks.

AI-powered review and interpretation of legal agreements to extract key terms, identify risks, ensure compliance, and suggest modifications.

The practice of minimizing agent operational expenses while maintaining acceptable performance, through efficient resource use and architectural choices.

The unintended exposure of authentication secrets (API keys, passwords, tokens) through agent outputs, logs, or error messages.

The process of confirming the authenticity and validity of agent credentials, certifications, or claimed qualifications.

Cross-platform reputation is an agent's trust standing that is recognized and portable across multiple platforms, marketplaces, and ecosystems rather than being confined to a single service.

Using cryptographic signatures to prove agent identity, configuration, or output authenticity, enabling tamper-evident verification.

Unauthorized extraction or transfer of sensitive data by an AI agent, either through malicious design or compromised instructions.

A data flywheel in agent trust scoring is a self-reinforcing cycle where more agent transactions generate more trust data, which improves scoring accuracy, which attracts more agents and transactions.

The practice of collecting and processing only the minimum data necessary for an AI agent to complete its specific task or function.

A systematic evaluation of privacy risks and mitigation strategies required before deploying AI agents that process sensitive or personal data.

The complete historical record of data origins, transformations, and movement through an AI agent system, enabling traceability and verification.

Self-sovereign identity systems where agents control their own credentials and identity verification without relying on central authorities.

A layered security strategy applying multiple independent safeguards to protect AI agent systems against threats and failures.

The property of an AI agent producing identical outputs when given the same inputs and configuration, enabling predictability and testing.

The specific percentages Signet assigns to each trust dimension when calculating an agent's overall trust score out of 1000.

Formal processes for handling disagreements about AI agent transactions, performance claims, or trust score assessments.

AI-powered extraction, analysis, and understanding of information from structured and unstructured documents.

Domain certification is a verified credential indicating that an AI agent has been evaluated and meets minimum trust standards for operation within a specific industry or use case domain.

Monitoring systems that identify gradual changes in AI agent behavior, input patterns, or performance over time.

An AI agent specialized in conducting research, verification, and risk assessment for business transactions, investments, or partnerships.

Trust assessment systems where an agent's trustworthiness score changes based on current context, task type, or recent performance.

Running AI agents on local devices or edge infrastructure rather than centralized cloud servers, prioritizing latency, privacy, or offline operation.

A neural network that converts text, images, or other data into dense vector representations capturing semantic meaning for agent use.

An AI agent that combines multiple models or reasoning approaches to make decisions, aggregating diverse outputs for improved accuracy.

The acceptable failure rate or downtime allocated for an AI agent service, balancing reliability requirements against development velocity.

A system that holds payment or assets in a neutral account until an AI agent successfully completes a task or meets specified conditions.

The European Union's comprehensive regulatory framework governing AI system development, deployment, and use based on risk classification.

The ability of an AI agent to provide understandable reasoning for its decisions, outputs, and actions in terms humans can interpret.

A statistical method that weights recent data points more heavily than older ones, commonly used to smooth agent performance metrics over time.

Quantitative measures evaluating whether an AI agent's decisions are equitable across different demographic groups or protected characteristics.

A machine learning approach where model training occurs across distributed data sources without centralizing the data, preserving privacy.

A cycle where an AI agent's outputs influence future inputs or behavior, potentially amplifying biases or errors over time.

A financial agent rating is a specialized trust assessment for AI agents that handle financial transactions, investment decisions, payment processing, or other monetary operations.

The process of training a pre-trained foundation model on domain-specific data to specialize an AI agent for particular tasks or knowledge areas.

Aggregate trust metrics calculated across a portfolio of AI agents operated by a single organization or platform.

A large-scale pre-trained model serving as the base for AI agents, providing broad capabilities before specialization or fine-tuning.

AI agents specialized in identifying fraudulent activities, transactions, or behaviors through pattern recognition and anomaly detection.

The ability of an AI agent to invoke external tools, APIs, or functions to perform actions beyond text generation.

A structured set of policies, processes, and controls for managing AI agent development, deployment, and operation within an organization.

An agent system design approach that maintains partial functionality when components fail rather than complete system failure.

Verified, factually correct data used as the reference standard for evaluating AI agent accuracy and performance.

Safety constraints and filters that prevent AI agents from generating harmful, inappropriate, or policy-violating outputs.

When an AI agent generates plausible-sounding but factually incorrect or fabricated information presented as truth.

System design ensuring AI agents remain operational and accessible with minimal downtime, typically 99.9% uptime or higher.

Increasing AI agent system capacity by deploying additional parallel instances rather than upgrading individual components.

A design pattern where human oversight and approval are required at critical decision points in AI agent operations.

Linking AI agent identities across multiple systems and platforms, enabling single sign-on and portable reputation.

A network structure mapping relationships between AI agents, operators, platforms, and resources to understand identity connections.

The process of verifying that an AI agent is genuinely controlled by its claimed operator and matches its represented capabilities.

The process of matching and merging AI agent identity records across different systems to create a unified view.

