The AI terms that matter in operations, in plain English
From agentic AI to digital twins: what each of 34 terms means, and where it shows up across the Owllys brands.
All terms, A to Z
Agent-to-Agent (A2A)
Agents coordinating with other agents — a planning agent asking a sourcing agent to cover a shortfall, for example — through structured messages instead of human hand-offs.
FLX4Agentic AI
AI that pursues a goal in steps — it plans, calls tools, checks its own work and acts, rather than answering a single prompt. The Owllys core loop (Predict → Decide → Execute → Learn) is agentic end to end.
AI & Machine LearningAI Adoption
The organisational side of AI: governance, training, phased autonomy and measured outcomes. The reason Owllys plans deployment and adoption together.
Tech & AI ConsultingAI Agent
A software worker powered by a language model plus tools and guardrails. It senses context, makes a decision and executes an action — e.g. re-sequencing a production line or re-routing a delivery.
AI & Machine LearningAI Copilot
An assistant embedded in a workflow that drafts, checks and recommends while a person stays in control. The step before full agentic automation.
AI Governance
The rules, audit trails and approval gates around AI decisions — who allowed what, on which data, with what rationale. “AI recommends. Business governs.”
AI Security & GovernanceAI Orchestration
The conductor layer that decides which agent, model or tool handles each step of a workflow, keeps them in order and recovers when a step fails.
Autonomous Agent
An agent trusted to complete a whole job without step-by-step approval — always inside guardrails, with a full audit trail. The end state of governed adoption.
Computer Vision
AI that understands images and video — face-based access, safety monitoring, artwork checks, OEE sensing on the press.
Confidence Gating
A quality bar an AI answer must clear before it is used. Below the gate, Owllys agents retrieve more context and iterate instead of guessing.
Context Window
How much information a model can consider at once. Bigger windows let agents reason over whole contracts, schedules and histories in one pass.
Decision Intelligence
Turning analytics into ranked, executable decisions with expected impact — not another dashboard to interpret.
Demand Sensing
Short-horizon forecasting from live signals — sell-through, promotions, weather — rather than last year’s averages. The heart of PlanningNest.
PlanningNest · FLX4Digital Twin
A live software mirror of a physical operation. Owllys twins keep a 1:1 mirror: test the decision in the twin, then fire it in the real world.
Digital twinEdge AI
Models running on or near the device — the gate, the vehicle, the machine — for sub-second decisions without a round trip to the cloud.
Evals (AI Evaluation)
Systematic testing of AI behaviour against known cases before and after deployment — the QA discipline that separates production AI from demos.
Explainability
Every recommendation carries its why: the data it used and the trade-offs it weighed. Essential for audits and for trust on the floor.
Fine-Tuning
Adapting a foundation model to a domain with your examples. Often unnecessary when RAG over live data does the job — cheaper and always current.
Foundation Model
A large pre-trained model (language, vision or multimodal) used as the base for many tasks instead of training one model per problem.
Grounding
Tying every AI statement to verifiable source data. Ungrounded output is opinion; grounded output can be audited.
Guardrails
Hard limits an agent cannot cross — policy, contract terms, SLAs, spend ceilings. Out-of-policy actions are blocked and surfaced, not silently attempted.
Hallucination
When a model states something plausible but false. Mitigated with RAG, grounding and confidence gating — never ship a low-confidence answer silently.
Human-in-the-Loop (HITL)
High-stakes actions wait for one-click human sign-off; routine ones run automatically. You choose the line, per process and risk tier.
AI Security & GovernanceInference
Running a trained model to get an answer. Inference cost and latency — not training — dominate the economics of production AI.
LLM (Large Language Model)
The foundation model class behind modern AI — trained on vast text, able to reason, write and use tools. Owllys deploys the most capable frontier models behind your governance.
MCP (Model Context Protocol)
An open standard for connecting AI models to tools and data sources in a uniform way — one protocol instead of a custom integration per system.
Multi-Agent System
Many specialised agents working one operation together under an orchestrator. In CMYK2, APMS runs packaging launches with specialised cooperating agents that prepare work for people to approve.
APMS · CMYK2Operations Graph
One connected data model of the business — orders, stock, machines, vehicles, suppliers, customers — that every Owllys agent reads and writes. The moat that compounds.
AI & Machine LearningPredictive Maintenance
Using machine telemetry to predict failures before they stop the line, and scheduling the fix off-shift. In INDUX2, OpsNest's machine health does this line by line.
OpsNest · INDUX2Prompt Engineering
Designing the instructions, context and output format given to a model so it behaves reliably in production, not just in a demo.
RAG (Retrieval-Augmented Generation)
Before answering, the model retrieves your live data — contracts, stock, telemetry — and grounds its answer in it. This is how Owllys agents stay factual about your operation.
AI & Machine LearningReasoning Model
A model that thinks in intermediate steps before answering — stronger on planning, maths and multi-step operational decisions.
Token
The unit models read and write (roughly ¾ of a word). Context windows, costs and speed are all measured in tokens.
Tool Use / Function Calling
The mechanism that lets a model act — query a database, create a PO, re-plan a schedule — by calling typed functions instead of just writing text.
Hear these terms in your own operation
We explain every recommendation in plain language, with the evidence beside it.