Enterprise AI Glossary
Plain-English definitions for the terms enterprises actually use when buying, building, and governing on-premise AI. Covers AI agents, multi-agent orchestration, the EU AI Act, private RAG, LLM routing, agentic design patterns, and the operational vocabulary in between. Each term links to the relevant VDF AI pillar, blog post, or product page for deeper context.
A
- # Agentic RAG Retrieval pattern where agents plan, retrieve, validate, and synthesize across multiple steps and sources.
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Agentic RAG replaces the fixed embed-then-generate pipeline with a workflow: a planner decomposes the question, retrievers route per sub-question, a validator checks evidence, and a synthesizer cites. It is the dominant pattern for cross-system enterprise questions that traditional RAG cannot answer reliably. See Agentic RAG vs Traditional RAG.
See also: RAG (Retrieval-Augmented Generation), GraphRAG
- # AI Agent Framework A library or SDK that provides the scaffolding — loops, memory, tool binding — for building AI agents.
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AI agent frameworks (LangChain, CrewAI, AutoGen, Pydantic AI, etc.) accelerate prototyping but do not solve governance, routing, or deployment. Enterprise teams typically use a framework inside a platform that adds those missing layers. See AI Agent Frameworks for a comparison, and VDF AI Networks for the platform approach.
See also: AI Agent, Multi-Agent Orchestration
- # AI Agent Governance The policies, permissions, logs, and approvals that make enterprise agents accountable.
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AI agent governance covers who can build agents, which tools they can call, which models are approved, what knowledge they can read, and how every execution is recorded. Without it, agent rollout becomes opaque and unauditable. The AI Agent Governance pillar describes the platform controls VDF AI uses to make agents reviewable end-to-end.
See also: Audit Log (AI), Tool Governance, Risk Management System
- # AI Inventory A live register of every AI system, model, and third-party AI tool in use across the enterprise.
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An AI inventory (sometimes “AI system register”) is the prerequisite for any compliance program. You cannot classify risk, run a DPIA/FRIA, or report to regulators without a baseline of what exists. The AI Inventory & Shadow AI Discovery use case explains how to build one across code repos, document stores, and collaboration tools.
See also: Shadow AI, AI Risk Classification
- # AI Platform RFP A structured request-for-proposal tuned to enterprise AI platforms rather than generic SaaS.
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An AI platform RFP evaluates vendors on the dimensions that decide outcomes for agentic systems: deployment model, multi-model routing, governance surface, execution trace export, integration breadth, and total cost. Generic SaaS templates miss most of these. See Enterprise AI Platform Evaluation for the RFP checklist, POC guide, and vendor scorecard.
See also: Decision Traceability, On-Premise AI Agent Platform
- # AI Risk Classification (EU AI Act) Mapping each AI system to the EU AI Act risk tiers: unacceptable, high, limited, minimal.
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The EU AI Act assigns obligations based on risk tier. High-risk systems trigger conformity assessments, technical documentation, and post-market monitoring; limited-risk systems trigger transparency duties; some uses are outright prohibited. See AI Risk Assessment & Classification for the workflow VDF AI uses to map this systematically.
See also: High-Risk AI System, Conformity Assessment
- # Air-Gapped AI Deployment Running AI systems on infrastructure with no external network connectivity.
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Air-gapped deployments are required in defence, intelligence, and certain critical infrastructure environments where no data — including model weights, prompts, or telemetry — may leave the physical perimeter. This is the most constrained form of on-prem AI and demands local models, local retrieval, and offline-capable orchestration. See Air-Gapped AI Deployments for Restricted Networks.
See also: On-Premise AI Agent Platform, Data Sovereignty, Air-Gapped LLM deployment, Air-Gapped RAG
- # Approval Workflow (Human-in-the-Loop) A required human decision point inside an agent workflow before a sensitive action is taken.
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An approval workflow pauses an agent before a high-impact step — sending a customer email, modifying a record, escalating a case — and routes the proposed action to a human reviewer. Approvals should be enforced in the orchestrator, not at the UI, so they cannot be bypassed by a different caller. See Human-in-the-Loop and AI Agent Governance.
- # Audit Log (AI) The immutable record of prompts, retrieval events, tool calls, model choices, and outputs.
