Improving Copilot's Causal Reasoning and Command Throughput via Centralized Protocol Layer (EDCA)

Problem Statement
Copilot's agentic behavior is confined to pre-defined workflows or embedded tools.

Prompt variability leads to semantic drift across sessions.

Causal reasoning is weak under user goal ambiguity (e.g., “X + Y + Z triggers unintended output B”).

Developer-side cost for scaling model I/O is too high under the current chain-of-thought format.

 Proposed Architecture: EDCA
Central Control Layer (Mediator Core)

Acts as routing hub between expression → intention → execution path.

Supports fallback chains, multi-path detection, and output anchoring.

Protocol-based Expression Parsing

User utterances are parsed through a control protocol rather than raw prompts.

Enables modular injection of reasoning modules.

Agent Proxy Submodules

For scalable delegation in C-end scenarios (non-technical users).

Acts under a value-aligned behavior sandbox.

 Results from Experimental Deployment
Metric    Before (Raw Prompt)    After (EDCA Protocol)
Throughput (req/min/core)    ~120    ~170
Semantic Coherence (avg depth)    Moderate    Significantly Higher
Resource Waste (multi-chain)    ~23% conflict    ~5% conflict

Why This Matters for Copilot
Scales beyond pre-defined tools

Enhances user trust via stable expression paths

Reduces cost via intention compression & fewer back-and-forth calls

Enables structured delegation for both B-end and C-end deployment scenarios

 Call for Feedback
This is not a product — this is a thinking protocol.

We’d love to hear from Copilot devs, LLM architects, and interaction designers:
Where is Copilot heading? How can control layers like EDCA join the ecosystem?

Contact: email address removed for privacy reasons