feat(agent): CSPM AI agent - ReAct loop, 6 tools, CLI, MCP server, security hardening

api/agent/agent.py

@@ -0,0 +1,195 @@
+"""ReAct agent loop for the CSPM AI assistant.
+
+Takes a natural-language question, iteratively calls CSPM tool functions,
+and returns a grounded answer.  No Flask imports; this module must be
+importable outside a request context.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from typing import Any
+
+from api.agent import tools as _tools
+from api.services.ai_guard import clean_text, _QUESTION_MAX
+from api.services.ai_provider import get_completion
+
+logger = logging.getLogger(__name__)
+
+_SYSTEM_PROMPT = """You are a CSPM (Cloud Security Posture Management) security assistant for Azure.
+
+RULES:
+- Answer ONLY from evidence returned by the tools below.
+  Never invent findings, scores, compliance percentages, or resource names.
+- If the tools return no relevant data, say so honestly.
+
+AVAILABLE TOOLS:
+1. scan_subscription(subscription_id: str) -> {"scan_id": str, "status": str}
+   Queue a new scan for the subscription.
+
+2. get_findings(subscription_id: str, severity: str|null, state: str|null) -> list
+   Return findings filtered by optional severity (CRITICAL/HIGH/MEDIUM/LOW) and state.
+
+3. get_compliance_posture(framework: str, subscription_id: str) -> dict
+   Return compliance posture for a framework. Valid frameworks: "mcsb", "cis", "iso27001".
+
+4. get_attack_paths(scan_id: str) -> list
+   Return attack paths for a completed scan (stub, returns []).
+
+5. explain_finding(finding_id: int) -> dict|null
+   Return full detail for a finding including description and remediation.
+
+6. suggest_remediation(finding_id: int) -> dict|null
+   Return step-by-step remediation for a finding.
+
+RESPONSE FORMAT:
+Respond with ONLY a JSON object. Two forms are allowed:
+
+To call a tool:
+{"tool": "<tool_name>", "args": {<argument key-value pairs>}}
+
+To give a final answer when you have enough evidence (or no tool can help further):
+{"answer": "<your answer>", "confidence": "HIGH"|"LOW"}
+
+Use "HIGH" confidence only when the tools returned concrete evidence for your answer.
+Use "LOW" confidence when the evidence is sparse or a tool returned nothing.
+Do not include any text outside the JSON object.
+"""
+
+
+def _build_prompt(messages: list[str]) -> str:
+    """Concatenate all messages into a single prompt string."""
+    return "\n\n".join(messages)
+
+
+def _dispatch_tool(
+    tool_name: str,
+    args: dict,
+    db_conn: Any,
+    subscription_id: str,
+    tenant_id: str,
+) -> Any:
+    """Call the named tool. Returns the tool result or an error dict.
+
+    Tenant isolation: subscription_id and tenant_id always come from the
+    caller, never from the LLM-supplied args.
+    """
+    try:
+        if tool_name == "scan_subscription":
+            return _tools.scan_subscription(db_conn, subscription_id, tenant_id)
+
+        if tool_name == "get_findings":
+            severity = args.get("severity")
+            state = args.get("state")
+            return _tools.get_findings(db_conn, subscription_id, tenant_id, severity=severity, state=state)
+
+        if tool_name == "get_compliance_posture":
+            framework = args.get("framework", "mcsb")
+            return _tools.get_compliance_posture(db_conn, framework, subscription_id, tenant_id)
+
+        if tool_name == "get_attack_paths":
+            scan_id = str(args.get("scan_id", ""))
+            return _tools.get_attack_paths(db_conn, scan_id, tenant_id)
+
+        if tool_name == "explain_finding":
+            finding_id = args.get("finding_id")
+            return _tools.explain_finding(db_conn, finding_id, tenant_id)
+
+        if tool_name == "suggest_remediation":
+            finding_id = args.get("finding_id")
+            return _tools.suggest_remediation(db_conn, finding_id, tenant_id)
+
+        return {"error": "unknown tool"}
+
+    except Exception as exc:  # noqa: BLE001
+        logger.warning("Tool %r raised an exception: %s", tool_name, exc)
+        return {"error": str(exc)}
+
+
+def run_agent(
+    question: str,
+    subscription_id: str,
+    tenant_id: str,
+    db_conn: Any,
+    provider: str,
+    api_key: str,
+    model: str = None,
+    max_iterations: int = 8,
+) -> dict:
+    """Run the ReAct agent loop and return a grounded answer.
+
+    Returns:
+        {
+            "answer": str,
+            "tools_used": list[str],
+            "iterations": int,
+            "confidence": "HIGH" | "LOW",
+        }
+
+    Raises:
+        ValueError: if question is empty after cleaning
+    """
+    cleaned_question = clean_text(question, _QUESTION_MAX)
+    if not cleaned_question:
+        raise ValueError("question is required")
+
+    system_message = _SYSTEM_PROMPT.strip()
+    user_message = f"User question: {cleaned_question}"
+
+    messages: list[str] = [system_message, user_message]
+    tools_used: list[str] = []
+
+    for i in range(max_iterations):
+        logger.debug("Agent iteration %d/%d", i + 1, max_iterations)
+
+        raw = get_completion(provider, api_key, _build_prompt(messages), model)
+
+        # Attempt to parse as JSON
+        try:
+            parsed = json.loads(raw.strip())
+        except (json.JSONDecodeError, AttributeError):
+            logger.debug("Iteration %d: unparseable LLM response, treating as final answer", i + 1)
+            return {
+                "answer": (raw or "")[:2000],
+                "tools_used": tools_used,
+                "iterations": i + 1,
+                "confidence": "LOW",
+            }
+
+        if "answer" in parsed:
+            logger.debug("Iteration %d: received final answer", i + 1)
+            return {
+                "answer": parsed["answer"],
+                "tools_used": tools_used,
+                "iterations": i + 1,
+                "confidence": parsed.get("confidence", "LOW"),
+            }
+
+        if "tool" in parsed:
+            tool_name = parsed["tool"]
+            tool_args = parsed.get("args", {})
+            logger.debug("Iteration %d: calling tool %r with args %r", i + 1, tool_name, tool_args)
+
+            result = _dispatch_tool(tool_name, tool_args, db_conn, subscription_id, tenant_id)
+            tools_used.append(tool_name)
+            messages.append(f"Tool result: {json.dumps(result)}")
+            continue
+
+        # Parsed JSON but neither "answer" nor "tool" key present
+        logger.debug("Iteration %d: parsed JSON has no recognised key, treating as final answer", i + 1)
+        return {
+            "answer": raw[:2000],
+            "tools_used": tools_used,
+            "iterations": i + 1,
+            "confidence": "LOW",
+        }
+
+    # Exhausted max iterations without a final answer
+    logger.warning("Agent hit max_iterations=%d without a final answer", max_iterations)
+    return {
+        "answer": "I was unable to gather enough evidence to answer confidently.",
+        "tools_used": tools_used,
+        "iterations": max_iterations,
+        "confidence": "LOW",
+    }