The Context Engine
Same frontier models.
More solved work per budget.
Augment's Context Engine maintains a live understanding of your stack across repos, services, and history, so agents spend fewer tokens searching and more turns shipping correct changes.
33%
Lower Spend
32%
Fewer Tokens
Opus 4.7
Same Model
001
Where AI coding costs get wasted
Most coding agents build context by grepping files, opening broad spans, and replaying everything they found into the model. They spend budget before they know what matters.
THE RESULT
Every miss becomes another tool call, another context replay, another correction, and another expensive turn.
COST DRIVERS IN LIMITED CONTEXT
+Context Engine benchmark+
More solved engineering work per AI budget.
On the same frontier model, Auggie solved tasks at effectively the same rate as Claude Code while using materially fewer tokens. The difference is retrieval: the Context Engine sends the model the context that matters instead of replaying broad, noisy file searches.
FULL CODE SEARCH
Real-time semantic retrieval
The Context Engine is not just grep or keyword matching. It is a full search engine for your code that retrieves the right slice before the model spends tokens exploring.
Augment semantically indexes and maps your code, understanding relationships between hundreds of thousands of files.
When you ask "add logging to payment requests," it maps the entire path: React app, Node API, payment service, database, and webhook handlers. The model sees what matters instead of paying for broad exploratory search.
Fig. 002—Semantic search retrieval
The Context Engine retrieves with:
Fewer misses across every source
Code is only part of the context agents need. Augment grounds retrieval in the artifacts that explain why the code works the way it does, so agents spend fewer turns rediscovering decisions your team already made.
Commit history
Why changes were made, reducing expensive archaeology
Codebase patterns
How your team actually builds, so agents reuse instead of reinvent
External sources
Docs, tickets, and design decisions that prevent dead-end searches
Tribal knowledge
Edge cases and conventions discovered through deep codebase analysis
INTELLIGENT CONTEXT CURATION
From millions of lines to the context that pays off
Less prompt weight. More task signal.
The Context Engine does not dump your entire codebase into the prompt. It:
- •Retrieves only what matters before the model spends tokens
- •Compresses context without losing critical information
- •Ranks and prioritizes based on task relevance
- •Respects access permissions with proof of possession
Result: Agents use fewer turns to find the change and fewer tokens to carry the work forward.
Activity
Fig. 003—Context signal over session duration
COST AND THROUGHPUT
Better context compounds across the team
Sharper context lowers per-task model waste, then compounds into faster reviews, safer refactors, and more engineering work completed per budget.
Lower benchmark spend
On Terminal Bench 2.0 with Opus 4.7, Auggie spent 33% less than Claude Code while solving tasks at effectively the same rate.
Lower private repo spend
Internal evaluations on private repositories showed the same pattern: near-parity on solved tasks with 41% lower total spend.
Saved monthly
A 200+ person team cut PR review time from 7 minutes to 3 minutes. Senior engineers see 35% higher velocity, spending less time reviewing.
Faster refactoring
A 150+ person team completed their most complex workflow refactoring in one week. Originally estimated at 6 months, with full test coverage.
Get Started
Control AI coding spend without downgrading models
The Context Engine works with codebases of any size, from side projects to enterprise monorepos, so agents spend less time searching and more time completing the work.