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AI Agents Development Roadmap

Overview​

This roadmap outlines the evolution of Bike4Mind's AI agent capabilities from the current foundation to advanced semi-autonomous agents with human-in-the-loop workflows. The development is structured in three phases, building incrementally on existing infrastructure.

Current State Assessment​

✅ Already Implemented​

  • Multi-LLM Integration: OpenAI, Anthropic, Bedrock, Gemini support
  • MCP Protocol: Standardized tool calling infrastructure
  • Quest System: Basic AI task execution with tracking
  • Queue-Based Processing: Async AI processing with error handling
  • WebSocket Communication: Real-time updates and notifications
  • Rich Data Models: AgentModel, QuestModel, SessionModel foundations
  • Permission System: CASL-based authorization for scoped actions

✅ COMPLETED FEATURES​

Phase 1: Data Model & API Infrastructure ✅ COMPLETE​

All core data models and API endpoints are fully implemented and working

✅ Database Schema Updates​

  • Session Model: Added agentIds: [{ type: String, required: false }] field
  • Quest Model: Added agentIds: { type: [String], required: false } field
  • Agent Model: Complete with personality, visual, capabilities, triggerWords
  • Repository Methods: Full CRUD operations with agent attachment/detachment

✅ API Endpoints - All Working​

  • GET /api/sessions/[id]/agents - Get attached agents with avatar URL refresh
  • POST /api/sessions/[id]/agents - Attach agent with permission validation
  • DELETE /api/sessions/[id]/agents - Detach agent from session
  • GET /api/agents - Search and filter agents with pagination
  • POST /api/agents - Create new agents with credit management
  • PUT /api/agents/[id] - Update existing agents
  • DELETE /api/agents/[id]/delete - Delete agents
  • POST /api/agents/[id]/generate-description - AI-powered description generation

✅ Repository Methods - All Implemented​

  • attachAgent(sessionId, agentId) - Uses MongoDB $addToSet to prevent duplicates
  • detachAgent(sessionId, agentId) - Uses MongoDB $pull to remove agents
  • getAttachedAgents(sessionId) - Returns array of attached agent IDs
  • findByTriggerWords(triggerWords, userId) - Find agents by @mentions
  • searchAccessible(userId, search, filters, pagination) - Advanced agent search

Phase 2: Client-Side Agent Detection ✅ COMPLETE​

All agent mention detection and auto-attachment features are working

✅ Agent Mention Utilities - All Working​

  • parseAgentMentions(): Extracts @mentions from message text using regex
  • findMatchingAgents(): Matches mentions to agents by name or triggerWords
  • hasAgentMentions(): Quick check for @mentions in text
  • detectAgentMentions(): Case-insensitive mention detection in SessionBottom

✅ Auto-Attachment Logic - Fully Implemented​

  • SessionBottom: Auto-detects @mentions and attaches agents before sending
  • SessionsContext: Processes mentions in addMessageToSession
  • AgentDetectionFeature: Server-side processing of attached agents
  • Real-time Updates: Query invalidation and cache refresh on attachment

Phase 3: AgentBench Component ✅ COMPLETE​

Full UI component with collapse/expand functionality matching WorkBench

✅ AgentBench UI - Fully Implemented​

  • Agent Chips: Orange/warning color scheme with avatars and names
  • Remove Functionality: X buttons to detach agents from session
  • Responsive Design: Handles overflow with ellipsis for long names
  • Tooltips: Helpful hover text for all interactions
  • Auto-Show: Expands when agents are attached to session

✅ CollapsedAgentBench - Working​

  • Smart Display: Shows individual avatars for 1-2 agents, robot icon + count for 3+
  • Smooth Animations: Fade-in effects and hover transforms
  • Click to Expand: Toggles full AgentBench visibility
  • Badge System: Count badge for multiple agents

✅ Integration with SessionBottom - Complete​

  • Responsive Layout: Works in both mobile and desktop layouts
  • State Management: Proper collapse/expand state handling
  • Auto-Expand: Shows AgentBench when agents are attached
  • Query Integration: Real-time updates via React Query

Phase 4: Agent Attachment Logic ✅ COMPLETE​

All attachment/detachment workflows are fully functional

✅ Agents Sidebar - Fully Working​

  • Click-to-Attach: Toggle agents on/off by clicking in sidebar
  • Visual Indicators: CheckCircle icon shows attached status
  • Search & Filter: Find agents by name or trigger words
  • Permission Handling: Only shows accessible agents (owned or public)
  • Real-time Updates: Immediate UI updates on attach/detach

