In this article
Executives do not need to become AI engineers to sponsor a strong implementation. They do need a vocabulary for accountability. When a vendor says its product includes agents, skills, MCP connections or computer use, the next question is what the feature changes in the organization's process, security model and cost.
Eight terms, translated into questions a buyer can use
Harness
The operating layer that supplies tools, constraints, context and verification around a model.
Ask the vendor: Ask who defines permissions and whether the system can demonstrate completed work.
Agent
A system that can choose or execute multiple steps with authorized tools.
Ask the vendor: Ask which decisions the software makes, which it proposes and which always require approval.
Skill
A reusable operating procedure, sometimes packaged with templates or code.
Ask the vendor: Ask whether a team can inspect, version and maintain the procedure.
MCP
A standard way for compatible AI applications to access exposed data resources and tools.
Ask the vendor: Ask what the connected service actually permits and how access can be revoked.
Loop engineering
The design of repeated actions, checks, retries and stopping behavior.
Ask the vendor: Ask how the workflow avoids duplicate sends, runaway costs and silently missed exceptions.
Computer use
AI interacting with software via the interface rather than only through application APIs.
Ask the vendor: Ask whether UI changes, authentication and high-impact clicks have safe recovery paths.
GEO and AEO
Overlapping practices for making reliable content more discoverable and useful in generative or direct answers.
Ask the vendor: Ask how citations, qualified visibility and conversions are independently measured.
AGI and superintelligence
Debated labels for broadly capable AI and hypothetical systems exceeding human intellectual performance.
Ask the vendor: Ask for proof of capability for the actual business process instead of relying on a label.
How the pieces fit together in a client reporting workflow
Start with a controlled analytics export and an approved set of metric definitions. A skill can specify how to draft the report. A connection, potentially using MCP, could retrieve approved records. An agent might decide which source to check next. The harness supplies the tool permissions, budget and verification. A feedback loop compares key totals with known results; if they disagree, the process stops. A human approves the exact final version before it leaves the organization.
That design can work without autonomous agents. A spreadsheet plus a deterministic script might be safer and cheaper when the reporting task is predictable. Use a model when interpreting approved material is valuable, not as a substitute for correct source data.
Put permission boundaries in the statement of work
- Identify the data controller, processing purpose and permitted systems.
- Separate reading data from editing records, sending messages and charging accounts.
- Require a human approver for externally visible or financially consequential actions.
- Define versioned acceptance tests, an execution log, retry limits and an escalation owner.
- Record model, tool and infrastructure costs beside manual review time.
GEO and AEO belong in the marketing plan—but need real measurement
A generative answer that cites a brand can create awareness without producing a lead. Keep verified website changes, search impressions, qualified inquiries and revenue events as distinct outcomes. Better descriptions, answer-ready pages and credible evidence improve the quality of your content; they do not guarantee placement in any AI system.
Translate this AI vendor proposal into a one-page approval checklist: [scope and claims]. Identify agents, skills, MCP connections, computer-use actions and feedback loops. Separate verified features from assumptions. Name required data access, approval events, failure tests, operating costs, and how the project will be evaluated against actual business outcomes.
Lead the implementation with evidence
Begin with an outcome and a small controlled pilot. If you need help diagnosing gaps across acquisition, lifecycle and delivery, explore Waymaker Agency's services. For accessible explanations and exercises, AI Teachers covers the underlying concepts.
Independent editorial discussion informed by Jim Clyde Monge's 2026 terminology article, Model Context Protocol documentation, OpenAI's harness engineering discussion and Anthropic's agent architecture guidance.
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