One family weekend. Five ways AI can help. See the difference between getting suggestions, using your preferences, taking action, building something, and keeping the work on track.
AI becomes more useful with the right information, tools and support. Keep each addition simple and give it a clear purpose.
Years mark major product/application waves, not the invention date of each technique.
Ask in natural language and receive useful text, code, reasoning, or explanation back. The model can only work with what you place in the conversation.
AI gives better answers when it has the right details, such as your budget, schedule and preferences. Extra information only helps when it is relevant.
A workflow follows a path written by code. An agent chooses the next step from the evidence it observes.
Predictable and explicit. Excellent when the sequence is known in advance.
With access to tools, AI can search, compare options and choose what to do next. It stops when the task is done or it needs your decision.
A coding agent can create or change a website, check how it works and show you the result. You describe the page you want in everyday language.
Create the itinerary page Check the mobile layout Check links and dates
✓ Page ready to review
A harness is the support system around AI. It saves progress, checks results, limits what AI can do and helps it recover when something goes wrong.
The progression is not simply “newer model = smarter product.” Each system adds a different engineering capability.
Five short demos follow the same family weekend. Try each one to see what changes.
See how two systems handle the same interrupted weekend search.
Ready.
Ready.
MCP connects systems to tools and context. A2A connects agents to agents. Multi-agent architectures are useful when specialization or parallel work justifies their complexity — not simply because more agents sounds more advanced.
The architecture above is grounded in current engineering guidance, not a claim that each technique was invented in the year shown.