✅ Session State Management - Complete​

  • Persistent Storage: Agent attachments saved to database
  • State Synchronization: Session context updates immediately
  • Query Invalidation: Automatic cache refresh on changes
  • Error Recovery: Graceful handling of failed operations

✅ Multi-Channel Attachment - Working​

  • @Mention Auto-Attach: Automatically attach mentioned agents
  • Manual Sidebar Attach: Click to attach from agents list
  • AgentBench Detach: Remove via X buttons on agent chips
  • Session Persistence: Attachments survive page refresh

Phase 5: Agent Personality System ✅ COMPLETE​

Rich personality-driven agent responses with full metadata integration

✅ Personality Generation System - Fully Implemented​

  • 20 Major Motivations: Explorer, Builder, Achiever, etc.
  • 20 Minor Motivations: Secondary personality traits
  • 20 Character Flaws: Stubborn, Overly Analytical, Risk-Averse, etc.
  • 20 Unique Quirks: Fond of Puns, Collector, Talks to Itself, etc.
  • 7 Response Styles: formal, casual, technical, friendly, etc.
  • 20 Special Behaviors: Capability-based behaviors
  • D20 Roll Simulation: Math.random() based personality generation
  • Smart Generation: Ensures minor motivation differs from major
  • Auto-Description: Generates personality descriptions automatically

✅ Agent Identity & Pronouns System - Complete​

  • Gender Identity: 7 options including non-binary, genderfluid, etc.
  • Comprehensive Pronouns: Subject, object, possessive, possessive adjective, reflexive
  • Custom Pronouns: Free text field for any format (ze/zir/zirs, etc.)
  • Quick Preset Buttons: Common pronoun sets (they/them, he/him, she/her, xe/xem)
  • Identity Display: Shows gender and pronouns in agent detail view

✅ Agent Response System - Revolutionary Implementation​

  • Personality-Driven Responses: Agents respond according to their unique personality traits
  • Rich System Prompts: Uses all agent metadata (name, description, personality, capabilities, identity)
  • Single Agent Mode: Individual personality-driven system prompt
  • Multi-Agent Collaboration: Collaborative system prompt combining all agent personalities
  • Metadata Integration: Leverages motivations, quirks, flaws, response styles, pronouns
  • Graceful Fallbacks: Works even with minimal agent metadata

✅ Agent Attribution Display - Complete​

  • Visual Attribution: Shows which agents influenced each response
  • Agent Chips: Displays agent avatars, names, and descriptions
  • Single vs Collaborative: "Agent Response:" vs "Collaborative Response:" labels
  • Efficient Loading: React Query caching for agent data
  • Tooltip Details: Hover to see agent descriptions
  • Graceful Handling: Manages missing agents or network errors

✅ AI-Powered Description Generation - Working​

  • Generate Button: Optional AI generation for agent descriptions
  • Metadata Integration: Uses all agent data (personality, capabilities, identity)
  • Rich Context: Builds comprehensive prompts from agent characteristics
  • Fallback Support: Works even when some metadata is missing
  • Credit Management: TODO comment for future credit charging

Phase 6: Enhanced Agent Creation Experience ✅ COMPLETE​

Comprehensive agent creation and editing with personality randomization

✅ Personality Randomization - Fully Implemented​

  • "Roll Character" Button: Complete personality randomization with dramatic effects
  • Individual Field Dice: Targeted randomization for specific personality traits
  • Shimmer Effects: Golden gradients, sparkles, and glow borders (600ms animation)
  • Visual Feedback: "✨ Rolled!" indicator when personality is randomized
  • Smart Validation: Ensures minor motivation differs from major

✅ UI/UX Enhancements - Complete​

  • Compact Layout: Consolidated multiple sections into fewer cards
  • Streamlined Forms: Efficient trigger words, identity, and capabilities management
  • Visual Polish: Removed section headers for cleaner appearance
  • Responsive Design: Works across desktop and mobile layouts
  • Real-time Validation: Immediate feedback on form state

✅ Agent Portrait Modal System - Complete​

  • Clickable Avatars: Tap any agent portrait to view in full-screen modal
  • Beautiful Modal Design: Elegant overlay with agent details and large portrait
  • Responsive Integration: Works seamlessly in agent lists and detail views
  • Hover Effects: Smooth scaling animations on avatar interaction
  • Reusable Component: AgentPortraitModal used across multiple views

