Continuous AI Technology

Top 6 Things You Didn't Know You Can Do With Continue

Discover the hidden features and powerful capabilities of Continue that can transform your development workflow

Brian Douglas's avatar Brian Douglas
8 min read
#continue #features #productivity #tips #developer-tools

While most developers know Continue as an open-source AI coding agents, the platform has quietly evolved far beyond basic code completion. With over 500,000 active users and growing enterprise adoption, Continue has shipped powerful capabilities that many users haven’t discovered yet. These features transform Continue from a simple coding agents into a comprehensive AI development platform that maintains complete control over your data and workflows.

1. Continue CLI: Bring AI to your terminal and CI/CD pipelines

The Continue CLI (cn) extends AI capabilities beyond your IDE into terminal workflows and CI/CD pipelines. This standalone command-line tool functions as an autonomous coding agent that can navigate codebases, make edits, run terminal commands, and even operate in headless mode for automation.

Installation and Core Capabilities

npm install -g @continuedev/cli

The CLI maintains all of Continue’s capabilities including model selection, custom configurations, and even Model Context Protocol (MCP) support. What makes it powerful is the -p flag for completely automated workflows—imagine having an AI agent that can fix failing tests or refactor code as part of your build pipeline.

Key Use Cases

  • Automated code reviews in CI pipelines with custom rule enforcement
  • Intelligent debugging that analyzes logs and suggests fixes
  • Semantic commit messages generated from your changes
  • Test generation based on implementation code
  • Documentation updates synchronized with code changes

Enterprise Automation

The CLI’s headless mode enables integration into any automation workflow. Teams use it for:

  • Pre-commit hooks that ensure code quality
  • Automated PR descriptions and reviews
  • Migration scripts that adapt to your codebase
  • Incident response automation

Documentation: Continue CLI Guide

2. Model Context Protocol: Securely connect any data source

Continue is one of the first AI coding agents with complete Model Context Protocol (MCP) support, including SSE and HTTP transports. MCP transforms Continue from an isolated tool into a universal data interface that can securely connect to databases, APIs, internal tools, and documentation systems—all without exposing sensitive data to LLMs.

How MCP Works

The protocol acts as a standardized bridge between Continue and your data sources. When you ask a question, MCP:

  1. Fetches relevant context from your configured sources
  2. Processes it locally without sending raw data to the LLM
  3. Provides the AI with structured context to answer accurately

Supported Integrations

Through MCP servers, Continue can connect to:

  • Databases: PostgreSQL, MySQL, SQLite (query without exposing data)
  • Development tools: GitHub, GitLab, Jira, Linear
  • Cloud services: AWS, Supabase, Firebase
  • Internal systems: Any HTTP endpoint or custom tool
  • Documentation: Confluence, Notion, internal wikis

Security Architecture

MCP’s design ensures data never leaves your infrastructure:

  • Local processing of sensitive information
  • Support for OAuth, mTLS, and custom authentication
  • Complete audit trails for compliance
  • Row-level security enforcement from existing databases

Configuration Example

mcpServers:
  - name: postgres
    command: npx
    args:
      - "-y"
      - "@modelcontextprotocol/server-postgres"
      - "postgresql://localhost/mydb"

Documentation: Model Context Protocol Deep Dive

3. Development Data: Measure and optimize AI impact

Continue’s Development Data Collection system automatically tracks how your team uses AI assistance, providing actionable insights into productivity patterns and ROI. This isn’t telemetry for Continue—it’s your own analytics platform for understanding AI’s actual impact on development.

