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Show HN: Inverting Agent Model (App as Clients, Chat as Server and Reflection)
by ddddazed
Hello HN. I’d like to start by saying that I am a developer who started this research project to challenge myself. I know standard protocols like MCP exist, but I wanted to explore a different path and have some fun creating a communication layer tailored specifically for desktop applications.
The project is designed to handle communication between desktop apps in an agentic manner, so the focus is strictly on this IPC layer (forget about HTTP API calls).
At the heart of RAIL (Remote Agent Invocation Layer) are two fundamental concepts. The names might sound scary, but remember this is a research project:
Memory Logic Injection + Reflection Paradigm shift: The Chat is the Server, and the Apps are the Clients.
Why this approach? The idea was to avoid creating huge wrappers or API endpoints just to call internal methods. Instead, the agent application passes its own instance to the SDK (e.g., RailEngine.Ignite(this)).
Here is the flow that I find fascinating:
-The App passes its instance to the RailEngine library running inside its own process.
-The Chat (Orchestrator) receives the manifest of available methods.The Model decides what to do and sends the command back via Named Pipe.
-The Trigger: The RailEngine inside the App receives the command and uses Reflection on the held instance to directly perform the .Invoke().
Essentially, I am injecting the "Agent Logic" directly into the application memory space via the SDK, allowing the Chat to pull the trigger on local methods remotely.
A note on the Repo: The GitHub repository has become large. The core focus is RailEngine and RailOrchestrator. You will find other connectors (C++, Python) that are frankly "trash code" or incomplete experiments. I forced RTTR in C++ to achieve reflection, but I'm not convinced by it. Please skip those; they aren't relevant to the architectural discussion.
I’d love to focus the discussion on memory-managed languages (like C#/.NET) and ask you:
-Architecture: Does this inverted architecture (Apps "dialing home" via IPC) make sense for local agents compared to the standard Server/API model?
-Performance: Regarding the use of Reflection for every call—would it be worth implementing a mechanism to cache methods as Delegates at startup? Or is the optimization irrelevant considering the latency of the LLM itself?
-Security: Since we are effectively bypassing the API layer, what would be a hypothetical security layer to prevent malicious use? (e.g., a capability manifest signed by the user?)
I would love to hear architectural comparisons and critiques.
The ergonomics of this are good in terms of integration mechanics. I wouldn't worry about performance as long as we are in the tens of milliseconds range on reflection/invoke.
I think the biggest concern is that the # of types & methods is going to be too vast for most practical projects. LLM agents fall apart beyond 10 tools or so. Think about the odds of picking the right method out of 10000+, even with strong bias toward the correct path. A lot of the AI integration pain is carefully conforming to the raw nature of the environment so that we don't overwhelm the token budget of the model (or our personal budgets).
I would consider exposing a set of ~3 generic tools like:
SearchTypes
GetTypeInfo
ExecuteScript
I would also consider schemes like attributes that explicitly opt-in methods and POCOs for agent inspection/use.
Sansung f05
Crafted by Rajat
Source Code