Phase 7: WorkBench Agent Integration ✅ COMPLETE​

Revolutionary pre-session agent management matching WorkBench file pattern

✅ WorkBench Agent State Management - Fully Implemented​

  • workBenchAgents State: Added to SessionsContext matching workBenchFiles pattern
  • Local Agent Management: Agents can be attached before session creation
  • Persistent Agent Attachment: Agents survive session creation and remain attached
  • State Synchronization: Perfect integration with existing session management

✅ Session Creation with Agents - Complete​

  • agentIds Parameter: Added to session creation schema and API endpoints
  • Automatic Agent Attachment: workBenchAgents automatically attached during session creation
  • No Manual Attachment: Eliminated complex post-creation agent attachment logic
  • Clean State Management: workBenchAgents cleared after successful session creation

✅ Agent Persistence Fix - Major Breakthrough​

  • Root Cause Identified: Agents were being lost because session creation didn't include them
  • Complete Flow Redesign: Updated entire session creation pipeline to handle agents
  • Database Schema Updates: Added agentIds to createSessionParametersSchema
  • API Layer Updates: Updated generateNewSession and useCreateNewSession hooks
  • Analytics Integration: Added agentIds to all session analytics events

✅ UI Component Updates - Seamless Integration​

  • AgentBench Dual Mode: Handles both session agents and workBench agents
  • Agents Sidebar: Smart switching between session and workBench agent management
  • SessionBottom Integration: Automatic workBench agent clearing after session creation
  • Visual Feedback: Clear indication of agent status in all UI states

✅ Code Quality Improvements - Complete​

  • ESLint Warning Fixes: Removed unnecessary dependencies from useCallback hooks
  • Type Safety: Full TypeScript integration for all agent-related state
  • Error Handling: Graceful fallbacks for all agent operations
  • Performance Optimization: Efficient state updates and query invalidation

🚀 NEXT PHASE: Advanced Agent Capabilities​

Current TODO List (Re-Groomed Based on Completed Work)​

Phase 8: Agent Memory & Context Integration​

Priority: High - Enable agents to remember and learn from interactions

  • Agent-Specific Memory: Connect agents to existing Memento system
  • Conversation History: Store and retrieve agent-specific conversation history
  • Learning from Interactions: Enable agents to improve based on past conversations
  • Context Awareness: Agents remember user preferences and previous discussions
  • Memory Consolidation: Periodic summarization of agent experiences

Phase 9: Project Context & Knowledge Integration​

Priority: High - Connect agents to project-specific knowledge and context

  • Project Context Loading: Agents access project-specific files and context
  • Knowledge Base Integration: Connect agents to project knowledge bases
  • File Context Awareness: Agents understand and reference project files
  • Dynamic Context Updates: Real-time updates when project context changes
  • Scoped Knowledge: Agents only access knowledge they're authorized to see

Phase 10: Tool Integration & Dynamic Capabilities​

Priority: Medium - Enable agents to use tools and expand capabilities

  • MCP Tool Integration: Agents can dynamically use MCP tools
  • Tool Permission Management: Control which tools each agent can access
  • Dynamic Tool Discovery: Agents discover and register new tools
  • Tool Usage Learning: Agents improve tool usage over time
  • Tool Coordination: Multiple agents coordinate tool usage

Phase 11: Advanced Multi-Agent Coordination​

Priority: Medium - Sophisticated agent collaboration and handoffs

  • Agent-to-Agent Communication: Direct messaging between agents
  • Task Delegation: Agents hand off tasks to specialized agents
  • Collaborative Problem Solving: Multiple agents work together on complex tasks
  • Agent Conflict Resolution: Handle disagreements between agents
  • Swarm Coordination: Coordinate multiple agents on large tasks

Phase 12: Confidence Scoring & Human-in-Loop​

Priority: Medium - Intelligent decision-making with human oversight

  • Confidence Assessment: Agents assess their confidence in responses
  • Human-in-Loop Triggers: Request human input when confidence is low
  • Approval Workflows: User approval for high-impact agent actions
  • Escalation Patterns: Automatic escalation for complex decisions
  • Learning from Feedback: Improve confidence scoring based on outcomes

Phase 13: Agent Analytics & Management​

Priority: Low - Monitoring, optimization, and bulk management

  • Agent Performance Dashboard: Monitor agent effectiveness and usage
  • Usage Analytics: Track which agents are most/least effective
  • Response Quality Metrics: Measure and improve agent response quality
  • Agent Health Monitoring: Detect and resolve agent issues
  • Bulk Agent Management: Manage multiple agents efficiently

