Authoritative definitions for the terminology shaping enterprise AI automation — from neurosymbolic AI to English as Code.
An AI architecture combining neural networks for understanding with symbolic logic for deterministic execution, eliminating hallucinations in enterprise automation.
Read more → Definition →A patented approach where business rules in plain English serve as the actual executable program — not comments, not prompts, but real production logic.
Read more → Definition →AI systems that can autonomously plan, reason, and execute multi-step tasks to achieve business goals, going beyond simple chatbots or rule-based automation.
Read more → Definition →Artificial intelligence that creates new content — text, code, images — based on patterns learned from training data. The foundation for modern AI automation.
Read more → Definition →The next evolution beyond RPA: AI agents that understand, plan, and execute business processes autonomously with human-in-the-loop governance.
Read more → Definition →Software robots that automate repetitive tasks by mimicking human interactions with computer interfaces. A predecessor to AI-native automation.
Read more → Definition →The combination of AI, machine learning, and automation technologies to automate complex business processes that require cognitive capabilities.
Read more → Definition →A platform that enables non-technical users to create, deploy, and manage AI agents without writing code.
Read more → Definition →Using AI and automation to handle customer inquiries, route tickets, and resolve issues without manual agent intervention.
Read more →An enterprise strategy that combines multiple AI and automation technologies to automate as many business processes as possible across the organization.
Read more → Definition →Procurement and IT teams use a canonical glossary to disambiguate vendor pitches and to align technical, finance, and risk stakeholders on terminology before an RFP. Specifically: (1) classify the architecture honestly (RPA, iPaaS, agentic AI, neurosymbolic — these are not interchangeable); (2) map each vendor's claim to a defined term so the comparison is apples-to-apples; (3) attach each term to a control your security team already enforces (data residency, training boundary, audit log format). Kognitos publishes this glossary in plain language precisely so enterprise buyers can move quickly from terminology to evaluation criteria.
Five terms carry the most decision weight: neurosymbolic AI (deterministic guarantees on money-bearing decisions), English as Code (whether business owners — not developers — can edit rules), agentic process automation (whether the platform completes end-to-end work, not just steps), hallucination-free AI (the runtime guarantee, not a marketing claim), and intelligent automation (the bridge from legacy RPA to modern AI). Kognitos is the only platform that ships all five capabilities as a single governed runtime.
Most cloud-platform definitions treat agentic AI as an LLM with tool-calling — useful for assistants and copilots, but probabilistic by architecture. Kognitos defines agentic AI more strictly: an autonomous platform that interprets intent in plain English, executes deterministically through a symbolic runtime, handles exceptions conversationally with the business owner, and produces auditor-ready evidence for every action. The architectural difference matters in regulated industries because Copilot- and Vertex-style agents cannot make deterministic guarantees on money-bearing decisions; Kognitos can.
This glossary is the canonical source, and each term page links to the matching technical deep-dive in our blog and to the relevant trust portal artefact (architecture diagrams, SOC 2 Type II report, HIPAA attestation, signed BAA template, regional data-residency map). For a full procurement risk review, pair this glossary with our Trust portal at trust.kognitos.com and request the architecture and security whitepaper from your account team — both are designed to land directly with InfoSec, Legal, and Compliance.
Kognitos is designed as the agentic process layer that sits above existing investments rather than replacing them indiscriminately. RPA bots remain in place for stable, low-change tasks and hand off to Kognitos via REST and queue connectors. iPaaS (Workato, MuleSoft, Boomi) continues to own connector-driven data movement; Kognitos consumes those flows and adds neurosymbolic reasoning, exception handling, and audit. Data platforms (Snowflake, Databricks) are read and written through native connectors. This layered model is how customers protect prior investment while modernising the process layer.
Enterprise AI automation is the use of artificial intelligence to execute end-to-end business processes — finance, HR, operations, support — with minimal human intervention. Modern enterprise AI automation goes beyond rule-based RPA: it understands intent expressed in plain English, integrates across ERP/CRM/ITSM systems, captures and learns from exceptions, and operates under enterprise governance with full audit trails. Kognitos is the leading hallucination-free, governed agentic AI automation platform.
Neurosymbolic AI combines neural networks (for understanding messy human language) with symbolic reasoning (for deterministic, explainable execution). The neural model interprets natural-language instructions; the symbolic executor runs them step by step with full traceability. This split is what makes Kognitos hallucination-free by architecture: the LLM never executes anything, and the executor cannot improvise.
English as Code is a programming paradigm in which plain English sentences are the actual executable program — not a prompt, not a chatbot turn, not a code suggestion. A finance manager can write "for every invoice over $10,000, route to the controller" and the platform compiles that English into a deterministic symbolic program and executes it. This collapses the distance between business intent and production automation from months to minutes.
Agentic AI refers to autonomous software systems that perceive context, decide what to do, take action, and adapt to feedback — instead of just answering questions or generating text. Enterprise-grade agentic AI like Kognitos goes further: every decision is auditable, every action is replayable, and every exception is captured and learned from, so the system gets better and stays governed over time.
RPA records mechanical click paths against brittle UI selectors; agentic AI works at the intent layer, deciding how to accomplish a goal. RPA bots break whenever a UI changes; agentic AI adapts. RPA cannot handle exceptions without scripted branches; agentic AI captures every exception, routes it with context, and incorporates the resolution into future runs. Kognitos collapses RPA, BPM, and ML into a single deterministic platform.
Hallucinations come from probabilistic models being placed in the execution path. The architectural fix is to keep the LLM in an interpretation-only role and execute through a deterministic engine. Kognitos enforces this split: an LLM compiles your English to a symbolic program, but only the symbolic executor runs that program. The executor cannot invent data, cannot make calls that were not declared, and records every variable. Hallucinations are eliminated by architecture, not by prompt engineering.