An agentic system of record is an authoritative operational system designed for AI agents as active users. Agents can read governed context, execute permitted work, verify effects, and write durable outcomes back to the record instead of operating from a detached copy. The agentic property is not simply that AI helps maintain data. The system exposes business objects, workflows, permissions, and actions in machine-usable form so an agent can pursue a goal across several steps while the record remains current. A CRM can be an agentic system of record for customer work. The same architecture can apply to ERP, HCM, IT service management, logistics, or other domains where agents need to act against authoritative state.

Context engine

A context engine is a runtime layer that assembles the most relevant, current, and permissioned information for a person or AI agent, connects it to the right entities and events, and turns new interactions into durable context for later decisions. It sits between raw business data and model reasoning. Systems of record establish what is authoritative. Knowledge bases store documents. Warehouses collect data. A context engine decides which pieces matter for this customer, task, moment, channel, and allowed action. A context engine is more than vector search or a long chat history. Production context includes identity resolution, structured records, relationships, recency, event state, permissions, provenance, working memory, and the result of previous actions.

Agentic context platform

An agentic context platform is a shared platform that gives AI agents persistent identity, memory, entity relationships, live business signals, governed retrieval, and write-back so they can coordinate work over time rather than operate from isolated prompts. It combines context infrastructure with execution state. An AI context platform can make information available to a model. An agentic context platform also tracks goals, actions, approvals, outcomes, and handoffs as agents pursue work across systems and channels. The platform does not replace every system of record. It connects their authoritative data to agent memory and action while preserving ownership, permissions, and provenance.

AI context platform

An AI context platform is a platform that connects structured records, unstructured knowledge, interactions, identity, and real-time signals so AI systems can retrieve relevant business context with provenance and permissions. It provides a reusable context layer for assistants, copilots, search, analytics, and agents. Typical capabilities include connectors, entity resolution, schemas, knowledge ingestion, memory, hybrid retrieval, access control, context assembly, and observability. An AI context platform is not automatically a system of record or an agent platform. It may read from authoritative systems without owning their data, and it may prepare context without executing actions.

Agent harness

An agent harness is the runtime and control system around an AI model that turns model outputs into reliable, stateful work. It manages the agent loop, tools, context, memory, permissions, execution environments, approvals, observability, retries, and stopping conditions. The model supplies reasoning and generation. The harness decides what the model can see, which tools it can call, how results return to context, what persists between sessions, when work pauses for a person, and how the system recovers from failure. A coding agent, research agent, or customer agent can use the same model with very different results because the harness shapes its environment, memory, interfaces, and feedback loop.

AI software factory

An AI software factory is an organizational production system that turns customer needs into shipped, reliable software with AI agents performing substantial work across planning, implementation, review, testing, deployment, operations, and feedback. It is the modern software factory: the full software delivery loop designed as a system, not a collection of individual coding prompts. Engineers increasingly define standards, context, environments, controls, and acceptance criteria while agents execute repeatable work inside those boundaries. The term is different from an AI factory that describes data-center infrastructure for producing model intelligence. An AI software factory is about how an organization continuously produces and operates software.

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