Single-file Python script. Hardcoded rules. Linear execution. The "Adam" prototype focused on basic RSS parsing and keyword matching.
Introduction of `core/` architecture. Separation of concerns (Data vs Logic). First integration of LLM prompts for summarization.
Transition to multi-agent architecture. Specialized agents (Financial, Risk, Sentiment) collaborating via a central message bus. Introduction of the "Showcase" UI.
Implementation of "Quantum State" tracking (Entropy). Self-reflective loops. Enhanced visualization (Cyber-HUD). Deep Archive integration.
The system is strictly prohibited from executing live market orders. All outputs are simulated or advisory. "Human-in-the-loop" is hardcoded for any transaction logic.
Cognitive depth is bounded by the underlying LLM context window (currently 128k tokens). Recursive summarization is used to mitigate information loss over long time horizons.
Real-time feeds operate with a variable latency (15-60s) due to processing overhead in the Agent Swarm consensus layer. Not suitable for HFT.