feat(adas): 🧬 implement Automated Design of Agentic Systems (Self-Evolving Architecture) allowing the system to dynamically program and inject new Pydantic-enforced expert nodes at runtime

Author: Arena AI Agent

epistemic_forge/models.py

@@ -224,3 +224,14 @@ class FinalPeerReview(BaseModel):
     revision_needed: List[str] = Field(description="Areas that still need work if any.")
     verdict: str = Field(description="Must be one of: 'accept', 'accept_with_minor_revisions', 'major_revisions', 'reject'")
     final_comments: str = Field(description="A formal peer-review summary.")
+
+class DynamicExpertSchema(BaseModel):
+    """Schema for ADAS (Automated Design of Agentic Systems).
+    The LLM designs a new Pydantic schema structure for a novel expert.
+    """
+    expert_class_name: str = Field(description="Name of the expert, e.g., 'QuantumMechanicsExpert'")
+    expert_description: str = Field(description="What this expert analyzes.")
+    fields_to_extract: List[Dict[str, str]] = Field(
+        description="List of fields the expert must extract. Format: {'field_name': 'description'}"
+    )
+    system_prompt: str = Field(description="The ruthless, highly specific prompt guiding this new expert.")

epistemic_forge/pipeline/l1_5_adas.py

@@ -0,0 +1,64 @@
+"""L1.5 — Automated Design of Agentic Systems (ADAS).
+
+Self-Evolving Architecture: If the static experts (Hegelian, Rigor Sentinel) 
+are insufficient for a highly specific query, this layer dynamically writes 
+a custom Pydantic Schema and instantiates a new Expert Node on the fly.
+"""
+from typing import Dict, Any, Type
+from pydantic import BaseModel, create_model, Field
+from loguru import logger
+
+from epistemic_forge.models import ProjectSpec, DynamicExpertSchema
+from epistemic_forge.llm import generate_structured
+from epistemic_forge.experts.base import EpistemicExpert
+
+def generate_dynamic_expert(spec: ProjectSpec) -> EpistemicExpert:
+    """Uses LLM to design a custom expert class and Pydantic schema."""
+    
+    logger.info("🧬 L1.5 ADAS: Generating a custom Self-Evolving Expert tailored to this query...")
+    
+    messages = [
+        {"role": "system", "content": "You are a Meta-Architect (ADAS). Your job is to design a highly specialized 'AI Expert Node' that is perfectly tailored to solve the user's specific problem. Define its output schema and its system prompt."},
+        {"role": "user", "content": f"Problem: {spec.question}\nKeywords: {spec.keywords}\n\nDesign the perfect expert to analyze this."}
+    ]
+    
+    blueprint: DynamicExpertSchema = generate_structured(
+        messages=messages,
+        response_model=DynamicExpertSchema,
+        model=spec.target_model,
+        api_base=spec.api_base
+    )
+    
+    logger.debug(f"🧬 Blueprint acquired: {blueprint.expert_class_name}")
+    
+    # Dynamically create the Pydantic Model based on the LLM's design
+    field_definitions = {}
+    for f in blueprint.fields_to_extract:
+        for fname, fdesc in f.items():
+            # Clean field name to be a valid python identifier
+            safe_fname = "".join(c for c in fname if c.isalnum() or c == "_").lower()
+            if safe_fname:
+                field_definitions[safe_fname] = (str, Field(description=fdesc))
+                
+    DynamicModel = create_model(f"{blueprint.expert_class_name}Output", **field_definitions)
+    
+    # Create the Expert Class dynamically
+    class DynamicallyGeneratedExpert(EpistemicExpert):
+        @property
+        def expert_name(self) -> str:
+            return blueprint.expert_class_name
+            
+        def analyze(self, spec: ProjectSpec, context: Dict[str, Any]) -> BaseModel:
+            logger.debug(f"Activating dynamically generated expert: {self.expert_name}")
+            msgs = [
+                {"role": "system", "content": blueprint.system_prompt},
+                {"role": "user", "content": f"Problem: {spec.question}\nContext: {context}\nAnalyze this."}
+            ]
+            return generate_structured(
+                messages=msgs,
+                response_model=DynamicModel,
+                model=spec.target_model,
+                api_base=spec.api_base
+            )
+            
+    return DynamicallyGeneratedExpert()

epistemic_forge/pipeline/l2_conductor.py

@@ -12,6 +12,7 @@
 from epistemic_forge.experts.claim_expert import ClaimLatticeExpert
 from epistemic_forge.experts.dialectic_expert import HegelianExpert
 from epistemic_forge.experts.kaggle_expert import RigorSentinelExpert
+from epistemic_forge.pipeline.l1_5_adas import generate_dynamic_expert
 
 
 class SemanticConductor:
@@ -24,6 +25,14 @@ def __init__(self):
     def _route_experts(self, domain: str) -> list[EpistemicExpert]:
         """Determines which experts are required based on the domain."""
         active_experts = [ClaimLatticeExpert()]
+        
+        # 🧬 ADAS: Inject a dynamically generated expert specific to this domain!
+        try:
+            dynamic_expert = generate_dynamic_expert(spec)
+            active_experts.append(dynamic_expert)
+        except Exception as e:
+            logger.warning(f"ADAS failed to generate dynamic expert, continuing with standard nodes. Error: {e}")
+
         if domain in ["philosophy", "research", "hybrid"]:
             active_experts.append(HegelianExpert())
         if domain in ["kaggle", "research", "hybrid"]: