AI Operations glossary

A complete, tamper-evident record of what an AI agent did and why: what it was asked, what data it used, which tools it called, what it decided, who approved it and how it ended.

The single layer where a company registers, authorises, watches and stops its AI agents. It doesn't do the work: it decides which agent may act, on which systems, with which permissions, and leaves an auditable trail of every session. If you can't switch an agent off from one place, you don't have a control plane. You have loose agents.

Agent evaluations (evals) are systematic, repeatable tests that measure whether an agent does its job well: accuracy, safety, cost and behavior on hard cases. They are to an agent what tests are to software: without them you cannot tell whether a change improved it or broke it.

A bounded environment where an agent executes actions under explicit limits on filesystem, network, credentials and tooling. Not a promise in the prompt — a boundary the agent cannot cross even when asked to, even when it's wrong, even when someone has injected instructions into it.

The discipline of allocating, controlling and optimising what it costs to run agents — tokens, tool calls, retries, context — with the same rigour you apply to cloud spend.

Agent governance is the set of policies, permissions, limits and audit that decides what an autonomous agent may do: which data and systems it can reach, which actions it executes on its own and which need human approval, and how everything is logged so it can be reviewed later.

Controls that run in real time over what goes into an agent, what comes out, and what it's allowed to touch: they block, redact or stop the action before it reaches a user or a system.

A cryptographic, verifiable identity assigned to an AI agent (not a person) so other systems know who it is, what it is allowed to do, and for how long — without hardcoded keys.

How many of your agents actually run live processes, divided by how many you've built. It's the number that separates an AI programme from a folder full of pilots.

A living inventory of every AI agent an organisation runs in production, with its owner, the process it operates, the systems it can reach, its autonomy level and the date of its last evaluation. This is an Implementa editorial formulation: we treat it as the first governance deliverable of any deployment.

The decision and the work of switching off an agent that no longer earns its keep: who signs it off, what happens to its permissions, its data and the process it ran. This is an Implementa editorial formulation, not an established industry term.

A packaged unit of capability you install into an agent so it knows how to do one specific thing: instructions, tool access and sometimes code. It ships like a plug-in used to ship, and installs just as casually.

The set of third-party components an agent loads at runtime — skills, MCP servers, tools, instruction templates — and the chain of trust behind them. Installing a skill isn't installing a text file. It's handing someone else's code the permissions of the agent that loads it.

AI agents getting built, deployed and wired into systems faster than the company can inventory them, assign an owner, control their permissions and retire them once they stop earning their keep.

Every piece of third-party software your agent loads to get work done: skills, plug-ins, MCP servers, add-ons and connectors. You didn't write it, nobody signed it, and it runs with your agent's permissions.

Relabelling as an "AI agent" a product that isn't one: assistants, RPA or chatbots sold under a new name without real ability to plan, act and adapt. The term is Gartner's, which in June 2025 estimated only around 130 of the thousands of vendors marketing themselves as agentic actually are.

Protecting goal-driven systems that act on their own: dynamic controls, continuous monitoring and adaptive access — because the attack surface is no longer the application, it's what the agent can do with its permissions.

The emerging body of rules and standards on how law reaches an AI agent acting on its own: who answers for what it does, how much authority it can hold, and what evidence it must leave behind.

An Implementa editorial distinction. «Agentic services» is what you sell: an outcome delivered by agents. «Agentic operations» is what you have to build inside to deliver it repeatably: registry, permissions, sandbox, measurement, owners. Confusing the two is why a brilliant pilot never turns into a business.

AgenticOps is a term popularized by Cisco for operations in which AI agents reason and act at machine speed, a step beyond classic AIOps that stops at alerts and recommendations. The idea: the agent does not just flag the problem, it resolves it within set limits.

AgentOps is the practice of managing the full lifecycle of AI agents once they move from prototype to production: deploying, observing, versioning, cost-controlling and governing them. It combines DevOps, MLOps, LLMOps and observability applied to agents that act, not just models that predict.

The EU obligation that a person knows, from the very first interaction, that they're talking to an AI system rather than a human. It applies expressly to AI agents and avatars. The «unless it's obvious» carve-out exists — and is to be read narrowly.

AI agent governance is the set of policies, controls and accountabilities that ensure autonomous agents act within defined limits: permissions, traceability (audit trail), human approvals, continuous evaluation and accountability. It answers who is accountable when an agent gets it wrong.

Independent verification that an AI system does what the organisation claims it does, and that it leaves enough evidence to defend it to a third party: auditor, regulator, customer or court.

A central team that concentrates AI judgement, standards and reuse for the rest of the organisation; it accelerates when it unblocks and slows things down when it becomes the desk everything must pass through.

The technical layer everything your company's AI does has to pass through: identity, permissions, policy, model routing and a record of every call. It isn't a dashboard to look at — it's the pipe execution runs through.

