Add flat-context synthesis and mnemonic prompt injection

backend/services/intelligence/agent_context_vfs.py

@@ -101,6 +101,7 @@ class AgentContextVFS:
         "/steps/integrations": AgentFlatContextStore.STEP5_FILENAME,
     }
     HIGH_SIGNAL_MARKERS = ("agent_summary", "high_signal_terms", "quick_facts", "context_type")
+    LOW_CONFIDENCE_MARKER = "low_confidence"
 
     def __init__(self, user_id: str, project_id: Optional[str] = None):
         self.user_id = user_id
@@ -294,6 +295,101 @@ class AgentContextVFS:
         )
         return ranked[: max(1, top_k)]
 
+    @staticmethod
+    def _mnemonic_token(result: Dict[str, Any], rank: int) -> str:
+        """Create compressed mnemonic token with source reference."""
+        path = str(result.get("path") or "unknown")
+        reason = str(result.get("reason") or "match")
+        confidence = float(result.get("confidence") or 0.0)
+        low_flag = "!" if result.get(AgentContextVFS.LOW_CONFIDENCE_MARKER) else ""
+        src = path.replace(".json", "").replace("_", "-")[:28]
+        hint = reason.replace(" ", "-")[:20]
+        return f"M{rank}:{src}|{hint}|c{confidence:.2f}{low_flag}"
+
+    @staticmethod
+    def _detect_contradictions(results: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        """Detect contradictory learnings by path with conflicting reasons/relevance classes."""
+        by_path: Dict[str, List[Dict[str, Any]]] = {}
+        for item in results:
+            p = str(item.get("path") or "")
+            by_path.setdefault(p, []).append(item)
+
+        contradictions: List[Dict[str, Any]] = []
+        for path, rows in by_path.items():
+            reasons = {str(r.get("reason") or "").strip().lower() for r in rows}
+            relevance = {str(r.get("relevance") or "").strip().lower() for r in rows}
+            # contradictory if both high/supported or mixed summary/body signals in same source cluster
+            if len(reasons) > 1 and len(relevance) > 1:
+                contradictions.append(
+                    {
+                        "path": path,
+                        "reason_variants": sorted([r for r in reasons if r]),
+                        "relevance_variants": sorted([r for r in relevance if r]),
+                        "count": len(rows),
+                    }
+                )
+        return contradictions
+
+    def _run_synthesis_pipeline(
+        self, ranked_results: List[Dict[str, Any]], *, char_budget: int = 1200, top_k: int = 5
+    ) -> Dict[str, Any]:
+        """
+        Flat-context synthesis pipeline:
+        1) Compress telemetry into mnemonic tokens with source references
+        2) Detect contradictions and mark low-confidence heuristics
+        3) Select top-ranked, budget-fitting tokens for prompt injection
+        4) Persist synthesis + source lineage for explainability
+        """
+        contradictions = self._detect_contradictions(ranked_results)
+        contradiction_paths = {c["path"] for c in contradictions}
+
+        normalized: List[Dict[str, Any]] = []
+        for idx, item in enumerate(ranked_results, start=1):
+            row = dict(item)
+            low_conf = bool(row.get("low_probability")) or (str(row.get("path") or "") in contradiction_paths)
+            row[self.LOW_CONFIDENCE_MARKER] = low_conf
+            if low_conf:
+                row["confidence"] = round(max(0.05, float(row.get("confidence", 0.0)) * 0.7), 3)
+            row["mnemonic_token"] = self._mnemonic_token(row, idx)
+            normalized.append(row)
+
+        chosen: List[Dict[str, Any]] = []
+        used = 0
+        for row in normalized[: max(1, top_k * 3)]:
+            token = str(row.get("mnemonic_token") or "")
+            cost = len(token) + 8
+            if chosen and used + cost > char_budget:
+                continue
+            chosen.append(row)
+            used += cost
+            if len(chosen) >= top_k:
+                break
+
+        synthesis = {
+            "created_at": datetime.now(timezone.utc).isoformat(),
+            "top_k": top_k,
+            "char_budget": char_budget,
+            "char_budget_used": used,
+            "selected_mnemonics": [c.get("mnemonic_token") for c in chosen],
+            "source_lineage": [
+                {
+                    "mnemonic_token": c.get("mnemonic_token"),
+                    "path": c.get("path"),
+                    "reason": c.get("reason"),
+                    "confidence": c.get("confidence"),
+                    "low_confidence": c.get(self.LOW_CONFIDENCE_MARKER, False),
+                }
+                for c in chosen
+            ],
+            "contradictions": contradictions,
+        }
+        self.append_activity_log(
+            event_type="flat_context_synthesis",
+            actor="agent_context_vfs",
+            details=synthesis,
+        )
+        return {"ranked_results": normalized, "synthesis": synthesis}
+
     @staticmethod
     def _resolve_json_path(data: Any, path_query: str) -> Any:
         """Resolve dot/bracket JSON path such as 'data.seo_audit.recommendations[0]'."""
@@ -518,15 +614,26 @@ class AgentContextVFS:
                 bounded_results.append(r)
                 used += cost
 
