Amp

Modern software development is moving beyond simple autocomplete and isolated code suggestions. Developers increasingly want an agent that can understand a real codebase, investigate problems, make changes across multiple files, run tools, and keep working while they focus on the bigger picture. This is where Amp takes a notably different approach.

Built as a frontier coding agent, it brings AI-assisted development into the terminal, editor, web, and mobile environments. Instead of treating AI as a small assistant that waits for every instruction, it is designed to handle longer development tasks and work through them with considerably less handholding.

The experience is particularly interesting for developers who regularly work on large repositories, debugging sessions, refactoring projects, migrations, or tasks where the correct solution is not obvious from a single file. Threads allow developers to keep conversations and coding work together, while remote execution makes it possible for agents to continue working even after a laptop is closed.

For someone who has spent hours jumping between terminal windows, documentation, source files, and browser tabs, this approach can feel much closer to having another developer working alongside you rather than simply using another autocomplete tool.

Key Features

• AI-powered coding agents designed for real software development tasks
• Support for terminal, web, and mobile workflows
• Multiple agent modes ranging from fast, low-cost tasks to complex reasoning
• Remote agent execution through isolated cloud machines called orbs
• Support for multiple leading AI models depending on the task
• Long-running agent threads for multi-step development work
• Codebase search and contextual retrieval
• Tool usage through terminal commands and MCP integrations
• Plugins for extending agents with additional tools and workflows
• Thread sharing and collaboration features for teams
• Remote creation and management of coding agents
• Automatic detection and redaction of many common secrets

User Interface

The interface is built around threads rather than a traditional chat-only experience. A developer can start an agent from the terminal, continue working through the web interface, or check active work from a mobile device. This makes the workflow feel less tied to a single development environment.

The web interface provides a central place to monitor active agents, while the CLI remains useful for developers who prefer staying close to their existing terminal workflow. Threads can also be shared with teammates, making it easier to review what an agent did and why it made particular changes.

One especially practical feature is the ability to run agents remotely. With an orb, the agent receives a dedicated remote environment where it can work on code independently. Developers can inspect the resulting files, review changes, use the terminal, and synchronize work back to their local environment when needed.

Accuracy & Performance

Performance depends on the complexity of the task and the selected agent mode. Instead of forcing every request through the same model configuration, the platform provides a dial ranging from low to ultra. Small, well-defined jobs can use a faster and less expensive mode, while architecture changes, migrations, and open-ended investigations can use more capable reasoning.

This distinction is useful in practice. Asking a highly capable model to rename a variable is unnecessary, while using a lightweight model for a complicated multi-file migration can create more rework than it saves. The different modes give developers a way to balance capability, speed, and cost according to the actual problem.

It can also use specialized subagents for tasks such as code review, codebase retrieval, complex planning, and external research. That division of work can be valuable when a project requires more than simply generating a code snippet.

As with any AI coding system, generated code should still be reviewed and tested. The service itself acknowledges that outputs can contain errors, bugs, security vulnerabilities, or inaccuracies. The strongest results are therefore likely to come from developers who treat the agent as an active engineering partner rather than an unquestionable source of truth.

Capabilities

The capabilities go well beyond generating individual functions. An agent can inspect a repository, search through files, execute commands, investigate errors, modify code, and work through a sequence of related tasks.

For example, a developer investigating a failing CI build can ask the agent to examine the relevant code and determine what changed. A frontend developer can ask it to inspect a running local application and make a particular interface element more minimal. A team maintaining an older codebase can use it to investigate dependencies, trace functionality, or plan a larger refactoring effort.

Remote orbs add another layer to this workflow. Agents can work on remote machines without occupying the developer's local environment, and those agents can continue operating while the developer is away. The system also supports scheduled and event-driven agent workflows, opening the door to development tasks that do not need to begin with a developer sitting at the keyboard.

Plugins provide another extension point. Teams can add tools, commands, event-driven behavior, and custom agent modes, making the platform adaptable to specialized development workflows.

Security & Privacy

Security is an important consideration for an agent that can inspect source code and interact with development environments. The service reports SOC 2 Type II certification and annual penetration testing across its client, backend, and infrastructure.

Data transmitted in transit is protected with TLS 1.2 or higher, while stored data uses AES-256 volume-level encryption. The system also includes automatic secret redaction designed to detect common credentials such as cloud keys, GitHub tokens, API keys, and service secrets before they are sent to external model providers.

Enterprise customers receive additional controls, including single sign-on, directory synchronization, advanced thread visibility controls, workspace management features, cost controls, MCP registry allowlists, and zero data retention for LLM inference.

It is worth understanding that remote AI development necessarily involves sending selected code and contextual information to cloud infrastructure and model providers. Developers handling sensitive projects should review the available retention settings and provider policies before using the service with confidential repositories.

Use Cases

• Debugging: Investigate errors, trace code paths, inspect related files, and work toward a complete fix.
• Code Refactoring: Handle changes that span multiple files instead of manually editing every occurrence.
• Large Codebases: Search and retrieve relevant parts of a repository when the required context is difficult to locate manually.
• Feature Development: Describe a desired outcome and let the agent investigate the existing implementation before making changes.
• Code Review: Use dedicated review capabilities to identify potential bugs and questionable changes.
• CI Investigation: Ask an agent to investigate failing builds or tests and determine possible causes.
• Migration Projects: Work through complicated changes involving multiple components, dependencies, or architectural decisions.
• Remote Development: Start agents on cloud machines and continue working without keeping a local development session open.
• Research: Combine codebase retrieval with external search when solving problems that require documentation or outside technical information.
• Team Collaboration: Share threads so developers can understand previous agent interactions and review ongoing work.