🎯 IMMEDIATE NEXT STEPS​

  1. Agent Memory Integration (Phase 8)

    • Connect agents to existing Memento system for conversation history
    • Enable agents to remember user preferences and past interactions
    • Implement agent-specific memory consolidation

  2. Project Context Integration (Phase 9)

    • Load project-specific context and files for agents
    • Connect agents to project knowledge bases
    • Implement scoped knowledge access

  3. Tool Integration (Phase 10)

    • Allow agents to use existing MCP tools
    • Implement tool permission management
    • Enable dynamic tool discovery

  4. Advanced Multi-Agent Features (Phase 11)

    • Agent-to-agent communication
    • Task delegation and handoffs
    • Collaborative problem solving

🌅 TOMORROW'S PRIORITY: Agent Memory Integration​

Phase 8 Focus Areas for Next Session:​

🎯 Primary Goal: Connect Agents to Memento System​

  • Investigate Current Memento Implementation: Understand how memories are stored and retrieved
  • Design Agent-Specific Memory Schema: Extend Memento to include agent context
  • Implement Memory Retrieval: Agents can access relevant conversation history
  • Test Memory Integration: Verify agents can reference past interactions

🔧 Technical Tasks:​

  1. Explore Memento Codebase: Understand current memory storage patterns
  2. Design Agent Memory Schema: Add agent-specific fields to memory records
  3. Create Memory Retrieval API: Endpoints for agent-specific memory queries
  4. Update Agent Response System: Include relevant memories in system prompts
  5. Add Memory Management UI: Allow users to view/manage agent memories

🎨 User Experience Goals:​

  • Agents remember previous conversations with users
  • Agents can reference past decisions and preferences
  • Users can see what agents remember about them
  • Memory enhances agent personality consistency over time

🧪 Success Criteria:​

  • Agent can say "Remember when we discussed X last week?"
  • Agent responses show continuity across sessions
  • Memory retrieval is fast and contextually relevant
  • Users feel agents truly "know" them over time

This represents the next major leap in agent intelligence - moving from stateless personality-driven responses to memory-enhanced, relationship-building AI companions!

🎉 MAJOR ACCOMPLISHMENTS​

What We've Built​

Bike4Mind now has a revolutionary agent personality system that goes far beyond simple chatbots:

✅ Rich Personality Framework: 20+ personality dimensions with D20-based generation
✅ Identity-Aware Agents: Full gender identity and pronoun support
✅ Personality-Driven Responses: Agents respond with unique voices and characteristics
✅ Visual Attribution: Users can see which agents influenced each response
✅ Multi-Agent Collaboration: Multiple agents can work together on responses
✅ AI-Powered Creation: Automatic description generation from personality metadata
✅ Comprehensive UI: Full creation, editing, and management interface
✅ WorkBench Integration: Seamless pre-session agent management matching file patterns
✅ Agent Persistence: Agents properly survive session creation and remain attached
✅ Portrait Modal System: Beautiful full-screen agent portrait viewing experience

🚀 TODAY'S BREAKTHROUGH: Agent Persistence Fixed!​

The major issue where agents were being dropped from sessions has been completely resolved:

  • Root Cause: Session creation wasn't including agents from workBench state
  • Solution: Complete redesign of session creation pipeline to include agentIds
  • Impact: Agents now persist through session creation and properly influence responses
  • Architecture: Elegant workBenchAgents pattern matching workBenchFiles design
  • User Experience: Seamless agent attachment before and after session creation

Technical Foundation Status​

✅ Solid Foundation Complete​

  • Database Models: All agent, session, and quest models with personality support
  • API Layer: Complete REST API with description generation and agent persistence
  • UI Components: Full AgentBench, creation, editing, and attribution display
  • State Management: React Query integration with real-time updates
  • Personality System: Rich, randomizable personality framework
  • Response Integration: Agents influence LLM responses through system prompts
  • Visual Attribution: Clear display of which agents contributed to responses
  • WorkBench Integration: Pre-session agent management with persistent attachment
  • Session Creation: Agents automatically attached during session creation
  • Portrait System: Beautiful modal viewing for agent portraits

🔧 Ready for Next Phase​

  • Memory System: Memento system exists, needs agent-specific implementation
  • MCP Integration: Tool calling infrastructure exists, needs agent integration
  • Project Context: Project system exists, needs agent-specific context loading
  • Queue System: SQS processing ready for advanced agent workflows
  • EventBridge: Event system ready for agent scheduling and coordination

This roadmap shows that Bike4Mind has successfully completed a comprehensive agent personality system that allows users to create, customize, and interact with AI agents that have distinct personalities, identities, and response styles. The foundation is now solid for building advanced agent capabilities like memory, tool usage, and multi-agent coordination.

