Agentics & ResearchOps Glossary This document serves as a living glossary of terms used in The Signal in the Noise to ensure clarifications regarding AI classifications, historical context, and ResearchOps methodologies.
Unlike standard generative AI where a user must input isolated, manual prompts, an agentic workflow involves giving the machine a goal, an integrated toolkit, and the permission to execute several steps semi-independently to achieve that goal, governed by human review checkpoints.
A professional certification covering the frameworks, enterprise risk models, and organisational accountability structures required to deploy AI systems responsibly, rather than focusing purely on technical coding.
The dangerously misguided perception of AI as an infallible, all-knowing answer-engine. Relying on an AI as an oracle leads researchers to blindly accept machine outputs rather than actively challenging and calibrating the data against their own human rigour.
The human psychological tendency to over-trust and over-rely on machine-generated outputs simply because they appear formally structured, visually clean, or mathematically confident.
Traditional statistical algorithms (e.g., regressions, decision trees) that learn from historical data to classify or predict outcomes. Unlike modern LLMs, classical ML relies on highly structured data sets and clearly defined parameters rather than unstructured deep learning.
A field of AI that trains systems to derive meaningful information from digital images, videos, and visual inputs. It is fundamentally different from text-generation and relies on distinct recognition architectures.
A graveyard of legacy, historical research and data locked deep in highly sensitive, air-gapped internal enterprise servers (like strict SharePoint sites) that are functionally inaccessible to third-party cloud vendors but can be mined by secure, in-house agentic workflows.
The rules-based programming paradigm (standard in Symbolic AI and traditional software) where a human must explicitly write the code instructing the system exactly how to behave in every scenario (e.g., "If X, then Y").
The specific, predictable ways in which a given technology breaks down or produces errors. In the context of AI, understanding a system's failure mode (e.g., an LLM hallucinating vs. a deterministic system crashing on a syntax error) is essential for designing safe governance and review loops.