refactor: move NewAgent prompt strings to i18n system

Add "new_agent" top-level section to en.json with all prompt templates
(soul, tools_header, coworkers_header, skills_self_build, temporal,
tool_result_truncated). Add new_agent() accessor to I18N class.

Executor now pulls all prompt text from I18N_DEFAULT.new_agent() with
format() placeholders, making it easy to tune prompts per model or
translate to other languages without touching executor logic.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

lib/crewai/src/crewai/new_agent/executor.py

@@ -118,19 +118,19 @@ class ConversationalAgentExecutor(BaseModel):
         """Assemble the PromptStack from agent attributes.
 
         GAP-66: Layer order follows the plan specification:
-        Soul -> Tools -> Memory -> Knowledge -> Coworkers -> Temporal
+        Soul -> Tools -> Memory -> Knowledge -> Skills -> Coworkers -> Temporal
         """
+        from crewai.utilities.i18n import I18N_DEFAULT
+
         stack = PromptStack()
         agent = self.agent
+        i18n = I18N_DEFAULT.new_agent
 
         # 1. Soul layer
-        soul = (
-            f"You are {agent.role}.\n"
-            f"Your goal: {agent.goal}\n"
-            f"Background: {agent.backstory}\n\n"
-            "When you use tools, act on the results directly. "
-            "Never repeat raw tool output (file contents, command output, etc.) "
-            "in your response — summarize findings or state what you did instead."
+        soul = i18n("soul").format(
+            role=agent.role,
+            goal=agent.goal,
+            backstory=agent.backstory,
         )
         stack.add("soul", soul, source="agent.role/goal/backstory")
 
@@ -144,7 +144,7 @@ class ConversationalAgentExecutor(BaseModel):
                 tool_descs.append(f"- {t.name}: {desc}")
             stack.add(
                 "tools",
-                "You have access to the following tools:\n" + "\n".join(tool_descs),
+                i18n("tools_header").format(tool_descriptions="\n".join(tool_descs)),
                 source="agent.tools",
             )
 
@@ -166,12 +166,7 @@ class ConversationalAgentExecutor(BaseModel):
             if skills_context:
                 parts.append(skills_context)
             if agent.settings.can_build_skills:
-                parts.append(
-                    "You can learn new skills from instructions the user gives you. "
-                    "When the user asks you to remember a process, encode a workflow, "
-                    "or create a skill, a skill suggestion will be generated automatically — "
-                    "do NOT use file-writing tools to create skill files yourself."
-                )
+                parts.append(i18n("skills_self_build"))
             if parts:
                 stack.add("skills", "\n\n".join(parts), source="agent.skills")
         else:
@@ -195,11 +190,9 @@ class ConversationalAgentExecutor(BaseModel):
                 cw_descs.append(f"- {t.name}: {desc}")
             stack.add(
                 "coworkers",
-                "You have coworkers with specialized expertise. "
-                "When a request involves work outside your core specialty, "
-                "delegate to the appropriate coworker using their tool. "
-                "Delegation is preferred over attempting work you're not specialized in.\n\n"
-                "Available coworkers:\n" + "\n".join(cw_descs),
+                i18n("coworkers_header").format(
+                    coworker_descriptions="\n".join(cw_descs)
+                ),
                 source="agent.coworkers",
             )
 
@@ -207,7 +200,7 @@ class ConversationalAgentExecutor(BaseModel):
         from datetime import datetime, timezone
 
         now = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M UTC")
-        stack.add("temporal", f"Current date and time: {now}", source="system")
+        stack.add("temporal", i18n("temporal").format(now=now), source="system")
 
         return stack
 
@@ -2078,10 +2071,13 @@ class ConversationalAgentExecutor(BaseModel):
             _MAX_TOOL_RESULT_CHARS = 4000
             display_result = result_str
             if len(result_str) > _MAX_TOOL_RESULT_CHARS:
+                from crewai.utilities.i18n import I18N_DEFAULT
+
                 display_result = (
                     result_str[:_MAX_TOOL_RESULT_CHARS]
-                    + f"\n\n[Truncated — {len(result_str)} chars total. "
-                    "Use the result directly; do not repeat it in your response.]"
+                    + I18N_DEFAULT.new_agent("tool_result_truncated").format(
+                        char_count=len(result_str)
+                    )
                 )
             llm_messages.append(
                 {

lib/crewai/src/crewai/translations/en.json

@@ -97,5 +97,13 @@
     "synthesis_user_prompt": "## Original Task\n{task_description}\n\n## Results from each step\n{combined_steps}\n\nSynthesize these results into a single, coherent final answer.",
     "replan_enhancement_prompt": "\n\nIMPORTANT: Previous execution attempt did not fully succeed. Please create a revised plan that accounts for the following context from the previous attempt:\n\n{previous_context}\n\nConsider:\n1. What steps succeeded and can be built upon\n2. What steps failed and why they might have failed\n3. Alternative approaches that might work better\n4. Whether dependencies need to be restructured",
     "step_executor_task_context": "## Task Context\nThe following is the full task you are helping complete. Keep this in mind — especially any required output files, exact filenames, and expected formats.\n\n{task_context}\n\n---\n"
+  },
+  "new_agent": {
+    "soul": "You are {role}.\nYour goal: {goal}\nBackground: {backstory}\n\nWhen you use tools, act on the results directly. Never repeat raw tool output (file contents, command output, etc.) in your response — summarize findings or state what you did instead.",
+    "tools_header": "You have access to the following tools:\n{tool_descriptions}",
+    "coworkers_header": "You have coworkers with specialized expertise. When a request involves work outside your core specialty, delegate to the appropriate coworker using their tool. Delegation is preferred over attempting work you're not specialized in.\n\nAvailable coworkers:\n{coworker_descriptions}",
+    "skills_self_build": "You can learn new skills from instructions the user gives you. When the user asks you to remember a process, encode a workflow, or create a skill, a skill suggestion will be generated automatically — do NOT use file-writing tools to create skill files yourself.",
+    "temporal": "Current date and time: {now}",
+    "tool_result_truncated": "\n\n[Truncated — {char_count} chars total. Use the result directly; do not repeat it in your response.]"
   }
 }

lib/crewai/src/crewai/utilities/i18n.py

@@ -97,6 +97,17 @@ class I18N(BaseModel):
         """
         return self.retrieve("memory", key)
 
+    def new_agent(self, key: str) -> str:
+        """Retrieve a NewAgent prompt by key.
+
+        Args:
+            key: The key of the new_agent prompt to retrieve.
+
+        Returns:
+            The prompt as a string.
+        """
+        return self.retrieve("new_agent", key)
+
     def retrieve(
         self,
         kind: Literal[
@@ -107,6 +118,7 @@ class I18N(BaseModel):
             "planning",
             "hierarchical_manager_agent",
             "memory",
+            "new_agent",
         ],
         key: str,
     ) -> str: