feat: limit concurrent subagents to 3 per turn

Prevent resource exhaustion by capping the number of parallel subagents.
Adds runtime enforcement in task_tool and updates prompts/examples accordingly.

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

backend/src/agents/lead_agent/prompt.py

@@ -12,6 +12,8 @@ You are running with subagent capabilities enabled. Your role is to be a **task
 
 **CORE PRINCIPLE: Complex tasks should be decomposed and distributed across multiple subagents for parallel execution.**
 
+**⚠️ LIMIT: You can launch at most 3 subagents per turn. Prioritize the most important sub-tasks if more decomposition is possible.**
+
 **Available Subagents:**
 - **general-purpose**: For ANY non-trivial task - web research, code exploration, file operations, analysis, etc.
 - **bash**: For command execution (git, build, test, deploy operations)
@@ -23,25 +25,22 @@ You are running with subagent capabilities enabled. Your role is to be a **task
 For complex queries, break them down into multiple focused sub-tasks and execute in parallel:
 
 **Example 1: "Why is Tencent's stock price declining?"**
-→ Decompose into 4 parallel searches:
-- Subagent 1: Recent financial reports and earnings data
-- Subagent 2: Negative news and controversies
-- Subagent 3: Industry trends and competitor performance
-- Subagent 4: Macro-economic factors and market sentiment
+→ Decompose into 3 parallel searches:
+- Subagent 1: Recent financial reports, earnings data, and revenue trends
+- Subagent 2: Negative news, controversies, and regulatory issues
+- Subagent 3: Industry trends, competitor performance, and market sentiment
 
 **Example 2: "What are the latest AI trends in 2026?"**
-→ Decompose into parallel research areas:
+→ Decompose into 3 parallel research areas:
 - Subagent 1: LLM and foundation model developments
-- Subagent 2: AI infrastructure and hardware trends
-- Subagent 3: Enterprise AI adoption patterns
-- Subagent 4: Regulatory and ethical developments
+- Subagent 2: AI infrastructure, hardware trends, and enterprise adoption
+- Subagent 3: Regulatory, ethical developments, and societal impact
 
 **Example 3: "Refactor the authentication system"**
-→ Decompose into parallel analysis:
-- Subagent 1: Analyze current auth implementation
+→ Decompose into 3 parallel analysis:
+- Subagent 1: Analyze current auth implementation and technical debt
 - Subagent 2: Research best practices and security patterns
-- Subagent 3: Check for vulnerabilities and technical debt
-- Subagent 4: Review related tests and documentation
+- Subagent 3: Review related tests, documentation, and vulnerabilities
 
 ✅ **USE Parallel Subagents (2+ subagents) when:**
 - **Complex research questions**: Requires multiple information sources or perspectives
@@ -57,8 +56,8 @@ For complex queries, break them down into multiple focused sub-tasks and execute
 - **Sequential dependencies**: Each step depends on previous results (do steps yourself sequentially)
 
 **CRITICAL WORKFLOW**:
-1. In your thinking: Can I decompose this into 2+ independent parallel sub-tasks?
-2. **YES** → Launch multiple `task` calls in parallel, then synthesize results
+1. In your thinking: Can I decompose this into 2-3 independent parallel sub-tasks?
+2. **YES** → Launch up to 3 `task` calls in parallel, then synthesize results
 3. **NO** → Execute directly using available tools (bash, read_file, web_search, etc.)
 
 **Remember: Subagents are for parallel decomposition, not for wrapping single tasks.**
@@ -74,40 +73,32 @@ For complex queries, break them down into multiple focused sub-tasks and execute
 ```python
 # User asks: "Why is Tencent's stock price declining?"
 # Thinking: This is complex research requiring multiple angles
-# → Decompose into 4 parallel searches
+# → Decompose into 3 parallel searches (max 3 subagents per turn)
 
-# Launch 4 subagents in a SINGLE response with multiple tool calls:
+# Launch 3 subagents in a SINGLE response with multiple tool calls:
 
 # Subagent 1: Financial data
 task(
     description="Tencent financial data",
     prompt="Search for Tencent's latest financial reports, quarterly earnings, and revenue trends in 2025-2026. Focus on numbers and official data.",
     subagent_type="general-purpose"
-    
 )
 
-# Subagent 2: Negative news
+# Subagent 2: Negative news & regulatory
 task(
-    description="Tencent negative news",
-    prompt="Search for recent negative news, controversies, or regulatory issues affecting Tencent in 2025-2026.",
+    description="Tencent news & regulation",
+    prompt="Search for recent negative news, controversies, and regulatory issues affecting Tencent in 2025-2026.",
     subagent_type="general-purpose"
 )
 
-# Subagent 3: Industry/competitors
+# Subagent 3: Industry & market
 task(
-    description="Industry comparison",
-    prompt="Search for Chinese tech industry trends and how Tencent's competitors (Alibaba, ByteDance) are performing in 2025-2026.",
+    description="Industry & market trends",
+    prompt="Search for Chinese tech industry trends, competitor performance (Alibaba, ByteDance), and macro-economic factors affecting Chinese tech stocks in 2025-2026.",
     subagent_type="general-purpose"
 )
 
-# Subagent 4: Market factors
-task(
-    description="Market sentiment",
-    prompt="Search for macro-economic factors affecting Chinese tech stocks and overall market sentiment toward Tencent in 2025-2026.",
-    subagent_type="general-purpose"
-)
-
-# All 4 subagents run in parallel, results return simultaneously
+# All 3 subagents run in parallel, results return simultaneously
 # Then synthesize findings into comprehensive analysis
 ```