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An AI audit log is the source of truth when something looks wrong: a sensitive output, a regulator request, or a misbehaving agent. It needs to capture timestamps, user identity, agent identity, retrieved sources, tool calls, and model decisions. See AI Agent Observability: Logs, Traces, Audit.
See also: Observability (AI Agent), Decision Traceability
B
- # Bias Detection & Fairness Auditing Testing AI outputs for systematic group-level disparity and documenting mitigations.
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Bias detection evaluates whether a model or workflow produces materially different errors, outcomes, or service quality across relevant groups. For high-risk AI systems, this evidence can support data-governance, risk-management, accuracy, and monitoring duties; it is also increasingly expected in enterprise procurement. See Bias Detection & Fairness Auditing.
C
- # Chunking Splitting documents into smaller segments for embedding and retrieval in a RAG pipeline.
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Chunking is the preprocessing step that determines RAG quality. Too large and the embedding is diluted; too small and you lose context. Strategies include fixed-size, sentence-boundary, semantic, and hierarchical chunking. The right choice depends on document type, embedding model, and downstream task. See RAG Technology Best Practices.
See also: RAG (Retrieval-Augmented Generation), Embeddings
- # Conformity Assessment The EU AI Act procedure by which a high-risk AI system is shown to meet legal requirements before placement on the market.
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A conformity assessment verifies that a high-risk AI system has a risk management system, technical documentation, data governance, logging, transparency, human oversight, accuracy and cybersecurity controls. The architecture decisions you make early — especially logging and traceability — determine whether this is straightforward or painful. See EU AI Act-Ready On-Premises AI Architecture.
D
- # Data Sovereignty Keeping data and the systems that process it inside a defined legal or geographic boundary.
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Data sovereignty is why many regulated organizations cannot route prompts, retrieved passages, or logs through uncontrolled third-party services. It is the practical driver behind on-prem and sovereign-cloud AI architectures. See Data Sovereignty vs Data Residency and Sovereign AI with VDF AI.
See also: On-Premise AI Agent Platform, Hybrid Deployment, Air-Gapped AI Deployment, Sovereign LLM, Sovereign RAG
- # Decision Traceability The ability to reconstruct, after the fact, why an agent or model produced a given output.
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Decision traceability ties outputs back to the inputs, retrieved sources, tool calls, and model selection that produced them. Without it, “why did the system decide that?” has no answer. See Decision Traceability for Audits.
- # DORA (Digital Operational Resilience Act) EU regulation requiring ICT resilience, incident reporting, and third-party risk controls for financial entities.
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DORA brings AI vendors used by financial entities into the ICT third-party register, expects aligned incident classification, and requires resilience testing that includes AI workflows. Concentration on a single AI provider is a flagged risk, which is one reason regulated buyers prefer platforms with deployment flexibility. See AI Governance Framework for Regulated Industries.
See also: EU AI Act, Data Sovereignty
Full definition: DORA (Digital Operational Resilience Act) →
- # DPIA / FRIA Data Protection Impact Assessment (GDPR) and Fundamental Rights Impact Assessment (EU AI Act).
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DPIA is a structured analysis of how a system affects personal data; FRIA is the EU AI Act equivalent for fundamental rights, required for high-risk systems used by public bodies and certain private deployers. Running them together avoids duplicate documentation. See DPIA/FRIA Integrated Impact Assessment.
E
- # Energy-Aware Routing (Green AI) Routing decisions that consider the energy and carbon profile of each model, not only quality and cost.
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Energy-aware routing prefers smaller, more efficient models for high-volume work where the quality threshold is met, reserving frontier models for steps that genuinely need them. See Orchestration & Energy research and the white paper How We Reduce Energy Consumption.
See also: LLM Routing
- # EU AI Act The European regulation that classifies AI systems by risk and assigns obligations to providers and deployers.
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The EU AI Act entered into force on 1 August 2024 and applies in stages. Prohibited-practice and AI-literacy rules began applying in February 2025, GPAI obligations in August 2025, and transparency rules in August 2026. Under the current timetable, specified high-risk use cases apply from December 2027 and high-risk systems embedded in regulated products from August 2028. See EU AI Act-Ready Architecture and VDF AI Compliance.