Data Collection Scope

All data is stored locally by default in .continue/dev_data/ and includes:

  • Interaction types and acceptance rates
  • Time spent on different coding tasks
  • Model usage and token consumption
  • Context patterns and efficiency metrics
  • Feature-specific usage statistics

Analytics Configuration

You can route data to your existing analytics infrastructure:

# config.yaml
dataPlatform:
  destination:
    type: "s3"  # or "azure", "gcs", "snowflake"
    config:
      bucket: "dev-analytics"
      region: "us-west-2"

Key Insights Available

  • Adoption patterns: Which teams and features see highest usage
  • Productivity metrics: Time saved on specific tasks
  • Quality indicators: Correlation between AI usage and code quality
  • Cost optimization: Token usage efficiency and model selection
  • Training needs: Identify teams that need additional support

Privacy and Control

  • All data collection is opt-in and configurable
  • Data never leaves your infrastructure unless explicitly configured
  • Full GDPR compliance with user-level data controls
  • Can be completely disabled for sensitive projects

Organizations use this data to:

  • Justify AI investment with concrete ROI metrics
  • Optimize model selection and context configuration
  • Identify best practices from high-performing teams
  • Create data-driven training programs

Documentation: Development Data Documentation

4. Models Add-on: Access any AI model through one platform

Continue’s Models Add-on provides a unique approach to model access—you can bring your own API keys for any provider, or purchase a single subscription that gives you access to multiple premium models without managing separate accounts. This flexibility solves a critical problem for teams: balancing cost, performance, and vendor management.

Two Ways to Access Models

Bring Your Own Key (BYOK):

  • Use your existing OpenAI, Anthropic, Google, or other API keys
  • Maintain direct billing relationships with providers
  • Full control over rate limits and usage
  • Support for 20+ model providers including local options

Continue Models Add-on:

  • Single subscription for multiple premium models
  • No separate API keys to manage
  • Includes latest models from OpenAI, Anthropic, and others
  • Simplified billing and administration
  • Enterprise pricing available for teams

For current pricing details, visit: Continue Pricing

Enterprise Benefits

For Organizations Using BYOK:

  • Leverage existing enterprise agreements
  • Maintain compliance with data policies
  • Use approved vendors only
  • Complete usage visibility

For Teams Using Models Add-on:

  • Simplified procurement (one vendor)
  • Predictable monthly costs
  • No API key management overhead
  • Instant access to new models as they’re released
  • Centralized billing for all AI usage

Cost Optimization Strategies

Continue’s model flexibility enables sophisticated cost management:

  • Use expensive models only for complex tasks
  • Route simple completions to cheaper models
  • Run local models for sensitive operations
  • Mix and match based on team preferences

Documentation: Model Providers Setup and Models Add-on Pricing

5. Rules and Configuration: Create team-wide AI standards

Continue’s configuration system enables teams to share consistent AI behavior through workspace configurations, custom rules, and prompt templates. This isn’t just about settings—it’s about encoding your team’s best practices and standards into the AI itself. See the documentation rule below for example:

---
name: Documentation Standards
globs: docs/**/*.{md,mdx}
alwaysApply: false
description: Standards for writing and maintaining Continue Docs
---

# Continue Docs Standards

- Follow Mintlify documentation standards
- Include YAML frontmatter with title, description, and keywords
- Use consistent heading hierarchy starting with h2 (##)
- Include relevant Admonition components for tips, warnings, and info
- Use descriptive alt text for images
- Include cross-references to related documentation
- Reference other docs with relative paths
- Keep paragraphs concise and scannable
- Use code blocks with appropriate language tags

Workspace Configuration

Place a example.md file in your .continue/rules directory at the project root to automatically configure Continue for everyone who opens that project.

Custom Rules

Rules tell Continue how to behave in specific contexts. They can be applied globally, triggered by file patterns, or even auto-generated when Continue learns from your corrections. Rules enable you to encode coding standards, best practices, and team conventions directly into the AI’s behavior.

For detailed information on creating and managing rules, see: Rules Documentation

This system enables:

  • Consistent code standards across large teams
  • Automatic onboarding with pre-configured environments
  • Compliance enforcement through mandatory rules
  • Knowledge sharing via documented best practices

Documentation: Configuration Deep Dive

6. Continuous AI: Build automated development workflows

Continuous AI represents a paradigm shift in how developers interact with AI—moving from isolated assistance to integrated workflows that span your entire development lifecycle. Continue enables you to create sophisticated AI pipelines that connect your IDE, terminal, and CI/CD systems into a unified, intelligent development environment.

