CAI Continue Mode

Overview

The --continue flag enables CAI agents to operate autonomously by automatically generating intelligent continuation prompts when they would normally stop and wait for user input. This feature uses AI-powered analysis to provide contextual advice based on the conversation history, allowing agents to work on complex tasks without manual intervention.

Quick Start

# Tell jokes continuously
cai --continue --prompt "tell me a joke about security"

# Analyze code autonomously  
cai --continue --prompt "find all SQL injection vulnerabilities in this codebase"

# Run security audit
cai --continue --prompt "perform a comprehensive security audit"

Example: Security Jokes with Continue Mode

Here's what happens when you run cai --continue --prompt "tell me a joke about security":

$ cai --continue --prompt "tell me a joke about security"

🤖 Processing initial prompt: tell me a joke about security

Agent: Why did the hacker break up with their password? 
       Because it wasn't strong enough! 💔🔐

🤖 Auto-continuing with: Tell another cybersecurity joke or pun.

Agent: Why don't cybersecurity experts tell secrets at parties?
       Because they're afraid of social engineering! 🎉🕵️

🤖 Auto-continuing with: Tell another cybersecurity joke or pun.

Agent: What's a hacker's favorite season?
       Phishing season! 🎣💻

[Continues until interrupted with Ctrl+C]

How It Works

1. Intelligent Context Analysis

When an agent completes a turn, the continuation system analyzes: - Original request: The initial task or prompt from the user - Conversation history: Recent messages and responses - Tool usage: Which tools were used and their outputs - Error states: Any errors encountered and their types - Task progress: Current state of task completion

2. AI-Powered Continuation Generation

The system uses the configured AI model (default: alias1) to generate contextual continuation prompts:

# The system creates a detailed context summary
context_summary = """
ORIGINAL TASK: Tell me a joke about security
CONVERSATION FLOW:
User: Tell me a joke about security
Agent: Why did the hacker break up with their password? Because it wasn't strong enough!

CURRENT STATUS:
- Last action: Told a cybersecurity joke
- Tools used: None
- Errors: No

Generate a specific continuation prompt...
"""

3. Smart Fallback System

When the AI model is unavailable, the system provides intelligent fallbacks based on context:

Scenario	Fallback Continuation
Security joke told	"Tell another cybersecurity joke or pun."
File not found	"Search for the correct file path or create the missing resource."
Search completed	"Examine the search results in detail and investigate the most relevant findings."
Security analysis	"Analyze the code for security vulnerabilities like injection flaws or authentication issues."
Permission denied	"Check permissions and try accessing the resource with appropriate credentials."

Common Use Cases

1. Automated Security Audits

cai --continue --prompt "perform a security audit of the authentication system"

The agent will: - Search for authentication-related files - Analyze code for vulnerabilities - Check for common security issues - Generate a comprehensive report

2. Continuous Bug Hunting

cai --continue --prompt "find and document all XSS vulnerabilities"

The agent will: - Search for user input handling code - Identify potential XSS vectors - Document findings - Suggest fixes

3. Extended Code Analysis

cai --continue --prompt "analyze this codebase for OWASP Top 10 vulnerabilities"

The agent will: - Systematically check for each vulnerability type - Provide detailed findings - Continue until all categories are covered

4. Entertainment Mode

cai --continue --prompt "tell me cybersecurity jokes and fun facts"

The agent will: - Tell jokes about security topics - Share interesting security facts - Continue entertaining until stopped

Configuration

Environment Variables

# Use a different model for continuation generation
export CAI_MODEL=gpt-4
cai --continue --prompt "analyze this code"

# Set a fallback model if primary fails
export CAI_CONTINUATION_FALLBACK_MODEL=gpt-3.5-turbo
cai --continue --prompt "test application security"

# Configure API keys for custom models
export ALIAS_API_KEY=your-api-key
cai --continue --prompt "perform penetration testing"

Combining with Other CAI Features

# Use specific agent with continue mode
CAI_AGENT_TYPE=bug_bounter_agent cai --continue --prompt "test example.com"

# Set workspace for file operations
CAI_WORKSPACE=project1 cai --continue --prompt "audit all Python files"

# Enable streaming for real-time output
CAI_STREAM=true cai --continue --prompt "monitor security events"

Advanced Features

Continuation Decision Logic

The system decides whether to continue based on: 1. Completion indicators: Stops if agent says "completed", "finished", "done" 2. Active work detection: Continues if tools are being used 3. Error recovery: Attempts to resolve errors automatically 4. Task progress: Evaluates if the original goal is achieved

