Glossary

Glossary of Terms

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

R

S

T

U

V

W

X

Y

Z

View All

Adaptation

Agents learning and improving their reasoning patterns based on real-world outcomes and feedback. Unlike static AI systems, Metafore's agents continuously adapt to changes in business conditions, data patterns, and performance metrics.

Auditability

The ability to examine, review, and document every decision made by an autonomous agent. In Metafore, auditability is built into the platform—every agent decision includes a complete decision trace showing the reasoning, rules applied, and data considered.

Autonomous Agent

An intelligent software system that can sense conditions, reason about them, and act independently within defined boundaries. Metafore agents are domain-specialized and coordinate through orchestration.

Bias Detection

The systematic monitoring and identification of unfair patterns in autonomous agent decision-making, particularly across demographic groups or customer segments. In Metafore, bias detection happens through continuous monitoring of agent decisions.

Black Box AI

Artificial intelligence systems where the internal decision-making process is not transparent and cannot be explained. Black box AI systems make decisions but provide no visibility into the reasoning.

Cognitive Agent

An autonomous agent designed to sense, reason, and act based on understanding context and domain knowledge. Metafore's cognitive agents combine reasoning capabilities with continuous learning.

Cognitive System

An enterprise system where autonomous agents collaborate to sense, reason, and act across business processes. Unlike traditional enterprise software, cognitive systems adapt and improve based on real-world interactions.

Compliance

In the context of Metafore, the ability of autonomous agents to operate within regulatory requirements, governance rules, and organizational policies. Built-in compliance means regulatory requirements are part of agent reasoning.

Context

The complete set of relevant information available to an autonomous agent at the time of decision-making. Context includes historical data, current conditions, business rules, and customer information.

Decision Trace

A complete record of how an autonomous agent arrived at a specific decision, including the rules applied, data considered, confidence levels, and alternatives rejected.

Domain-Specialized Agent

An autonomous agent trained and optimized for a specific business domain (lending, customer service, risk management). These agents develop deep reasoning capabilities within their domain.

Enterprise Fabric

The unified knowledge foundation across an enterprise that enables autonomous agents to reason with consistent context. The enterprise fabric connects customer data, transaction history, compliance status, and risk assessments.

Explainability

The ability to explain why an autonomous agent made a specific decision in human-understandable terms. Metafore agents are explainable by design.

Fair Lending

The practice of ensuring lending decisions are made fairly without discrimination based on protected characteristics like race, gender, or age. In Metafore, fair lending is monitored continuously.

Feedback Loop

The mechanism by which autonomous agents learn and improve over time. Agent decisions are logged, outcomes are measured, patterns are identified, and improvements are proposed and deployed.

Fragmented Systems

Enterprise systems that operate in silos without coordinated communication or shared knowledge. Legacy enterprises typically have fragmented core banking, CRM, compliance, and risk systems.

Governance

The systems and processes that maintain control over autonomous agent behavior, decision-making, and evolution. Governance ensures agents remain compliant and aligned with business objectives.

Handoff

The structured transfer of a task, decision, or piece of work from one autonomous agent to another within an orchestrated workflow. Handoffs ensure proper information flow.

Intelligent Automation

The use of autonomous agents to automate business processes while maintaining reasoning, adaptability, and governance. Unlike traditional RPA, intelligent automation adapts to changing conditions.

Intelligent Orchestration

The coordination of multiple autonomous agents to work together on complex business processes. Intelligent orchestration defines sequencing, dependencies, handoffs, and governance.

Integration

In the context of Metafore, connecting autonomous agents to legacy systems without requiring system replacement. The orchestration layer integrates across fragmented systems.

Knowledge Fabric

Alternative term for enterprise fabric—the unified data and context foundation that enables agents to reason consistently across the organization.

Knowledge Graph

Structured representation of enterprise knowledge showing relationships between entities, enabling intelligent agents to understand complex connections and make informed decisions.

Knowledge Integration

The process of unifying knowledge across fragmented enterprise systems into a consistent foundation that autonomous agents can reason from. Knowledge integration is the first layer of orchestration.

Legacy System Integration

The connection of autonomous agents to existing enterprise systems without replacing those systems. Metafore's orchestration layer handles this integration.

Machine Learning

Computational algorithms that learn patterns from historical data. Metafore uses machine learning as one component of agents, not as the entire decision-making system.

Modular Agents

Autonomous agents designed as focused, reusable components rather than monolithic systems. Modular agents enable faster deployment and easier scaling.

Neural Networks

Machine learning models that process information through interconnected layers, useful for pattern recognition but often lack explainability. Metafore uses neural networks alongside other reasoning mechanisms.

Orchestration

The coordination of autonomous agents and data flows to transform fragmented enterprise operations into a unified, intelligent system. Orchestration includes knowledge integration, agent coordination, governance, and evolution.

Orchestration Layer

The architectural component that coordinates how autonomous agents work together, handles sequencing and dependencies, and ensures governance. The orchestration layer sits on top of legacy systems.

Process Automation

The use of technology to automatically execute business processes and workflows. Process automation ranges from simple task repetition to complex intelligent reasoning.

Quality Assurance

The process of ensuring autonomous agent decisions meet accuracy, compliance, and business standards. Quality assurance includes continuous monitoring and validation.

Reasoning Calibration

The process of tuning autonomous agent reasoning patterns to align with business logic and domain knowledge. Well-calibrated agents make decisions matching human expert judgment.

RPA

Robotic Process Automation technology that automates repetitive, rule-based tasks through software robots. RPA is different from intelligent orchestration.

Silos

Enterprise systems that operate independently without shared knowledge or coordination. Silos prevent intelligent orchestration and limit the value of autonomous agents.

Time-to-Value

The time required to deploy autonomous agents and realize business benefits. Orchestration-first design enables faster time-to-value (weeks, not months).

Unified Intelligence

The convergence of multiple specialized agents operating together through orchestration to create comprehensive enterprise-wide intelligence. Unified intelligence emerges from coordinated agents.

Validation

The process of testing and verifying that autonomous agents make decisions meeting accuracy, fairness, and compliance standards. Validation happens both before and after deployment.

Workflow Orchestration

The definition and coordination of how work flows between autonomous agents and human teams. Workflow orchestration ensures proper sequencing, handoffs, and governance.

X-Ray Audit

The ability to examine and trace any autonomous agent decision back through its complete reasoning process, data inputs, and business rules applied. The ability to completely examine any autonomous agent decision by tracing it back through