Plain definitions for the terms behind agentic automation: what they mean, why they matter for regulated work, and how each one shows up in Infofusion AI.
13 Terms
Concepts
Augmented Intelligence
Augmented intelligence is the design principle that AI should amplify human judgment rather than replace it: removing low-value operational load so professionals focus on context, decisions, and relationships.
Where "artificial intelligence" implies substitution, augmented intelligence keeps the person at the center of the decision. The system analyzes documents, applies domain rules, and proposes a reasoned assessment; the professional decides and guides, the system executes. It is the most defensible model for regulated processes, where a mistake costs more than the time saved.
In Infofusion AI — Augmented intelligence is the operating principle behind every Infofusion AI workflow: agents amplify operational capacity, your team keeps control over every sensitive decision.
Agentic AI describes AI systems that pursue a goal across multiple steps: planning, using tools, and acting on external systems instead of only answering a single prompt.
Unlike a chatbot that responds turn by turn, an agentic system decomposes a task, calls tools and APIs, checks intermediate results, and keeps going until the goal is met or a human is asked to decide. It is the foundation for automating real operational work instead of only drafting text.
In Infofusion AI — Infofusion AI runs agentic workflows where specialized agents read documents, apply your rules, and act on your systems, pausing for human confirmation on every sensitive decision.
An AI agent is a software component that perceives inputs, reasons over them with a language model, and takes actions through tools to accomplish a defined task.
An agent combines a model, a set of tools it is allowed to use, and a scope of responsibility. Specialized agents can be composed: one classifies documents, another validates data, another updates a system of record. Each is accountable for a narrow, testable job.
In Infofusion AI — In Infofusion AI, each agent is expert in one area (mortgage underwriting, AML screening, procurement) and operates only within the permissions and knowledge you grant it.
An AI assistant, or copilot, is a conversational tool that helps a person draft, summarize, or answer questions, but does not independently carry out multi-step work on business systems.
Assistants are reactive: they wait for a prompt and return a response for the user to act on. Agents are proactive: they execute a workflow end to end. The distinction matters in regulated operations, where the question is not "can it draft an email" but "can it complete the process with an audit trail".
In Infofusion AI — Infofusion AI is not a copilot bolted onto a chat box. It orchestrates agents that complete document-intensive workflows and return control to your team at decision points.
Multi-agent orchestration coordinates several specialized AI agents by sequencing their work, sharing context, and resolving dependencies, so they can complete a workflow together.
Rather than one large model doing everything, orchestration assigns each step to the agent best suited to it and manages hand-offs, retries, and escalation. This makes complex, multi-party processes observable and controllable at every stage.
In Infofusion AI — Infofusion AI orchestrates intake, extraction, reasoning, human review, and execution as a single governed pipeline across your channels and systems.
The Model Context Protocol (MCP) is an open standard that lets AI models connect to external tools, data sources, and systems through a consistent interface.
MCP standardizes how an agent discovers and calls capabilities (a database query, a CRM update, a document lookup) so integrations are reusable and permission-scoped instead of hard-coded per model. It is becoming the common layer for connecting agents to enterprise systems.
In Infofusion AI — Infofusion Actions is an MCP server that lets agents perform controlled operations on CRM, ERP, and APIs, always with scoped permissions and human confirmation for sensitive actions.
Retrieval-augmented generation (RAG) grounds an AI model's output in specific documents retrieved at query time, so answers cite real sources instead of relying on memorized training data.
A RAG system searches a knowledge base for the passages most relevant to a request, then asks the model to answer using only that retrieved context. This reduces hallucination and makes every claim traceable to a source, which is essential where accuracy has to be auditable.
In Infofusion AI — Infofusion AI grounds agent reasoning in your regulations, internal policies, and historical cases, so recommendations always cite the source passage they came from.
A knowledge base is a curated, searchable collection of documents and rules that an AI system uses as its authoritative source of truth.
In an agentic platform, the knowledge base holds the regulations, policies, and reference material agents must apply. Keeping it curated and versioned means the same question yields consistent, defensible answers over time.
In Infofusion AI — Infofusion Space stores your projects, dossiers, and knowledge bases, so agents reason over policies and precedents you control rather than generic web content.
Intelligent document processing (IDP) uses AI to classify documents, read their contents, and extract validated structured data from unstructured files like PDFs, scans, and photos.
IDP goes beyond OCR: it recognizes document type, understands layout and language, pulls the relevant fields, checks internal consistency, and flags anomalies. It turns a pile of documents into a structured, verifiable dataset.
In Infofusion AI — Infofusion AI classifies IDs, payslips, tax returns, account statements, and contracts, extracts the fields that matter, and traces every value back to its exact source page.
Human-in-the-loop is a design pattern where an AI system pauses to request human confirmation before taking a consequential or sensitive action.
HITL keeps accountability with people for decisions that carry legal, financial, or safety weight. Well-designed HITL surfaces the evidence and the proposed action so the reviewer can approve, edit, or reject quickly, without redoing the work the agent already did.
In Infofusion AI — Human-in-the-loop is the default in Infofusion AI: agents prepare and propose, and authorized staff confirm every sensitive decision before it is executed.
An AI workflow is a defined sequence of steps (intake, extraction, reasoning, review, and execution) that AI agents run to complete a business process end to end.
Modeling work as an explicit workflow makes it observable and repeatable: each step has inputs, outputs, and checks, and can be inspected or paused. It is the difference between a one-off model call and an operational process you can trust and audit.
In Infofusion AI — Every Infofusion AI process follows the same five-step workflow, so operations across insurance, finance, and legal share one predictable, auditable operating model.
AI governance is the set of controls (permissions, policies, logging, and human oversight) that keep an AI system's actions safe, compliant, and accountable.
Governance defines what agents may access, which actions require approval, and how every decision is recorded. In regulated sectors it is not optional: it is what makes automated work defensible to auditors and regulators.
In Infofusion AI — Infofusion AI enforces scoped permissions, policy-based rules, human confirmation, and full logging, so automation stays inside your compliance boundary.
An audit trail is a complete, reconstructable record of every input, extraction, rule applied, source cited, human confirmation, and system action taken during a workflow.
For AI-driven processes, the audit trail is how you prove what happened and why. It links each output back to the evidence and the person who approved it, so a decision can be reviewed or defended long after the fact.
In Infofusion AI — Infofusion AI logs every step of every workflow, from source page to final action, into an audit trail your compliance and risk teams can reconstruct on demand.
Bring one process and watch the vocabulary become an operating model.
In 30 minutes we map a real process into intake, extraction, reasoning, and the human checkpoints your team keeps. The concepts on this page, running on your work.
