AGENTIC AI FRAMEWORK

Agentic AI framework organizes AI based problem solving into processes and roles divided into three stages: 1) learning from data and advice, 2) organizing and verifying learnt knowledge, and 3) applying verified knowledge incrementally to solve problems. 

All done through autonomous agents driven by continually evolving knowledge.

This framework defines hooks to customize tasks and checkpoints for the three stages to form feedback cycles to suit specific enterprises.

AI QUANTIFICATION FRAMEWORK

Traditional ML model accuracies are limited to individual models and do not translate to quantify composite AI decisions. Unintended consequences are examples of limitations related to reliance on monolithic model centric accuracies.

AI Quantification framework focuses on decision levels using metrics defined at intersections of Human Machine Teaming themes and Responsible AI principles. 

For example a metric at the intersection of Observability theme and Equitable principle quantifies a decision on bias management, while another metric at the intersection of Directability theme and Equitable principle quantifies the same decision on how unintended consequences are managed.  

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AI Quantification leverages TeamingSpace Decision Path Analytics for exception alerts, such as specific Decision Step involved with contexts for remediations.

AI COMPETENCY CENTER  (AICC) FRAMEWORK

AI driven problem solving and AI quantification frameworks are supported by organizational roles and responsibilities defined by this framework.  AICC is an essential tool to sustain progress from enterprise investments.

AI competencies are built on technical foundations enabled by TeamingSpace AI platform and the framework maps the foundations to Competency Center roles and responsibilities.

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AI Competency Centers are essential to leverage AIOps.

HUMAN MACHINE TEAMING DESIGN THEMES FRAMEWORK

Human Machine Teaming goals are grouped into themes such as decision observability, ability to direct decision chains, common ground parameters, and governability. 

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Human Machine Teaming themes are vital to measure the effectivity of Agentic AI.

This framework specifies TeamingSpace tools to define procedures to be applied and contexts to be captured to uphold themes and measure adherence.

TeamingSpace AI platform enables Human Machine Teaming theme procedures to be added as seamless Advice.  Thus, outcomes of design themes become integral part of Agentic AI implementing Human Machine Teaming procedures.

ADVERSARIAL AI ATTACK QUANTIFICATION FRAMEWORK

Adversarial AI attacks are on the rise through data poisoning, input perturbations, concealed attack vectors that are enabled by black box nature of the models, etc.

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TeamingSpace AI Platform provides a unique opportunity to quantify adversarial AI attack metrics.  Attack vectors are sifted out because Decision Steps do not conceal them.  Decision Steps signature can be a viable tool to sift out attack vectors.  

Moreover Agents can vary threat surfaces to thwart adversarial detections.  TeamingSpace AI Platform continually evolves based on external frameworks such as MITRE ATT&CK.

DECISION PATH ANALYTICS FRAMEWORK

This framework, inspired by clickstream analytics, specifies mechanisms to help stakeholders measure and monitor algorithmic decisions at various units of control.

While Decision Steps enable proactive measures, Decision Path Analytics framework provides opportunities to continually guard them.