AI glossary
The AI terms that matter in operations, in plain English
From agentic AI to digital twins — what each term means and where it shows up inside Owllys.
- Agentic AI01
- 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.
- See it in Owllys
- AI Agent02
- 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.
- See it in Owllys
- Agent-to-Agent (A2A)03
- 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.
- See it in Owllys
- Multi-Agent System04
- Many specialised agents working one operation together under an orchestrator. Owllys APMS runs packaging governance with 12 cooperating agents.
- See it in Owllys
- AI Orchestration05
- 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.
- RAG (Retrieval-Augmented Generation)06
- 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.
- See it in Owllys
- Grounding07
- Tying every AI statement to verifiable source data. Ungrounded output is opinion; grounded output can be audited.
- Hallucination08
- When a model states something plausible but false. Mitigated with RAG, grounding and confidence gating — never ship a low-confidence answer silently.
- Confidence Gating09
- 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.
- Human-in-the-Loop (HITL)10
- High-stakes actions wait for one-click human sign-off; routine ones run automatically. You choose the line, per process and risk tier.
- See it in Owllys
- AI Governance11
- The rules, audit trails and approval gates around AI decisions — who allowed what, on which data, with what rationale. “AI recommends. Business governs.”
- See it in Owllys
- Guardrails12
- Hard limits an agent cannot cross — policy, contract terms, SLAs, spend ceilings. Out-of-policy actions are blocked and surfaced, not silently attempted.
- Explainability13
- Every recommendation carries its why: the data it used and the trade-offs it weighed. Essential for audits and for trust on the floor.
- LLM (Large Language Model)14
- 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.
- Foundation Model15
- A large pre-trained model (language, vision or multimodal) used as the base for many tasks instead of training one model per problem.
- Fine-Tuning16
- Adapting a foundation model to a domain with your examples. Often unnecessary when RAG over live data does the job — cheaper and always current.
- Prompt Engineering17
- Designing the instructions, context and output format given to a model so it behaves reliably in production, not just in a demo.
- Context Window18
- How much information a model can consider at once. Bigger windows let agents reason over whole contracts, schedules and histories in one pass.
- Tool Use / Function Calling19
- 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.
- MCP (Model Context Protocol)20
- 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.
- Reasoning Model21
- A model that thinks in intermediate steps before answering — stronger on planning, maths and multi-step operational decisions.
- Inference22
- Running a trained model to get an answer. Inference cost and latency — not training — dominate the economics of production AI.
- Token23
- The unit models read and write (roughly ¾ of a word). Context windows, costs and speed are all measured in tokens.
- Digital Twin24
- A live software mirror of a physical operation. Owllys ControlTower keeps a 1:1 twin — test the decision in the twin, then fire it in the real world.
- See it in Owllys
- Operations Graph25
- One connected data model of the business — orders, stock, machines, vehicles, suppliers, customers — that every Owllys agent reads and writes. The moat that compounds.
- See it in Owllys
- Decision Intelligence26
- Turning analytics into ranked, executable decisions with expected impact — not another dashboard to interpret.
- Predictive Maintenance27
- Using machine telemetry to predict failures before they stop the line, and scheduling the fix off-shift. OpsNest cuts unplanned downtime ~45% this way.
- See it in Owllys
- Demand Sensing28
- Short-horizon forecasting from live signals — sell-through, promotions, weather — rather than last year’s averages. The heart of PlanningNest.
- See it in Owllys
- Computer Vision29
- AI that understands images and video — face-based access, safety monitoring, artwork checks, OEE sensing on the press.
- Edge AI30
- Models running on or near the device — the gate, the vehicle, the machine — for sub-second decisions without a round trip to the cloud.
- See it in Owllys
- AI Copilot31
- An assistant embedded in a workflow that drafts, checks and recommends while a person stays in control. The step before full agentic automation.
- Autonomous Agent32
- 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.
- Evals (AI Evaluation)33
- Systematic testing of AI behaviour against known cases before and after deployment — the QA discipline that separates production AI from demos.
- AI Adoption34
- The organisational side of AI: governance, training, phased autonomy and measured outcomes. The reason Owllys plans deployment and adoption together.
- See it in Owllys
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