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PHASE 4 DIRECTIVE: THE INTEGRATION (THE ENGINE) You have the parts (Anonymizer, Regime Signals, Fact Checker). Now you must bolt them together into a Working Engine without it exploding.
The Fatal Bottleneck: JSON Compliance. You are about to feed complex prompts to models that love to yap. You need a Strict JSON Guardrail.
The Directive (@CodingAgent):
Enforce Schema: Implement a Pydantic parser for all Agent outputs.
If an Agent returns text, trigger a Retry Loop (Max 2 retries) with the error message: "You failed to output JSON. Fix format."
Hard Gating:
Connect the FactChecker to the Judge/Risk node in trading_graph.py.
Logic: If FactCheck.valid == False: REJECT_TRADE_IMMEDIATELY.
Do not allow "warnings." A hallucination is a disqualification.
Latency Budget:
Measure the time per step. If the FactChecker takes > 2.0s on average, you must switch the NLI model to ONNX runtime or quantize it.
Execute Phase 4. Bring me the main_workflow.py (or updated trading_graph.py) where these components actually talk to each other.
You have built a functioning engine.
Hard Gates: You actually implemented the "No Warning" policy. Good. A hallucination is a disqualification, not a suggestion.
Retry Logic: 2 retries is the sweet spot. If it can't fix JSON in 2 tries, the model is too dumb for the task.
Latency Monitoring: You are watching the clock. This makes it production-viable.
However, you left a "Landmine" in your error handling.
The Flaw: The "Null State" Crash. In your report, you wrote:
Python
if len(contradictions) > 0: return None, metrics # IMMEDIATE REJECTION The Risk: In a state machine (like LangGraph), returning Python None often breaks the graph execution flow or causes the next node to crash because it expects a State Dictionary, not NoneType. The Fix: Never return None. Return a "Dead State" object.
return {"signal": "NO_TRADE", "reason": "FACT_CHECK_FAILURE", ...}
Status: APPROVED. (Assuming you fix the Null return).