fix(pipeline): ground local-agent system prompt with current date (#2399)

The local-agent runner's system prompt is a static string with no
template-variable support, so the model had no anchor for "today" and
resolved relative time references (e.g. "this quarter", "latest")
against whichever period was best represented in training data instead
of the real date, sometimes confidently answering with stale
information for time-sensitive questions.

PreProcessor now appends a short, deterministically-computed
"Current date: ..." note to the system prompt on every request for
local-agent pipelines, alongside guidance to verify time-sensitive
facts with a search tool rather than answering from memory. The
existing skill-awareness prompt injection is refactored to share the
same append-to-system-prompt helper.

src/langbot/pkg/pipeline/preproc/preproc.py

@@ -41,6 +41,29 @@ class PreProcessor(stage.PipelineStage):
         selected_tool_names = {tool for tool in selected_tools if isinstance(tool, str)}
         return [tool for tool in tools if tool.name in selected_tool_names]
 
+    @staticmethod
+    def _append_to_system_prompt(
+        messages: list[provider_message.Message],
+        addition: str,
+    ) -> None:
+        """Append text to the first system message, creating one if none exists.
+
+        Handles both plain-string and content-element (list) message bodies.
+        """
+        if messages and messages[0].role == 'system':
+            head = messages[0]
+            if isinstance(head.content, str):
+                head.content = head.content + addition
+            elif isinstance(head.content, list):
+                for ce in head.content:
+                    if getattr(ce, 'type', None) == 'text':
+                        ce.text = (ce.text or '') + addition
+                        break
+                else:
+                    head.content.append(provider_message.ContentElement(type='text', text=addition))
+        else:
+            messages.insert(0, provider_message.Message(role='system', content=addition.strip()))
+
     async def process(
         self,
         query: pipeline_query.Query,
@@ -275,6 +298,23 @@ class PreProcessor(stage.PipelineStage):
         query.prompt.messages = event_ctx.event.default_prompt
         query.messages = event_ctx.event.prompt
 
+        # =========== Current date grounding for the local-agent runner ===========
+        # local-agent system prompts are static strings with no template-variable
+        # support, so without an explicit anchor the LLM resolves relative time
+        # references (e.g. "this quarter", "latest", "currently") against whichever
+        # period is best represented in its training data instead of the real date,
+        # and won't reliably know to double check time-sensitive facts with a tool.
+        if selected_runner == 'local-agent':
+            date_addition = (
+                f'\n\nCurrent date: {datetime.datetime.now().strftime("%Y-%m-%d (%A)")}. '
+                'Resolve relative time references (e.g. "today", "this quarter", "latest", '
+                '"currently") based on this date, not your training cutoff. For anything '
+                'time-sensitive that may have changed since training — stock prices, '
+                'financial results, news, current events, exchange rates, or similar — '
+                'verify with a search tool if one is available rather than answering from memory.'
+            )
+            self._append_to_system_prompt(query.prompt.messages, date_addition)
+
         # =========== Skill awareness for the local-agent runner ===========
         # The actual activation goes through the ``activate`` Tool Call so the
         # LLM doesn't see full SKILL.md instructions until it commits to a
@@ -310,27 +350,7 @@ class PreProcessor(stage.PipelineStage):
                 bound_skills=bound_skills,
             )
             if skill_addition:
-                # Append to the first system message; create one if the
-                # prompt has none. Handles both plain-string and
-                # content-element (list) message bodies.
-                if query.prompt.messages and query.prompt.messages[0].role == 'system':
-                    head = query.prompt.messages[0]
-                    if isinstance(head.content, str):
-                        head.content = head.content + skill_addition
-                    elif isinstance(head.content, list):
-                        appended = False
-                        for ce in head.content:
-                            if getattr(ce, 'type', None) == 'text':
-                                ce.text = (ce.text or '') + skill_addition
-                                appended = True
-                                break
-                        if not appended:
-                            head.content.append(provider_message.ContentElement(type='text', text=skill_addition))
-                else:
-                    query.prompt.messages.insert(
-                        0,
-                        provider_message.Message(role='system', content=skill_addition.strip()),
-                    )
+                self._append_to_system_prompt(query.prompt.messages, skill_addition)
                 self.ap.logger.debug(
                     f'Skill index injected into system prompt: '
                     f'pipeline={query.pipeline_uuid} '