See also: GPAI Model, Provider vs Deployer, Transparency Obligations
F
- # Foundation Model A large model trained on broad data that is adapted (via prompting, fine-tuning, or RAG) for downstream tasks.
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Foundation models are the generic substrate of modern AI. Enterprises rarely use them raw; they layer retrieval, routing, and governance on top. For high-volume internal tasks, a smaller specialised model often outperforms a generic foundation model on cost, latency, and energy. See Small Language Models in Enterprise AI.
See also: Small Language Model (SLM), Fine-Tuning
G
- # Governance Plane The layer of the AI stack that enforces policy across identity, models, tools, retrieval, and logs.
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A governance plane is what turns a collection of agents into a controllable enterprise system. It is not a reporting dashboard added later; it is the runtime that decides who can do what, with which model, on which data. See AI Agent Governance.
- # GPAI Model General-Purpose AI Model — a model with broad capability across tasks, as defined in the EU AI Act.
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GPAI providers face specific transparency, documentation, and (for systemic-risk models) evaluation and incident-reporting obligations. Deployers of GPAI inside a high-risk product inherit additional duties. See EU AI Act-Ready Architecture.
- # GraphRAG (Knowledge Graph RAG) Retrieval pattern that uses a knowledge graph as a structured backbone, often alongside vector search.
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GraphRAG (also called Knowledge Graph RAG) treats entities, relationships, and properties as first-class retrieval targets. It outperforms pure vector retrieval on multi-hop and relational questions — policy applicability, contract obligations, supply chain dependencies — and combines well with vector retrieval in hybrid setups. See Knowledge Graph RAG.
See also: Agentic RAG, Vector Database
H
- # Hallucination A confident-sounding output that is not grounded in the retrieved or provided evidence.
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Hallucinations are reduced — not eliminated — by good retrieval, citations, and evaluation. In an enterprise setting, the more dangerous failure mode is a hallucinated action, not a hallucinated answer: an agent that fabricates a justification for the wrong tool call. See Private RAG vs Enterprise Search and Agent Evaluation.
See also: RAG (Retrieval-Augmented Generation), AI Guardrails
- # High-Risk AI System An EU AI Act category covering AI used in safety components or in listed high-impact domains.
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High-risk AI systems include those used in critical infrastructure, education, employment, essential services, law enforcement, migration, and the administration of justice. They trigger the bulk of EU AI Act obligations. Architecture matters because logging, oversight, and documentation cannot be retrofitted cheaply. See EU AI Act-Ready Architecture.
- # Human Oversight The requirement and capability for qualified humans to monitor, intervene in, and override AI system decisions.
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Human oversight is a core EU AI Act requirement for high-risk systems. It ranges from simple approval gates to real-time monitoring dashboards that flag anomalous agent behaviour. The key question is not whether humans can intervene, but whether the system makes intervention practical and timely. See Human Oversight Requirements and Human-in-the-Loop.
See also: Approval Workflow (Human-in-the-Loop), High-Risk AI System
- # Hybrid Deployment Running sensitive workloads on local infrastructure while selectively using cloud models for approved tasks.
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Hybrid deployment is the realistic shape of most regulated enterprise AI. Retrieval, embeddings, logs, and routing stay local; specific workflows can call external models when policy allows and quality benefits justify it. See On-Premise AI Agent Platform.
See also: Private LLM, On-Premises LLM, Data Sovereignty
K
- # Knowledge Graph A structured representation of entities, relationships, and properties that machines can traverse and reason over.
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A knowledge graph encodes facts as triples (entity–relationship–entity) and enables multi-hop queries that pure vector search cannot answer reliably. In enterprise AI, knowledge graphs power policy applicability checks, supply-chain tracing, and contract-clause navigation. See Knowledge Graph RAG and Federated Knowledge Graph Playbook.
See also: GraphRAG (Knowledge Graph RAG), Semantic Search
L
- # LLM Observability Inspecting prompts, retrievals, tool calls, model choices, latency, cost, and outputs in production.
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LLM observability is what turns a black box into an operable system. Without it, every failure looks like “the agent did something strange.” See AI Agent Observability: Logs, Traces, Audit.
- # LLM Routing Choosing the right model for each task based on quality, cost, latency, energy, and policy.
