Add LLM-backed planning agent (A4/A11/A13) — step 5a

- planner.py: LLMPlanningAgent offers exactly two tools, submit_plan
  and ask_human, so gating stays structural at the tool layer; no
  proceed tool exists. System prompt requires explicit, low-inference
  plans because a weaker model executes them (A13)
- Model output is appended to the canonical log as it arrives, before
  being interpreted, so a crash mid-planning leaves a truthful
  transcript
- A bad turn (prose instead of a tool call, malformed arguments, or a
  failed call) retries once, then raises PlanningError; the
  orchestrator gates to AWAIT_INPUT rather than inventing a plan
- Orchestrator: appends every operator message, plan_submitted, and
  gate_opened to the transcript, so re-planning sees rejection
  feedback as conversation rather than out-of-band parameters; pins
  the A11 role mapping at task start
- llm.py: non-JSON or non-object tool arguments now raise LLMError
  instead of escaping as JSONDecodeError — observed from llama3.2,
  which emits truncated argument strings
- Live planning test is opt-in on NISSE_LIVE_PLANNER_MODEL: planning
  escalates unconditionally (A12) and the local model does not hold
  the tool-call contract, so it awaits a frontier route

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

nisse-dev/src/nisse_dev/llm.py

@@ -131,11 +131,26 @@ class LiteLLMClient:
         # gets a locally-minted canonical ID; the provider's is dropped, since
         # every future request is rendered wholly from the canonical log.
         for call in message.get("tool_calls") or []:
+            raw = call["function"]["arguments"] or "{}"
+            # Weak models emit truncated or non-JSON argument strings; that is
+            # a bad model turn, not a client crash (A15).
+            try:
+                arguments = json.loads(raw)
+            except json.JSONDecodeError as exc:
+                raise LLMError(
+                    f"tool call {call['function']['name']!r} had non-JSON "
+                    f"arguments: {raw[:200]!r}"
+                ) from exc
+            if not isinstance(arguments, dict):
+                raise LLMError(
+                    f"tool call {call['function']['name']!r} arguments were "
+                    f"{type(arguments).__name__}, expected object"
+                )
             out.append(
                 ToolCall(
                     call_id=mint_call_id(),
                     tool=call["function"]["name"],
-                    arguments=json.loads(call["function"]["arguments"] or "{}"),
+                    arguments=arguments,
                 )
             )
         return out