An AI coding agent built by Augment Inc. in the United States. Since its launch in April 2024, its strength has been the “Context Engine,” a codebase analysis layer that lets the agent work against the dependencies of a whole repository rather than a single open file. Give it an instruction in plain language and it breaks the task down, then works autonomously through feature additions, bug fixes, test generation, documentation updates, and pull request creation. Today the product is presented as “Cosmos,” a platform for an always-on software delivery system that moves work from spec to verification, with the emphasis shifting toward workflow-level automation: code review, ticket-to-PR, vulnerability remediation, and incident response.
Key Features
- A Context Engine that covers the whole repository: It indexes the entire codebase, not just the file you have open, so answers and edits account for complex dependencies. Chat, the IDE extensions, the CLI, and cloud agents all share the same context layer
- Agents that finish the task: From a natural-language instruction, it decomposes the work and continues through implementation, tests, documentation updates, and pull request creation. Asynchronous execution means work keeps going in the background after you close your laptop
- Auggie CLI for terminal-based work: A CLI that brings the agent, the Context Engine, and its tools into the terminal — well suited to scripting and CI integration where no GUI is involved
- IDE extensions for VS Code and JetBrains: Completions, chat, and agent runs are available inside your usual editor; the official documentation covers both families of extensions
- Workflow automation with Cosmos: Recurring loops such as code review, ticket-to-PR conversion, vulnerability fixes, and incident response are automated through triggers paired with specialized agents. Integrations include GitHub, GitLab, Slack, Linear, Jira, and webhooks
- MCP and native tool integration: External services and tools can be connected via the Model Context Protocol, and a configuration that exposes the Context Engine itself as an MCP server for other agents is published
Pricing
| Plan | Monthly price | Key features |
|---|---|---|
| Business | $100 | Up to 50 seats (no per-seat charge), $100/month of usage pooled across LLM, Context Engine, and compute; Context Engine / coding agent / CLI / MCP; 50 concurrent sessions; SOC 2 Type II; pay-as-you-go top-ups beyond the included amount |
| Enterprise | Contact sales | Everything in Business plus unlimited users, unlimited concurrent sessions, custom compute size and multi-region options, SSO (OIDC, SCIM), CMEK, ISO 42001, SIEM integration, data residency, audit trails, and a dedicated support team |
Usage is consumed by what you actually run rather than by seat count, so the real monthly cost depends more on how much agent execution and context lookup you do than on team size. The pricing model has changed before — it moved to a credit-based structure in October 2025 — so it is worth confirming the current terms before adopting it.
Pricing information is current as of August 2026. Please check the official website for the latest pricing.
Pros & Cons
✅ Pros
- Whole-codebase context makes it less likely to lose the thread in large repositories with legacy code and tangled dependencies
- Implementation, tests, documentation, and pull request creation can be handed over as one continuous flow, arriving in reviewable units
- The IDE extensions, CLI, and cloud agents share a single foundation, so you can switch surfaces without losing context
- The Business plan covers up to 50 seats with no per-seat charge, so costs do not spike as the team grows
- SOC 2 Type II compliance, with SSO, audit trails, and data residency available on Enterprise for organizational requirements
⚠️ Cons
- No low-cost individual plan appears on the official pricing page; the entry point is $100 per month, which is steep for a solo developer
- Usage-based billing makes costs harder to predict in months with heavy agent use
- Pricing and plan structure have changed in the past, so long-term budgeting should account for that risk
- The broad scope of autonomous execution does not remove the need for human review of the resulting changes
- With the product centered on Cosmos-style workflow automation, it is easily overspecified for anyone who only wants code completion
Comparison with Similar Services
| Criteria | Augment Code | GitHub Copilot | Cursor | Devin |
|---|---|---|---|---|
| Provider | Augment Inc. | GitHub (Microsoft) | Anysphere | Cognition |
| Primary form | Context Engine + autonomous agents + workflow automation | IDE-extension-centric coding assistance | AI-first code editor | Autonomous AI software engineer |
| Main use case | Large-repository changes and PR automation | Everyday completion and chat | Interactive editing in the editor | Delegating whole tasks |
| Async / background execution | Supported | Partially supported | Partially supported | Supported |
| Intended users | Development teams and enterprises | Individuals to enterprises | Individuals to teams | Development teams and enterprises |
Who Is It For
- Development teams with repositories in the hundreds of thousands of lines, where file-level completion does not supply enough context
- Organizations that want to automate the ticket-to-PR path and concentrate human effort on review
- Teams looking to keep an agent permanently on recurring work such as code review and vulnerability remediation
- Developers who move between IDE, terminal, and cloud and want to continue in the same context
- Companies that need SOC 2, SSO, and audit trails satisfied before adopting AI coding
Summary
Augment Code is a coding agent built on a Context Engine that reads the whole codebase, carrying work autonomously from implementation through to the pull request. As Cosmos, it has expanded toward handing the spec-to-verification workflow to always-on agents. Pricing starts at $100 per month for Business plus usage-based charges, which is not a casual price point for individuals, but for teams maintaining large repositories it is a credible way to introduce automation that does not lose the thread. A reasonable first step is to try the IDE extensions and the Auggie CLI from the official documentation and judge the fit against your own repository size and workflow.