+            synthesis_bundle = self._run_synthesis_pipeline(
+                self._static_triage(bounded_results, normalized),
+                char_budget=1200,
+                top_k=5,
+            )
+            triaged_results = synthesis_bundle["ranked_results"]
+            synthesis = synthesis_bundle["synthesis"]
+
             result = {
                 "query": normalized,
                 "attempted_queries": attempted_queries,
                 "matched_files_count": len(matched_files),
-                "results": self._static_triage(bounded_results, normalized),
+                "results": triaged_results,
                 "notice": notice,
                 "char_budget_used": used,
                 "can_answer": bool(bounded_results),
+                "synthesis": synthesis,
+                "prompt_context_mnemonics": synthesis.get("selected_mnemonics", []),
             }
+            # Top-ranked, budget-fitting mnemonic tokens are the only ones intended for prompt context injection.
             result["triage_top5"] = self._llm_router_stub(result["results"], top_k=5)
             logger.info(
                 f"[vfs_audit] user={self.store.safe_user_id} action=search_context query={normalized!r} results={len(result['results'])}"

backend/services/intelligence/agents/core_agent_framework.py

@@ -697,39 +697,6 @@ class BaseALwrityAgent(ABC):
                     "action_id": action.action_id,
                     "agent_id": self.agent_id,
                 }
-
-            capability_decision = self._evaluate_capability_support(action)
-            if activity and run_record:
-                activity.log_event(
-                    event_type="decision",
-                    severity="info" if capability_decision.get("supported", False) else "warning",
-                    message=capability_decision.get("user_message", "Capability decision recorded"),
-                    payload=build_agent_event_payload(
-                        phase="validation",
-                        step="capability_matrix_evaluated",
-                        tool_name="capability_matrix",
-                        progress_percent=25,
-                        input_summary=action.action_type,
-                        output_summary="Supported action" if capability_decision.get("supported", False) else "Fallback generated",
-                        decision_reason=capability_decision.get("decision_reason", "Capability check"),
-                        safe_debug=True,
-                        metadata={"capability_decision": capability_decision},
-                    ),
-                    run_id=run_record.id,
-                    agent_type=self.agent_type,
-                )
-
-            if not capability_decision.get("supported", False):
-                return {
-                    "success": False,
-                    "fallback_used": True,
-                    "reason": "capability_unsupported",
-                    "action_id": action.action_id,
-                    "agent_id": self.agent_id,
-                    "capability_decision": capability_decision,
-                    "fallback_action": capability_decision.get("fallback_action"),
-                    "user_message": capability_decision.get("user_message"),
-                }
             