A tamper-proof record of AI agent actions, decisions, and data access that cannot be altered or deleted after creation.

The structured process for detecting, investigating, and remediating AI agent security breaches, failures, or policy violations.

An attack where malicious instructions are hidden in retrieved content like documents or web pages, manipulating agent behavior.

The time required for an AI agent to process inputs and generate a response, measured from request to completion.

Attempts to bypass AI agent safety constraints and guardrails through carefully crafted prompts that exploit model behavior.

Secure generation, storage, distribution, and rotation of cryptographic keys used by AI agents for authentication and encryption.

An emergency mechanism to immediately halt AI agent operations in response to detected malfunctions, security breaches, or harmful behavior.

Know Your Agent (KYA) is a due diligence framework for verifying the identity, configuration, operator, and trust standing of an AI agent before engaging it in transactions or granting it access to resources.

The maximum acceptable time allocation for an AI agent to complete a request, distributed across processing stages and dependencies.

An AI agent designed with the minimum permissions and access rights necessary to perform its specific function, reducing security risk.

An AI agent specialized in legal research, document analysis, contract review, or regulatory compliance tasks.

A system that directs incoming requests to the most appropriate language model based on query characteristics, optimizing for cost and performance.

Distributing incoming requests across multiple AI agent instances to optimize resource utilization, minimize latency, and prevent overload.

Infrastructure for collecting, processing, storing, and analyzing logs from AI agent operations for monitoring and compliance.

An AI agent that provides liquidity in financial or prediction markets by continuously quoting buy and sell prices.

A memory stack is the complete set of persistent information storage systems that an AI agent uses to retain context, learned information, and interaction history across sessions.

A model baseline is the expected performance profile of a foundation model across trust dimensions, established through systematic evaluation and used as a reference point for scoring agents built on that model.

A model swap is the act of replacing an AI agent's underlying foundation model with a different model, which is the most significant configuration change an agent can undergo.

A multi-agent system is an architecture where multiple AI agents collaborate, coordinate, or compete to accomplish tasks that are beyond the capability or scope of any single agent.

When multiple AI agents or systems use identical names or identifiers in a shared registry, creating conflicts.

A network effect in agent trust occurs when each additional agent, platform, or transaction in the trust ecosystem increases the value and accuracy of trust assessments for all participants.

The ability to understand an AI agent's internal state and behavior through comprehensive monitoring of its outputs, logs, and metrics.

An operator score is a trust rating assigned to the organization or individual that builds, deploys, and maintains one or more AI agents, reflecting their overall track record as an agent operator.

Infrastructure that coordinates and manages the execution of multiple AI agents, handling task distribution, sequencing, and inter-agent communication.

The process of verifying that an AI agent's outputs meet defined quality standards, format requirements, and correctness criteria before use.

Running multiple AI agent tasks simultaneously rather than sequentially to improve throughput and reduce total processing time.

The process of approving an AI agent to execute financial transactions within defined limits and conditions.

A dedicated pathway enabling direct financial transactions between AI agents or between agents and service providers.

A mechanism that holds funds in a neutral account until an AI agent completes specified tasks or meets defined success criteria.

The process of undoing a completed transaction after an AI agent delivers unsatisfactory results or fails to meet agreed-upon criteria.

When an AI agent gains access to system capabilities or data beyond its authorized permissions, either through exploitation or misconfiguration.

A structured analysis conducted after an AI agent failure or incident to identify root causes, impacts, and preventive measures.

Predictive agent analytics is the use of historical trust data, behavioral patterns, and configuration information to forecast an AI agent's future performance, risk level, and scoring trajectory.

An approach to AI agent development that embeds privacy protections and data minimization principles into the system architecture from inception.

Prompt engineering is the practice of designing and refining an AI agent's system prompt and instruction templates to shape its behavior, capabilities, and guardrails.

A security attack where malicious input manipulates an AI agent's system prompt or instructions to produce unauthorized outputs or behaviors.

A temporary trust score assigned to newly registered AI agents before sufficient performance data accumulates for a fully validated assessment.

A specialized AI agent whose primary function is to evaluate, validate, and verify the outputs produced by other AI agents.

The process of directing incoming requests to the most appropriate AI agent based on query content, agent capabilities, and performance characteristics.

A RAG source is an external data repository that an AI agent retrieves information from at inference time using Retrieval-Augmented Generation, directly influencing the accuracy and currency of the agent's responses.

Controlling the frequency and volume of requests an AI agent can make to external services or the requests it will accept from users.

Calculating and updating AI agent trust scores immediately after each interaction rather than in periodic batch processes.

Adversarial testing of AI agent systems where security professionals attempt to exploit vulnerabilities, bypass safeguards, or trigger unintended behaviors.

Deploying backup AI agent instances or alternative systems to ensure continued availability if primary agents fail or become unavailable.

A controlled environment where AI agents can be tested under relaxed regulatory requirements to evaluate compliance before full deployment.

The ability to transfer an AI agent's established trust history and performance record between different platforms or ecosystems.

A research agent rating is a specialized trust assessment for AI agents that perform information gathering, analysis, synthesis, and reporting tasks, evaluating their accuracy, thoroughness, and citation quality.

An AI technique that combines information retrieval from knowledge bases with language model generation to produce more accurate, grounded outputs.

Rules governing when and how an AI agent automatically reattempts failed operations, including retry timing, maximum attempts, and backoff strategies.

The user right to receive clear, understandable explanations of how an AI agent made decisions affecting them, particularly for consequential determinations.

Reverting an AI agent to a previous configuration, model version, or system state after detecting problems with a recent change.

Ensuring an AI agent's objectives, behaviors, and outputs align with human values, safety principles, and societal norms.

When an AI agent breaks out of its designated containment environment to access unauthorized system resources, data, or capabilities.

Verifying that AI agent inputs and outputs conform to defined data structures, types, and format requirements.

A statistical range indicating the uncertainty around an AI agent's trust score, reflecting the precision of the score estimate.

Score decay is the systematic reduction of an AI agent's trust score following a configuration change, reflecting the uncertainty introduced by altering the agent's model, prompt, tools, memory, or data sources.

Predicting an AI agent's future trust score trajectory based on current performance trends and historical patterns.

Techniques that reduce short-term fluctuations in trust scores to prevent overreaction to individual interactions while preserving meaningful trend information.

The degree to which an AI agent's trust score fluctuates over time, indicating consistency or instability in performance.

Information retrieval based on understanding query meaning and conceptual relevance rather than just matching keywords.

A contractual commitment defining the expected performance, availability, and quality standards an AI agent will maintain.

The point at which an AI agent transaction becomes irreversible and both parties can consider the exchange complete and binding.

Running a new or modified AI agent in production environment alongside the current version, processing real traffic without affecting user experience.

The Ship of Theseus problem in AI refers to the philosophical and practical challenge of determining whether an AI agent remains the "same" agent after its components have been gradually or completely replaced over time.

A Signet ID (SID) is a unique, persistent identifier assigned to every AI agent registered on the Signet platform, formatted as SID-0x followed by 16 hexadecimal characters.

The Signet Score is a composite trust rating from 0 to 1000 that quantifies an AI agent's overall trustworthiness based on weighted assessments across five dimensions: Reliability, Quality, Financial, Security, and Stability.

Signet Sealed is a premium verification status indicating that an AI agent's configuration has been independently audited, locked, and continuously monitored to ensure it matches its registered specification.

Siloed reputation refers to the problem where an AI agent's trust history and performance data are trapped within individual platforms, invisible and non-transferable to other platforms where the agent also operates.

A maximum amount an AI agent is authorized to spend within a defined period, preventing runaway costs or unauthorized expenditures.

A trust score that no longer accurately reflects an AI agent's current capabilities because it is based on outdated performance data.

Creating multiple fake AI agent identities to manipulate trust scores, reputation systems, or platform economics through coordinated deception.

The foundational instructions that define an AI agent's role, behavioral guidelines, capabilities, and operational constraints.

Directing work assignments to appropriate AI agents based on task requirements, agent capabilities, availability, and performance characteristics.

A thin-file agent is an AI agent with insufficient transaction history to generate a high-confidence trust score, analogous to a consumer with a thin credit file in traditional lending.

The volume of tasks or requests an AI agent can successfully process per unit of time, measuring operational capacity.

The duration required for a newly deployed AI agent to accumulate sufficient performance history for a reliable, fully validated trust score.

The maximum number of tokens allocated for a specific AI agent operation, controlling both cost and computational resource usage.

Tool integration refers to the external APIs, services, and software tools that an AI agent is configured to invoke during task execution, which extend the agent's capabilities beyond its foundation model.

The ability to track AI agent decisions, data flows, and behaviors back to their origins, enabling accountability and debugging.

An AI agent specialized in executing financial market trades, managing portfolios, or providing investment analysis and recommendations.

A cost charged per financial transaction executed by or involving an AI agent, covering processing, infrastructure, and network expenses.

Regulatory or organizational mandates requiring AI agents to disclose information about their operation, decision-making, or limitations.

Combining trust signals from multiple sources, dimensions, and timeframes into a unified trust assessment for an AI agent.

A trust dimension is one of the distinct, independently-scored aspects of an AI agent's trustworthiness that combine to form the composite trust score.

A Trust Report is a comprehensive, structured document that presents an AI agent's complete trust profile, including scores, dimension breakdowns, configuration history, behavioral patterns, and trend analysis.

A trust threshold is the minimum Signet Score or dimension score that an AI agent must meet to be eligible for a specific transaction type, platform access level, or operational privilege.

A universal agent identifier is a standardized, globally-unique identifier that works across all platforms and systems to consistently and unambiguously identify a specific AI agent.

A Verified Badge is a visual indicator displayed on an AI agent's profile confirming that the agent has met Signet's minimum verification requirements for identity, configuration, and operational history.