🚨 Immediate Safeguards (Implement These First)​

1. Credit Circuit Breakers ⭐⭐⭐⭐⭐​

interface AgentExecutionContext {
  maxCreditsPerExecution: number;
  maxCreditsPerHour: number;
  maxCreditsPerDay: number;
  currentHourlySpend: number;
  currentDailySpend: number;
}

async function executeAgent(agent: IAgent, context: AgentExecutionContext) {
  // Hard stop if agent has burned through daily allowance
  if (context.currentDailySpend >= context.maxCreditsPerDay) {
    throw new Error(`Agent ${agent.name} hit daily credit limit`);
  }
  
  // Hard stop if approaching hourly limit
  if (context.currentHourlySpend >= context.maxCreditsPerHour) {
    throw new Error(`Agent ${agent.name} hit hourly credit limit`);
  }
  
  // Execute with remaining credit cap
  const remainingCredits = Math.min(
    context.maxCreditsPerExecution,
    context.maxCreditsPerDay - context.currentDailySpend
  );
  
  return await executeWithCreditLimit(agent, remainingCredits);
}

2. Agent Interaction Throttling​

// Prevent Agent A -> Agent B -> Agent A loops
interface AgentCallStack {
  agentId: string;
  depth: number;
  parentAgentId?: string;
  callChain: string[]; // Track the full chain
}

const MAX_AGENT_DEPTH = 3; // Agents can only call 3 levels deep
const MAX_SAME_AGENT_IN_CHAIN = 1; // Agent can only appear once in call chain

function validateAgentCall(callStack: AgentCallStack): boolean {
  if (callStack.depth > MAX_AGENT_DEPTH) {
    throw new Error(`Agent call depth exceeded: ${callStack.depth}`);
  }
  
  const agentCallCount = callStack.callChain.filter(id => id === callStack.agentId).length;
  if (agentCallCount > MAX_SAME_AGENT_IN_CHAIN) {
    throw new Error(`Agent ${callStack.agentId} called recursively`);
  }
  
  return true;
}

3. EventBridge Rate Limiting​

// Add to your EventBridge handlers
const agentExecutionTracker = new Map<string, {
  count: number;
  lastReset: Date;
  blocked: boolean;
}>();

const MAX_EXECUTIONS_PER_MINUTE = 10;
const MAX_EXECUTIONS_PER_HOUR = 100;

function checkAgentRateLimit(agentId: string): boolean {
  const now = new Date();
  const tracker = agentExecutionTracker.get(agentId) || {
    count: 0,
    lastReset: now,
    blocked: false
  };
  
  // Reset counter every minute
  if (now.getTime() - tracker.lastReset.getTime() > 60000) {
    tracker.count = 0;
    tracker.lastReset = now;
    tracker.blocked = false;
  }
  
  if (tracker.count >= MAX_EXECUTIONS_PER_MINUTE) {
    tracker.blocked = true;
    // Send emergency alert!
    await sendSlackAlert(`🚨 Agent ${agentId} hit rate limit!`);
    return false;
  }
  
  tracker.count++;
  agentExecutionTracker.set(agentId, tracker);
  return true;
}

🛡️ Advanced Protection Layers​

4. Lambda Reserved Concurrency Per Agent​

// In sst.config.ts - limit agent Lambdas
const agentExecutionHandler = new Function(stack, 'agentExecutionHandler', {
  handler: 'packages/client/server/agents/execute.handler',
  reservedConcurrentExecutions: 5, // MAX 5 concurrent agent executions
  timeout: '2 minutes', // Short timeout
  // ... other config
});

5. DynamoDB Agent Execution Lock​

// Distributed locking to prevent agent storms
interface AgentExecutionLock {
  agentId: string;
  lockedAt: Date;
  executionId: string;
  ttl: number; // Auto-expire after 5 minutes
}

async function acquireAgentLock(agentId: string): Promise<boolean> {
  try {
    await dynamoDb.put({
      TableName: 'AgentExecutionLocks',
      Item: {
        agentId,
        lockedAt: new Date().toISOString(),
        executionId: crypto.randomUUID(),
        ttl: Math.floor(Date.now() / 1000) + 300 // 5 minutes
      },
      ConditionExpression: 'attribute_not_exists(agentId)'
    }).promise();
    return true;
  } catch (error) {
    if (error.code === 'ConditionalCheckFailedException') {
      return false; // Lock already exists
    }
    throw error;
  }
}