Pros and Cons

Pros

• Designed around agentic software development rather than basic autocomplete.
• Can work across repositories and multiple files.
• Supports terminal, web, and mobile workflows.
• Remote execution allows agents to continue working independently.
• Multiple models and agent modes provide flexibility between cost and capability.
• Extensible through plugins and MCP-based tools.
• Strong security documentation and enterprise controls.
• Useful collaboration features for development teams.

Cons

• Advanced agentic workflows can consume more resources than simple code completion.
• Usage-based pricing requires developers to pay attention to model and agent consumption.
• AI-generated code still requires human review and testing.
• Remote execution may not be suitable for every organization or sensitive development environment.
• The number of available modes and models may require some experimentation before finding the right workflow.

Pricing Plans

The pricing model is designed around both subscriptions and usage-based access. Developers can continue using pay-as-you-go credits without committing to a monthly subscription, with credits charged according to actual model and tool usage.

The Megawatt subscription is available for $20 per month and includes all product features, 750 hours of small orbs, $20 of included agent usage, and access to low and medium modes. Users can also link an eligible ChatGPT subscription for additional GPT-5.6 usage.

The Gigawatt subscription costs $200 per month and includes everything in Megawatt, along with 1,000 hours of large orbs, $200 of included agent usage, and access to all modes, including high and ultra.

Pay-as-you-go credits remain available without a subscription, with a minimum credit purchase of $5. Enterprise customers receive additional security, administration, and workspace controls under a separate enterprise pricing structure.

How to Use the Coding Agent

Getting started is straightforward for developers who are comfortable with a terminal-based workflow. After installing the client, sign in and open the project or directory you want to work with. From there, you can create a thread and describe the development task in natural language.

A good first request is usually specific about the desired outcome rather than dictating every individual edit. For example, instead of telling the agent which files to modify, you can explain that a particular test is failing and ask it to investigate the cause, make the necessary correction, and verify the result.

For smaller jobs, a lower mode can provide a faster and more economical experience. More complicated work can be moved to higher modes when deeper reasoning is justified. Developers can also use remote orbs when they want an agent to continue working away from their local machine.

Once the agent finishes, review its changes, inspect the relevant files, run tests, and verify the result. This final review remains an important part of any AI-assisted development workflow.

Comparison with Similar Tools

There are many AI coding assistants available today, but they do not all approach development in the same way. Traditional coding assistants tend to focus heavily on inline suggestions and autocomplete, while agent-oriented products concentrate more on completing multi-step tasks.

The main distinction here is the emphasis on persistent threads, remote execution, multiple agent modes, and the ability to operate across different environments. This makes the platform particularly appealing to developers who want AI to investigate and execute a complete piece of work rather than simply suggest the next few lines of code.

Another differentiator is the combination of model flexibility and specialized subagents. Instead of presenting developers with one fixed model for every task, the system can use different models and supporting agents for reasoning, retrieval, review, research, and other parts of the development process.

For developers who primarily want autocomplete inside an editor, a conventional coding assistant may remain the simpler choice. For developers looking for longer-running autonomous workflows and remote agents, this approach is considerably more ambitious.

Conclusion

AI coding tools are becoming less about predicting the next line and more about completing meaningful engineering work. This product sits firmly on that side of the transition, combining coding agents, multiple models, persistent threads, remote machines, and extensibility into a single development workflow.

Its biggest appeal is the ability to delegate substantial engineering tasks without completely stepping away from the process. Developers can start an investigation, let an agent work through the repository, review the resulting changes, and continue from the same thread later.

For experienced developers, software teams, and technically minded builders who want to experiment with a more autonomous style of programming, it offers a compelling way to rethink how AI fits into everyday development.

Frequently Asked Questions (FAQ)

What is this coding tool used for?

It is an AI coding agent designed to help developers investigate codebases, write and modify code, debug problems, run development tools, review changes, and complete multi-step engineering tasks.

Can it work with large codebases?

Yes. The system includes codebase retrieval and search capabilities designed to help agents find relevant files and context while working on development tasks.

Can AI agents work remotely?

Yes. Remote orbs provide dedicated machines where agents can run without depending on an open local development session. Developers can inspect the work and synchronize changes back to their local environment.

Does it support multiple AI models?

Yes. Different models are used for different tasks, including coding, reasoning, retrieval, review, research, and multimodal work. The available agent modes are designed to balance capability, speed, and cost.

Is there a pay-as-you-go option?

Yes. Developers can purchase usage credits without subscribing to a monthly plan. The service charges based on actual model and certain tool usage.

Is it suitable for enterprise development?

Yes. Enterprise features include SSO, directory synchronization, advanced workspace controls, cost management, MCP allowlists, audit capabilities, and zero data retention for LLM inference.

Should developers review AI-generated code?

Absolutely. AI-generated code can contain bugs, security issues, or incorrect assumptions. Developers should review changes, run tests, and verify the resulting behavior before merging or deploying them.