The Continuous AI Philosophy

Traditional AI coding agents operate in isolation—they help with code completion or answer questions, but don’t understand your broader workflow. Continuous AI breaks down these silos by:

  • Connecting contexts across different tools and environments
  • Automating repetitive development tasks end-to-end
  • Learning from patterns in your development process
  • Orchestrating complex multi-step operations

Real-World Applications

Automated Refactoring Campaigns:

  • Identify technical debt across repositories
  • Plan refactoring strategy with AI
  • Execute changes systematically
  • Track progress and measure impact

Intelligent Code Reviews:

  • Pre-review code before human inspection
  • Suggest improvements based on team patterns
  • Auto-fix common issues
  • Generate review summaries

Self-Healing Systems:

  • Monitor application logs for errors
  • Generate fixes for known patterns
  • Test fixes in isolated environments
  • Deploy patches automatically

Cross-Environment Context Sharing

Continuous AI maintains context across different environments:

  • IDE context flows to terminal commands
  • Terminal output informs IDE suggestions
  • CI/CD results update local development
  • Production metrics influence code generation

This creates a feedback loop where every part of your development process informs and improves the others.

Getting Started with Continuous AI

  1. Start small: Automate one workflow (e.g., commit messages)
  2. Connect tools: Link IDE, CLI, and CI/CD systems
  3. Build patterns: Create reusable workflow templates
  4. Share knowledge: Document and distribute workflows
  5. Iterate: Continuously improve based on metrics

The power of Continuous AI isn’t in any single feature—it’s in how Continue’s capabilities combine to create intelligent, automated workflows that adapt to your development process. This transforms AI from a helpful agents into an integral part of your development pipeline.

For practical examples of Continuous AI in action, see: Beyond the Editor: How I’m Using Continue CLI to Automate Everything

Documentation: Continuous AI Guide

Strategic implementation recommendations

For Startups and Small Teams

Start with the Continue CLI for immediate automation wins and Development Data to track your ROI. Add Continuous AI workflows for repetitive tasks. These features require minimal setup but deliver measurable productivity gains.

For Mid-Size Organizations

Deploy workspace configurations to standardize AI behavior across teams. Implement MCP for secure database access and the Models Add-on for simplified model management. Build Continuous AI workflows for your most time-consuming processes.

For Enterprises

Begin with a pilot team using the full stack: CLI for automation, MCP for secure data access, Development Data for ROI tracking, standardized configurations for compliance, and Continuous AI workflows for end-to-end automation. The combination of local processing and complete transparency makes Continue uniquely suitable for regulated industries.

The path forward with Continue

These six capabilities represent Continue’s evolution from a code completion tool to a comprehensive AI development platform. What sets Continue apart isn’t just the breadth of features, but the philosophy of complete control and transparency. Every capability maintains data sovereignty, allows full customization, and provides clear visibility into AI operations.

The concept of Continuous AI—where assistance flows seamlessly across your entire development workflow—is unique to Continue’s architecture. By connecting your IDE, terminal, and CI/CD systems with intelligent automation, Continue enables a new paradigm of AI-augmented development that goes far beyond simple code suggestions.

The open-source nature of Continue means these features will continue evolving with community input. Unlike proprietary solutions, you’re not locked into a vendor’s vision—you can modify, extend, and adapt Continue to your specific needs. Whether you’re a solo developer or a Fortune 500 enterprise, Continue provides the tools to implement AI assistance on your terms.

As AI becomes essential to competitive software development, the question isn’t whether to adopt these tools, but how to maintain control while doing so. Continue’s answer is clear: powerful capabilities, complete transparency, and absolute sovereignty over your development process—all connected through Continuous AI workflows that transform how software is built.