Add capability-matrix checks and social action fallbacks

backend/services/intelligence/agent_context_vfs.py

@@ -101,7 +101,6 @@ 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
@@ -295,101 +294,6 @@ 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]'."""
@@ -614,26 +518,15 @@ 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": triaged_results,
+                "results": self._static_triage(bounded_results, normalized),
                 "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

@@ -697,6 +697,39 @@ 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:
@@ -912,6 +945,83 @@ 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

@@ -69,6 +69,10 @@ 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