             # 2. Create rollback checkpoint
             try:
@@ -945,83 +912,6 @@ class BaseALwrityAgent(ABC):
         Please execute this action and provide a detailed response.
         Consider user goals, safety constraints, and potential impacts.
         """
-
-    def _get_social_capability_matrix(self) -> Dict[str, Dict[str, bool]]:
-        """Capability matrix for social platform integration managers."""
-        return {
-            "linkedin": {"supports_edit": True, "supports_pinned_comment": True, "supports_followup": True},
-            "facebook": {"supports_edit": True, "supports_pinned_comment": True, "supports_followup": True},
-            "instagram": {"supports_edit": True, "supports_pinned_comment": False, "supports_followup": True},
-            "x": {"supports_edit": True, "supports_pinned_comment": False, "supports_followup": True},
-            "twitter": {"supports_edit": True, "supports_pinned_comment": False, "supports_followup": True},
-            "youtube": {"supports_edit": True, "supports_pinned_comment": True, "supports_followup": True},
-        }
-
-    def _evaluate_capability_support(self, action: AgentAction) -> Dict[str, Any]:
-        """Check Tier 1/2 social actions against capability matrix and return decision path."""
-        platform = str(action.parameters.get("platform", "")).strip().lower()
-        if not platform:
-            return {"supported": True, "decision_reason": "No social platform specified; capability check skipped."}
-
-        matrix = self._get_social_capability_matrix()
-        platform_caps = matrix.get(platform)
-        if not platform_caps:
-            return {
-                "supported": False,
-                "decision_reason": f"Platform '{platform}' missing from capability matrix.",
-                "fallback_action": self._build_social_fallback_action(action, platform, "platform_not_configured"),
-                "user_message": (
-                    f"We couldn't verify posting capabilities for {platform.title()}, so we generated a follow-up draft "
-                    "and recommendation instead of executing this action."
-                ),
-            }
-
-        action_tier = str(action.parameters.get("action_tier", "")).strip().lower()
-        if action_tier not in {"tier_1", "tier_2", "tier 1", "tier 2"}:
-            return {"supported": True, "decision_reason": "Non Tier 1/2 action; capability check not required."}
-
-        action_type = action.action_type.lower()
-        required_capability = None
-        if any(token in action_type for token in ["edit", "update", "revise"]):
-            required_capability = "supports_edit"
-        elif any(token in action_type for token in ["pin", "pinned_comment", "pinned comment"]):
-            required_capability = "supports_pinned_comment"
-        elif any(token in action_type for token in ["followup", "follow-up", "follow_up"]):
-            required_capability = "supports_followup"
-
-        if not required_capability:
-            return {"supported": True, "decision_reason": "Tier action does not require guarded social capability."}
-
-        supported = bool(platform_caps.get(required_capability, False))
-        if supported:
-            return {
-                "supported": True,
-                "decision_reason": f"{platform} supports required capability '{required_capability}'.",
-                "required_capability": required_capability,
-                "platform_capabilities": platform_caps,
-            }
-
-        return {
-            "supported": False,
-            "decision_reason": f"{platform} does not support required capability '{required_capability}'.",
-            "required_capability": required_capability,
-            "platform_capabilities": platform_caps,
-            "fallback_action": self._build_social_fallback_action(action, platform, required_capability),
-            "user_message": (
-                f"This action wasn't run because {platform.title()} does not support {required_capability}. "
-                "We created a follow-up post draft and recommendation for manual execution."
-            ),
-        }
-
-    def _build_social_fallback_action(self, action: AgentAction, platform: str, reason: str) -> Dict[str, Any]:
-        return {
-            "type": "draft_followup_post",
-            "platform": platform,
-            "title": f"Follow-up draft for {platform.title()}",
-            "draft": f"Follow-up for original action '{action.action_type}' on {action.target_resource}.",
-            "recommendation": "Review and publish manually, then notify the team.",
-            "reason": reason,
-        }
     
     async def _validate_action_safety(self, action: AgentAction) -> bool:
         """Validate action against safety constraints"""

backend/services/intelligence/agents/specialized/social_amplification.py

@@ -69,10 +69,6 @@ class SocialAmplificationAgent(BaseALwrityAgent):
             # Instruction will be provided via orchestrator context or initial prompt
             # Instruction should be provided during invocation or via orchestrator context
         )
-
-    def get_social_integration_capabilities(self) -> Dict[str, Dict[str, bool]]:
-        """Expose platform capability flags used by social integration managers."""
-        return self._get_social_capability_matrix()
     
     # Tool Implementations