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LLM routing is how enterprise AI economics actually work. Routing pushes classification and summarization to smaller models, reserves frontier models for hard reasoning, and respects policy (some tasks cannot leave a boundary). See the LLM Routing pillar and the SEEMR architecture for the VDF AI implementation.
See also: SEEMR (Self-Evolving Model Router), Energy-Aware Routing, Self-Hosted LLM, On-Premises LLM
M
- # MCP (Model Context Protocol) An open protocol for exposing tools, data sources, and prompts to AI agents in a standard way.
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MCP lets you wrap an internal API or a third-party agent platform as a tool any compatible agent can invoke. In practice it is the integration glue between platforms — for example, running n8n or CrewAI flows as tools inside a VDF AI Network. See Model Context Protocol and Integrate n8n & CrewAI as MCP Tools.
See also: Tool Use / Function Calling, Agent-to-Agent Protocol
- # Model Card Short structured documentation of a model: intended use, training data summary, evaluations, and limits.
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A model card is the artefact procurement and compliance teams ask for. It is also useful internally: when routing decides which model handles a step, the card explains why that model is approved for that workload. See Model Evaluation Suite.
See also: Model Evaluation
- # Model Evaluation Systematically testing AI models and agents against benchmarks, domain tasks, and safety criteria before deployment.
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Model evaluation goes beyond accuracy scores. Enterprise evaluation tests for domain fitness, hallucination rate, bias, latency under load, compliance with output policies, and regression when models are updated. Without it, model swaps and routing changes are guesswork. See Agent Evaluation and Model Evaluation Suite.
See also: Model Card, LLM Routing
- # Multi-Agent Orchestration Coordinating multiple specialised agents into one governed workflow with routing, retries, and oversight.
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Multi-agent orchestration is what enterprise AI looks like past the single-chatbot phase: retrieval, reasoning, validation, and action are different agents, run as a DAG with per-node model routing and explicit approval points. See the AI Agent Orchestration pillar and VDF AI Networks.
See also: Multi-Agent System, LLM Routing, Tool Governance
- # Multi-Agent System An architecture where multiple specialised AI agents collaborate, each with its own role, tools, and scope.
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A multi-agent system divides complex work across agents: one retrieves, one reasons, one validates, one acts. This decomposition improves reliability and auditability — each agent has a narrow scope and can be evaluated independently. See Multi-Agent Systems and Secure Multi-Agent Networks.
See also: Multi-Agent Orchestration, Agent-to-Agent Protocol
O
- # Observability (AI Agent) Live and historical visibility into agent execution — what ran, on what data, with what result.
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AI agent observability goes beyond LLM call logs. It needs to capture orchestration paths, tool invocations, approvals, retries, and downstream effects. See Agent Observability and AI Agent Observability: Logs, Traces, Audit.
See also: Audit Log (AI), LLM Observability
P
- # Permission-Aware Retrieval RAG that respects source-system access controls instead of surfacing content the user cannot read.
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Without permission-aware retrieval, the RAG layer becomes a permissions bypass: it indexes content the user would not be allowed to open in the source system and serves it back through the chat surface. See Private RAG.
See also: On-Premises RAG, Air-Gapped RAG, Private RAG
- # Private RAG Retrieval-augmented generation over enterprise-controlled data, storage, and embeddings.
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Private RAG differs from cloud RAG in three ways: where the embeddings live, who controls retrieval policy, and whether retrieval is audited as part of the AI platform. See the Private RAG pillar and Private RAG vs Enterprise Search.
See also: Permission-Aware Retrieval, Vector Database, Embeddings, Self-Hosted RAG, Sovereign RAG
- # Prompt Injection An attack where malicious input causes an AI system to ignore its instructions or execute unintended actions.
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Prompt injection — both direct (user input) and indirect (via retrieved content) — is the most discussed LLM security risk. Defences include input sanitisation, instruction-data separation, output filtering, and treating retrieved text as data rather than instructions. No single defence is complete; layered guardrails at the platform level are required. See AI Guardrails and Enterprise AI Agent Security.
See also: AI Guardrails, Zero-Trust AI Agent
- # Provider vs Deployer EU AI Act roles: the provider places an AI system on the market; the deployer uses it under their authority.
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Most enterprises are deployers, not providers. Deployer duties (oversight, monitoring, fundamental-rights impact assessment) are different from provider duties (conformity assessment, technical documentation, post-market monitoring). Knowing which role you play for each system is the start of any compliance plan. See EU AI Act-Ready Architecture.
R
- # RAG (Retrieval-Augmented Generation) Retrieving relevant passages and passing them into a generation step so answers are grounded in evidence.
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RAG reduces hallucination and gives users source citations. Quality depends on ingestion, chunking, embeddings, ranking, and freshness — “upload documents and chat” is the demo, not the architecture. See RAG Technology Best Practices.
See also: Private RAG, On-Premises RAG, Self-Hosted RAG
- # ReAct Pattern An agent control loop that interleaves Reasoning and Acting steps — think, act, observe, repeat.
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The ReAct pattern (Reason + Act) structures an agent's execution as alternating reasoning traces and tool calls, with each observation feeding the next thought. It is the most widely adopted agent loop because it is inspectable: you can read why the agent chose each action. See ReAct Agent Pattern and Agentic Design Patterns.
See also: AI Agent, Tool Use / Function Calling
- # Risk Management System (EU AI Act) A continuous, documented process for identifying, evaluating, and mitigating risks of an AI system.
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The EU AI Act requires high-risk systems to operate a risk management system across the full lifecycle: design, development, deployment, and post-market monitoring. It is not a one-off document. See AI Governance & Compliance Problems.
S
- # SEEMR (Self-Evolving Model Router) VDF AI's architecture for governed, observable, adaptive LLM routing across providers and local models.
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SEEMR formalises model selection as a runtime decision shaped by policy, cost, latency, energy, and outcome feedback — rather than a static configuration. See the SEEMR architecture page and the white paper The Self-Evolving Model Router.
See also: LLM Routing, Energy-Aware Routing
- # Shadow AI AI tools used inside the organization without security, procurement, or compliance approval.
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Shadow AI is the gap between employees adopting AI tools and the organization discovering, reviewing, and governing that use. It can include consumer chatbots, embedded SaaS features, browser extensions, model APIs, and employee-built automations. You cannot govern what you cannot see. See AI Inventory & Shadow AI Discovery.
- # Sovereign AI AI operated under a specific jurisdiction's legal, infrastructure, and data-residency requirements.
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Sovereign AI is the practical answer to “our data cannot leave the country / cannot leave our control.” It usually combines on-prem or sovereign-cloud deployment, private retrieval, and approved local or jurisdiction-bound models. See the AI Agent Security & Data Sovereignty pillar for the zero-trust architecture, and Sovereign AI with VDF AI for the product perspective.
See also: Zero-Trust AI Agent, Data Sovereignty
T
- # Tool Governance Scoping which agents may call which tools, under which conditions, with which approval steps.
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Tool governance is where governance becomes operational. An agent that can read documents is one risk profile; one that can modify records or send customer-facing content is another. The orchestrator must enforce scope, not the calling code. See AI Agent Governance.
See also: Tool Use / Function Calling, AI Agent Governance
- # Transparency Obligations EU AI Act duties to disclose AI use to affected people — for example, when interacting with a chatbot or seeing AI-generated content.
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Transparency obligations apply broadly, not only to high-risk systems. Deployers of emotion-recognition or biometric-categorisation systems have additional disclosure duties; providers of generative AI must mark synthetic outputs. See AI Governance & Compliance Problems.
Z
- # Zero Data Retention A processing mode in which the model provider does not store inputs or outputs after a request completes.
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Zero data retention is one of the controls procurement teams ask for when sensitive prompts must traverse an external API. It is not a substitute for keeping data local — logs, embeddings, and retrieved passages still live somewhere — but it constrains what a third party can keep. See Why Data Security Matters in AI.
- # Zero-Trust AI Agent Architecture where every agent has its own identity, every tool call is authorized, and every output is logged.
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Zero-trust applied to AI agents means no agent, model output, or retrieved passage is trusted by default. Identity is per-agent, tool access is least-privilege, retrieved text is treated as data rather than instructions, and execution traces are exportable for replay. This is the baseline for regulated and sovereign deployments. See AI Agent Security & Data Sovereignty.
See also: Sovereign AI, Tool Governance
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