8 AI Terms That Reveal What an AI Product Can Really Do

In this guide

1. The useful starting point
2. The eight terms that matter when choosing tools
3. Why the distinctions matter: an imaginary support workflow
4. Three common mistakes in AI tool comparisons
5. A simple evaluation card for any 'agent' product
6. Questions you might be asking
7. Sources and further reading

The useful starting point

MCP describes compatible tool connections; skills are reusable task procedures; agents may make and execute multi-step decisions; harnesses provide tools, controls and feedback. Judge a product by testable permissions and outcomes, not labels.

Many AI product pages now say they have 'agents,' 'skills' and 'connections.' Those labels may describe different layers of one system. For evaluating an app, the most useful question is not whether it uses fashionable terminology but what capability the term signifies.

The eight terms that matter when choosing tools

Skills

Reusable instructions and supporting resources for a specific job.

Buyer question: Ask whether the procedure requires connections or can operate on documents you provide.

MCP

A protocol for compatible AI applications to find and use approved tools or data resources.

Buyer question: Ask which tools are exposed, whether they can write and how access is revoked.

Agent

Software that may select steps and invoke tools toward a goal, rather than simply returning one response.

Buyer question: Ask for a trace of real actions and how a person can stop it.

Harness

The code and controls around a model that manage context, tools, policies and feedback.

Buyer question: Ask what is tested, logged, constrained and retried.

Computer use

AI operating graphical interfaces via clicks, typing and screen inspection.

Buyer question: Ask whether a direct supported integration would be safer and more reliable.

Loop engineering

Designing repeated action-feedback cycles, including limits, error recovery and completion criteria.

Buyer question: Ask what prevents duplicate work or an endless retry.

GEO / AEO

Related efforts to make authoritative pages useful in AI-generated answers and direct-response interfaces.

Buyer question: Ask how claims, citations and visibility are measured; never promise placement.

AGI / superintelligence

Debated terms for general-purpose and beyond-human intellectual capabilities.

Buyer question: Ask what specific demonstrated ability is actually relevant to your task.

Why the distinctions matter: an imaginary support workflow

Picture a team that wants to categorize incoming demo questions. A skill supplies the categorization instructions. A connected tool might retrieve permitted messages through MCP. An agent may decide which information to look up next. The harness controls which tools it can use and when it must stop. A loop can retry a failed retrieval, but a well-designed loop exits after a defined limit. Human reviewers approve any outgoing answer.

Computer use could navigate the help-desk interface if no suitable API exists, although interface automation is more prone to layout changes and ambiguous clicks. GEO and AEO concern whether the support content itself can be discovered and quoted in answer interfaces, not the routing of tickets.

Three common mistakes in AI tool comparisons

• Confusing connection with consent: An integration exists, but the tool still needs appropriate authorization and a bounded purpose.
• Confusing a saved procedure with an agent: Reusing instructions does not mean software is independently taking steps.
• Confusing an ambitious label with demonstrated results: Neither AGI claims nor impressive demos establish reliability for your specific workflow.

A simple evaluation card for any 'agent' product

Evaluate this AI application for [team task]. Identify which features are models, skills, MCP connections, agent behaviors or computer-use actions. Separate documented functionality from marketing language. Specify one safe test, evidence to collect, permissions and stop conditions.

For a deeper start, read our earlier RAG/MCP/agents explainer, then compare candidates with the tool trial planner. The useful superpower is knowing exactly what a product can and cannot do.

Based on independent synthesis of Jim Clyde Monge's October 2026 terminology roundup, official MCP documentation, OpenAI harness engineering and Anthropic agent design guidance.

Questions you might be asking

Are MCP and AI skills the same?

No. MCP is about exposing compatible tools or data; skills capture procedures for working with information and tools.

What is an AI harness?

The surrounding software that provides a model with tool use, context, constraints and checks, which influences how it completes tasks.

Is computer use safer than an API?

Not necessarily. UI actions can be brittle or ambiguous. Evaluate each route's permissions, reviewability and error handling.

Sources and further reading

• Jim Clyde Monge, 8 AI terms (2026)
• Official MCP documentation
• OpenAI on harness engineering
• Anthropic on effective agents

Sources checked October 16, 2026. Tool features and plan availability can change; review the linked publisher documentation before connecting an account.

Put one idea to work

Compare a tool on work you understand, measure review effort, and keep the result only when it improves the task.

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