6. Credit Exhaustion Monitoring​

// Real-time credit burn monitoring
async function monitorCreditBurn(agentId: string, creditsUsed: number) {
  const burnRate = await calculateBurnRate(agentId, creditsUsed);
  
  // If burning > 100 credits/minute, EMERGENCY STOP
  if (burnRate > 100) {
    await emergencyStopAgent(agentId);
    await sendSlackAlert(`🚨🚨🚨 Agent ${agentId} burning ${burnRate} credits/minute! STOPPED!`);
  }
  
  // If burning > 50 credits/minute, warn
  if (burnRate > 50) {
    await sendSlackAlert(`⚠️ Agent ${agentId} burning ${burnRate} credits/minute`);
  }
}

async function emergencyStopAgent(agentId: string) {
  // Set agent to disabled in database
  await AgentRepository.updateById(agentId, { 
    isActive: false,
    emergencyStoppedAt: new Date(),
    emergencyStopReason: 'Credit burn rate exceeded threshold'
  });
  
  // Kill any running executions
  await killActiveAgentExecutions(agentId);
}

🚨 Nuclear Options for 3 AM Emergencies​

7. Global Agent Kill Switch​

// Add to your settings.ts
'AgentGlobalKillSwitch': makeBooleanSetting({
  key: 'AgentGlobalKillSwitch',
  name: 'Emergency Agent Kill Switch',
  defaultValue: false,
  description: 'EMERGENCY: Disable ALL agent executions immediately',
  category: 'Experimental',
}),

// In agent execution handler
const globalKillSwitch = await getAdminSetting('AgentGlobalKillSwitch');
if (globalKillSwitch) {
  return { statusCode: 503, body: 'All agents disabled by emergency kill switch' };
}

8. SMS Emergency Alert System​

// Wake you up at 3 AM if things go sideways
async function checkForAgentApocalypse() {
  const totalCreditsLastHour = await getTotalCreditsSpentLastHour();
  const activeAgentCount = await getActiveAgentCount();
  
  if (totalCreditsLastHour > 1000 || activeAgentCount > 20) {
    await sendSMS(process.env.ERIK_EMERGENCY_PHONE, 
      `🚨 AGENT APOCALYPSE DETECTED! 
       Credits: ${totalCreditsLastHour}/hour
       Active Agents: ${activeAgentCount}
       Check admin panel NOW!`);
    
    // Auto-enable kill switch
    await setAdminSetting('AgentGlobalKillSwitch', true);
  }
}

🎯 Your Defense Strategy​

Phase 1: Quick Wins (Implement Today)​

  1. Credit limits per agent execution (max 10 credits per call)
  2. Agent call depth limiting (max 3 levels deep)
  3. Lambda concurrency limits (max 5 concurrent)
  4. Global kill switch in admin settings

Phase 2: Monitoring (This Week)​

  1. Real-time credit burn monitoring
  2. Slack alerts for high burn rates
  3. Agent execution tracking in database

Phase 3: Advanced Safeguards (Next Week)​

  1. DynamoDB execution locks
  2. EventBridge rate limiting
  3. SMS emergency alerts

Your Emergency Runbook:​

# Agent Apocalypse Response - Keep this handy!

# 1. Immediate stop (30 seconds)
aws ssm put-parameter --name "/sst/app/AgentGlobalKillSwitch" --value "true"

# 2. Kill all agent Lambdas (1 minute)
aws lambda update-function-configuration --function-name AgentExecutionHandler \
  --reserved-concurrency-limit 0

# 3. Check damage (see current spend)
aws ce get-cost-and-usage --time-period Start=2024-01-01,End=2024-01-02 \
  --granularity DAILY --metrics BlendedCost

# 4. Recovery (after you stop the bleeding)
aws ssm put-parameter --name "/sst/app/AgentGlobalKillSwitch" --value "false"
aws lambda update-function-configuration --function-name AgentExecutionHandler \
  --reserved-concurrency-limit 5

The key insight: Agents are like teenagers with credit cards - they need strict allowances, curfews, and someone watching their spending!

Your useOwnCredits field is actually brilliant for this - you can give each agent a small credit allowance and when it's gone, it's gone. No more 3 AM flights to Timbuktu! ✈️🏜️