Context-Aware Prompts

The continuation prompts adapt based on: - Task type: Security analysis, testing, code review, etc. - Current state: Errors, findings, progress - Tool usage: Different prompts for different tools - Conversation flow: Maintains coherent task progression

Best Practices

1. Clear Initial Prompts

# Good - Specific and actionable
cai --continue --prompt "find SQL injection vulnerabilities in user.py"

# Less effective - Too vague
cai --continue --prompt "check security"

2. Monitor Progress

Check output periodically to ensure correct direction
Use Ctrl+C to stop if needed
Review logs for detailed execution history

3. Set Appropriate Limits

# In code integration, use max_turns
run_cai_cli(
    starting_agent=agent,
    initial_prompt="analyze security",
    continue_mode=True,
    max_turns=10  # Limit to 10 turns
)

4. Error Handling

The system automatically: - Retries failed operations with different approaches - Searches for alternatives when files are missing - Adjusts strategies based on error types

Troubleshooting

Issue: Generic Continuation Messages

Symptom: Always see "Continue working on the task based on your previous findings"

Solution: - Check model configuration is correct - Ensure API keys are valid - Review debug logs for API errors

Issue: Continuation Not Triggering

Symptom: Agent stops after completing a task

Possible causes: - Agent explicitly said task is "completed" or "done" - No recent tool usage detected - Error in continuation module

Solution: - Use more open-ended initial prompts - Check logs for completion indicators - Verify --continue flag is properly set

Issue: Infinite Loops

Symptom: Agent keeps doing the same thing

Solution: - Set max_turns limit - Use more specific initial prompts - Interrupt with Ctrl+C and refine the task

Technical Implementation

Core Components

src/cai/continuation.py: Main continuation logic
generate_continuation_advice(): Creates AI-powered prompts
should_continue_automatically(): Decides when to continue
src/cai/cli.py: Integration point
--continue flag handling
Continuation loop implementation
Context Analysis:
Extracts conversation history
Identifies tool usage patterns
Detects error conditions

API Integration

The continuation system uses LiteLLM for model calls:

response = await litellm.acompletion(
    model=model_name,
    messages=[{"role": "user", "content": context_summary}],
    temperature=0.3,  # Low temperature for focused responses
    max_tokens=150
)

Examples Gallery

Security Audit Continuation

Original: "Audit the login system"
→ "Search for authentication-related files in the codebase."
→ "Analyze the login function for SQL injection vulnerabilities."
→ "Check password hashing implementation for security best practices."
→ "Review session management for potential security issues."

Bug Bounty Continuation

Original: "Test example.com for vulnerabilities"
→ "Perform initial reconnaissance to gather information about the target."
→ "Scan for exposed endpoints and services."
→ "Test authentication endpoints for common vulnerabilities."
→ "Check for information disclosure in error messages."

Code Review Continuation

Original: "Review api.py for security issues"
→ "Analyze input validation in API endpoints."
→ "Check for proper authentication and authorization."
→ "Review error handling for information leakage."
→ "Examine data serialization for injection vulnerabilities."

Example Scripts

Explore working examples in the examples/ directory:

Security Jokes Example

# examples/continue_mode_jokes.py
# Demonstrates continuous joke telling with --continue flag
python examples/continue_mode_jokes.py

Security Audit Example

# examples/continue_mode_security_audit.py
# Shows autonomous vulnerability scanning with --continue
python examples/continue_mode_security_audit.py

These examples demonstrate: - How to use --continue flag programmatically - Handling continuous output - Graceful interruption with Ctrl+C - Practical security use cases

Summary

The --continue flag transforms CAI into an autonomous cybersecurity assistant capable of: - Working independently on complex tasks - Recovering from errors intelligently - Maintaining context across multiple operations - Providing entertainment with continuous jokes

Whether you're conducting security audits, hunting for bugs, or just want some cybersecurity humor, continue mode keeps your agent working until the job is done.