AI for developers and Architects

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?

1. Write /reverse prompt/Adjust instructions into md file
2. Agentic AI (specialising in Doc Extraction) to extract and refine from md file
3. 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)

AI needs to be able to connect to my existing Tool Landscape:
I use Azure, C#, Playwright, and GitLab.  I want my IDE to work with these tools and many more.  MCP servers publish their functionality, and I can connect my Copilot/Agent to use multiple MCP servers.  This is what GHCP does for VS Code, allowing you to add MCP clients dynamically to use existing MCP Servers. 

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 provides specialist knowledge. Authentication should use OAuth to secure access. My Applications/Agents use the MCP to ask the MCP Server, 'What can you do?' so they know how to use it.  The MCP server allows you to interact with a system.  It is often referred to as the "Arms and Legs" of AI. The MCP Server, when built, informs the client of its capabilities and then performs actions, such as updates, via an API.   Summary: Use MCP to enable the client to communicate with other resources/tools. NB: An Agent can utilise multiple MCP Servers.

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.