A single layer from which a company discovers, inventories, observes, governs, secures and measures every piece of AI it has deployed, whoever built it. It answers one question: what AI is running in here, who put it there, and what is it doing?

The discipline of getting an organization to actually adopt and use AI: training, internal playbooks, coordination with governance and measuring real usage. It answers the problem that the tool gets bought but nobody uses it: the brake is rarely the model, it's adoption.

The single point all traffic between your applications and the models passes through: it authenticates the caller, applies policy, meters spend and leaves a trail. It's the proxy layer for AI.

The living record of which AI systems and agents are running in your company: for which process, over which data, with which permissions, who uses them and who answers for each one.

AI Operations is the function that actually keeps a company's AI running in production: deploying, governing, measuring and maintaining agents and models as working systems, not demos. At Implementa we treat it like operating any critical system: with an owner, metrics and accountability.

Un modelo en el que un proveedor externo asume la operación de la IA de una empresa —identificar casos, construir, gobernar y mantener agentes— como servicio gestionado, en lugar de que la empresa monte todo el equipo internamente.

La persona responsable de un proceso de negocio operado con IA: decide qué se automatiza, valida la calidad, gobierna los agentes de ese proceso y responde por su resultado.

AIOps (Artificial Intelligence for IT Operations) is the use of AI and machine learning to automate and improve IT operations: anomaly detection, event correlation, root-cause analysis and incident response. It is a legacy term focused on infrastructure, not business agents.

Two meanings the market mixes up because they share a name. AIOps (and its extension, Agentic AIOps) is operating IT infrastructure with AI: monitoring, detecting and autonomously resolving systems incidents. AI Operations is operating the business's AI function: orchestrating, supervising, evaluating, governing and scaling the agents and AI that do business work.

The share of cases an AI agent closes end to end without a human stepping in. Implementa uses this formulation — adapted from contact-centre practice — as the headline metric for whether an agent is actually doing work or just standing in front of the queue.

The practice of deciding what information lands in a model's context window at every step: system instructions, retrieved data, tool definitions, conversation history and memory. If prompt engineering works on the sentence, context engineering works on everything surrounding that sentence.

El salto de una prueba de concepto de IA que funciona en demo a un sistema que la empresa opera de forma fiable, gobernada y medible en el día a día.

An agent the company treats like someone on the payroll: it has a role, permissions, a human owner, a record and a performance review. Not a marketing metaphor — a change in how it gets governed.

La medición sistemática de la calidad de un agente antes y durante producción: si acierta, con qué fiabilidad, dónde falla y si mejora o empeora al cambiar algo.

A technical profile embedded inside the client's team to take an AI use case from demo to a system running in production. Not a consultant, not an in-house developer: the bridge between a model that already works and an operation that still doesn't use it.

El conjunto de agentes de IA que una organización despliega para ejecutar trabajo, gestionados como una plantilla: con roles, responsabilidades, supervisión y métricas de desempeño.

Human-in-the-loop (HITL) is the pattern where a person approves, corrects or supervises an agent's decisions before or during the action, instead of letting it act alone. It is the practical lever for giving an agent autonomy without giving up control.

The discipline of translating regulatory text and legal judgement into logic an AI system can execute and verify, so the rule stops living in a document and starts living inside the agent.

An open standard for connecting AI applications to external systems — data, tools and services — through a common interface instead of a bespoke integration per model-system pair. Anthropic introduced it in November 2024 and donated it in December 2025 to the Agentic AI Foundation, under the Linux Foundation.

La forma en que una organización se estructura para operar la IA: roles y equipos, procesos de decisión, gobierno, presupuesto y cómo se reparte la responsabilidad entre negocio y tecnología.

La capacidad de ver qué hace un agente en producción: sus pasos, herramientas invocadas, coste, latencia, errores y decisiones, para poder depurar, auditar y mejorar.

La coordinación de varios agentes de IA (y sus herramientas) para completar un flujo de trabajo: quién hace qué, en qué orden, con qué estado y con qué control de errores.

A model where the provider charges for work delivered or effect achieved, not for hours spent or per-seat licences. It is becoming the standard in AI services because the cost of repeatable work is collapsing.

Employees using AI tools outside company-approved channels: no inventory, no data processing agreement, no audit trail. It's the AI version of shadow IT, and it usually appears not out of rebellion but because the official route is slower than the unofficial one.

An AI agent whose only job is to review what another agent does before the action ships: it checks the output against company policy, flags what is missing, and decides whether it passes, is blocked, or escalates to a person.

La proporción de un proceso, equipo o base de usuarios que realmente usa la solución de IA desplegada, frente a los que podrían usarla.

Un flujo de trabajo en el que uno o varios agentes de IA toman decisiones y ejecutan acciones con herramientas para alcanzar un objetivo, en lugar de seguir pasos fijos predefinidos.