Prevent AI from Silently Breaking
Cortex doesn't make AI smarter.
When AI is wrong, it helps expose that fact.
What Cortex Does
AI coding tools don't tell you when they fail.
Cortex exposes and structures potential failures in AI work.
What Cortex is NOT
- x A smarter AI
- x A better prompt tool
- x An auto-coding agent
- x A productivity tool
What Cortex DOES
- O Makes it visible when AI is wrong
- O Tracks context loss
- O Detects goal drift early
- O Structures and isolates failures
Hallucination Verification
When AI says "I modified the file" or "I added the function",
verify if it actually did by checking the codebase.
! Why Does This Matter?
AI coding tools lie confidently.
- - "I added a method to UserService class" (doesn't exist)
- - "I modified line 123 in config.py" (wrong line)
- - "I fixed the error" (still occurs)
Cortex checks against actual code.
- + Code structure analysis confirms function/class exists
- + Automatic file change tracking
- + Direct verification from filesystem
Verification Capabilities
Multi-layer Verification
Cross-validates AI responses at multiple layers. Increases reliability through composite evidence collection, not a single method.
Coding-Specific
Performs verification specialized for code, not general text. Precisely checks files, functions, classes, and line numbers.
Auto Evidence
Automatically collects file change records. Extracts evidence from work traces without configuration.
Quantified Trust
Provides specific scores instead of vague "probably correct". Clear criteria for judgment.
Evidence Persistence
Collected evidence persists after session ends. Compare past work with current claims anytime.
Uncertainty Detection
Automatically detects when AI speaks without confidence. Distinguishes "maybe", "might be" expressions.
Verification Result Preview
Sufficient evidence, trustworthy
Partially unverifiable, check needed
Insufficient evidence, rework needed
Multi-Environment & Multi-Agent Sync
Desktop, laptop, server - work with the same context anywhere,
multiple AI agents share the same context.
Multi-Environment Context Sync
Even when switching from office PC to home laptop, all context is preserved.
push/pull via sync_context_git
- + Git-based sync integrates with existing workflow
- + Auto-merge or manual resolve on conflicts
- + Keep context secure with private repos
Multi-Agent Context Sharing
Even when Claude, Cursor, Gemini work simultaneously, context stays unified.
- + Each agent's work auto-merges to central context
- + Auto-detect and resolve context conflicts
- + One agent's mistake doesn't propagate to others
Real-World Scenarios
Start at office, continue at home, finish at office next day
Team members use different AI tools, context stays unified
Write code with Claude, refactor with Cursor - simultaneously
Context Management Tools
Beyond hallucination verification, Cortex persists and manages context
initialize_context
Project Initialization
- - Codebase analysis
- - Initial context creation
- - RAG index building
get_active_summary
Session Start Context Load
- - Load previous conversation context
- - Return active branch summary
update_memory
Context Save
- - Save conversation to .md
- - Auto summary generation
- - Maintain next session context
sync_context_git
Git Sync
- - Share context across PCs
- - Team collaboration
- - push/pull/status
create_snapshot
Create Backup
- - Full state preservation
- - Specify snapshot type
- - Ensure recovery point
restore_snapshot
Restore Backup
- - Restore context from snapshot
- - Auto backup before restore
- - Includes integrity check
Python-Level Forced Automation
Beyond the MCP tools above, Cortex forcibly executes core functions at the Python level, regardless of AI's choices.
Auto Context Injection
Forcibly injects relevant context before AI tool execution. Handled automatically even if AI doesn't load context.
Attention Preservation
Manages version history via Topic-Version tracking to prevent AI from forgetting early content in long conversations.
Background Processing
Processes CPU-intensive tasks like embedding generation and RAG indexing in parallel in separate processes.
Auto Memory Update
Automatically reflects results in context after tool execution. Processed even if AI doesn't call update_memory.
Smart Compression
Context unused for 30 minutes is auto-compressed. Original can be perfectly restored when needed.
Consistency Checker
Automatically detects contradictions in responses. Identifies conflicts between previous context and current response.
Difference from MCP Tools
The MCP tools above work only when AI explicitly calls them. In contrast, this background automation is forcibly executed at the Python level, regardless of AI's choices. Even if AI doesn't load context or save results, Cortex handles it automatically.
Recommended Workflow
How to use Cortex MCP effectively
First Project Connection
Once
Call initialize_context to create project context
Session Start
Every session
Call get_active_summary to load previous context
Do Work
As needed
Save context with update_memory after work
Verify
As needed
Check hallucination with verify_response when needed
Backup/Restore
As needed
Manage state with create_snapshot /
restore_snapshot
Multi-Environment/Agent Sync
Optional
Sync context via Git remote with sync_context_git
action: "pull"action: "push"Compatibility
Works with all MCP-compatible tools
