The cost of prototypes is unbelievably low using AI.
Rapidly creating a prototype, especially with new or less well-known technology, is where I derive significant benefits from AI.
How to build application prototypes?
- Write /reverse prompt/Adjust instructions into md file
- Agentic AI (specialising in Doc Extraction) to extract and refine from md file
- Run using IDE-based copilot (VS Code with GitHub Copilot) (AmazonQ) (Cursor, Windsurf, Steamlit)
Thoughts: Developers are adjusting to using Ai to support software solutions. The developer role will continue the trend of making technical implementation more accessible, allowing knowledgeable IT engineers or domain experts to build faster and better than citizen/amateur developers. Ai assists in complex decisions!
What needs to improve?
- Knowledge is key. AI needs to have narrow expertise at the right time. i.e. only domain knowledge, not influenced by other data. Quality of input data used to train. Allows for dynamic reasoning.
- Session/long-term contact agreement/understanding to improve the understanding between your IDE and me. Remember how I prompt and provide feedback on how I digest information. Context between the human developer and Ai is Paramount.
- Control of IDE integration with coding copilots, clear return to the user developer to make better decisions. Context is Paramount.
- Governance & Data (Connectors, API's, code complex processes (MCP maybe), quality of data).
Retrieval Augmentation Generate (RAG)
Model Context Protocol (MCP)
MCP is a protocol (created by Anthropic) that allows an MCP client to connect to an MCP server, which in turn has specialist knowledge that the MCP server will expose. Authentication uses OAuth to secure access.
My Applications/Agents use the MCP to ask the MCP Server, 'What can you do?' so that they are aware of how to use the MCP Server.
The MCP Server, when built, informs the client of its capabilities and then performs actions such as updates using an API.
Summary: Use MCP to allow the client to talk to other resources/tools
Agents-to-agent (A2A)
A2A allows agents to work together. So two agents can leverage each other; the other agent solves the issue and returns the answer for the first agent to use. Whereas MCP allows any agent to speak to a source. Agents complete a task and give it back to the calling agent.
Summary: Use A2A to talk to specialised Agents, and the agent returns the calling agent's answers.
