AI agents and generative AI that solve real tasks.

Generative AI suits text, image, code and unstructured-information tasks with clear inputs and reviewable outputs. Prioritise by volume, time, error consequence and data availability. A frequent low-risk task is usually a better first project than an occasional executive decision.

Measure released time, response speed, quality or correctly handled cases, including integration and review costs. Test representative and difficult examples with a manual safety net before broadening the workflow.

A chatbot converses, an automation follows predetermined steps, and an agent can interpret a goal, plan steps and use permitted tools. That flexibility requires narrow permissions, stop conditions, traceable tool use and human approval for publishing, spending or customer-facing decisions.

Combine approaches when useful: a chatbot gathers intent, an agent investigates, a rule checks permission and a person approves. Keep the architecture no more complex than the task demands.

Anthropic introduced Model Context Protocol in 2024 as an open standard connecting AI systems with tools and data. Google introduced Agent2Agent for collaboration between agents through discoverable capabilities and structured tasks.

MCP focuses on context and tools, while A2A focuses on agent collaboration. Neither replaces identity, permission or policy. Publish precise descriptions, protect personal data and risky actions, rate-limit public functions and return structured errors. See V7's own AI agent in operation on /en/agents.

Map every data source, destination, retention period and viewer. Apply data minimisation, appropriate processing agreements and least-privilege access. Human review must include enough context to change or reject an output.

Log model, tool and outcome without copying unnecessary personal data. The EU AI Act is being phased in, so maintain an inventory of purpose, supplier, risk and accountable owner, supported by appropriate legal advice.

List concrete tasks with frequency, systems, time and known errors. Prioritise one where digital inputs exist and mistakes can be caught before affecting customers or finances. Prototype with limited data and permissions, then compare against a human baseline.

Integrate only after testing failure cases. Add approval, logs, timeouts, rate limits and fallback behaviour. In controlled operation, measure quality, handling time, rejections and human corrections before adding more tools or autonomy.