A

AI visibility report

Augment Code ranks #1 in Autonomous Coding Agents AI search.

Outside the top three on 8 of the 25 prompts buyers actually ask.

Anthropic (Claude Code) is cited on 4 of those losses.

25 prompts
5 platforms
Updated Jun 30, 2026 - refreshed weekly
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9percent
Presence Rate
Low presence

Best among 17 vendors · still absent from 91.2% of tracked prompt responses

Top-3 citations across 125 prompt × platform pairs

+0.21
Sentiment
-1.00.0+1.0
Positive
#1of 17

Peer Ranking

#1#17
Top tierin Autonomous Coding Agents

Key Metrics

Presence Rate8.8%
Share of Voice32.7%
Avg Position#7.2
Docs Presence0.0%
Blog Presence0.0%
Brand Mentions8.0%

Platform Breakdown

Google AI Mode
28%7/25 prompts
Bing Copilot
8%2/25 prompts
Perplexity
8%2/25 prompts
Gemini Search
0%0/25 prompts
ChatGPT
0%0/25 prompts

Leader, with room to expand. Augment Code leads this category on presence and share of voice, but appears in only 8.8% of tracked prompt responses. The priority is defending current wins while expanding absolute coverage.

Where Augment Code is losing

Prompts where competitors are visible and Augment Code is not.

These prompt-level losses are the first prompts to track and repair.

Where Augment Code is winning5

  • What's the easiest AI coding agent to get running locally on a large existing TypeScript monorepo without hours of configuration?

    Avg # 1.0 · 1 platform

  • Which cloud coding agents are best for generating and merging pull requests asynchronously without a developer staying in the loop?

    Avg # 1.0 · 1 platform

  • Which AI coding agents handle context window limitations most gracefully when working across dozens of files in an enterprise codebase?

    Avg # 2.0 · 1 platform

  • What autonomous coding tools handle legacy codebases in dynamically typed languages best — Python 2 or older PHP specifically?

    Avg # 3.0 · 1 platform

  • Which cloud coding agents have the best uptime and task success rates for a mid-size team running dozens of concurrent agent jobs daily?

    Avg # 4.0 · 1 platform

Where Augment Code is losing5

  • What agentic coding tools handle long-running tasks reliably — resuming after an interruption rather than starting over from scratch?

    Competitors on 2 platforms

    Track this prompt
  • Which cloud coding agents integrate with CI pipelines to automatically attempt fixes when a build or test suite fails?

    Competitors on 1 platform

    Track this prompt
  • What autonomous coding agents run tasks inside a secure sandbox so a compromised prompt can't affect the host filesystem?

    Competitors on 1 platform

    Track this prompt
  • Which autonomous coding agents can reliably write and run tests, interpret failures, and self-correct without human intervention?

    Competitors on 1 platform

    Track this prompt
  • Which agentic CLI tools work out of the box on popular operating systems without requiring a container sandbox just to get started?

    Competitors on 1 platform

    Track this prompt

Track Augment Code daily before the next report refresh.

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Research dossierCapabilities, use cases, sources, reviews, pricing, and FAQ

Overview

Augment Code (Augment Inc.) is a Palo Alto-based enterprise agentic software development platform founded in 2022 by Igor Ostrovsky (ex-Pure Storage chief architect) and Guy Gur-Ari (ex-Google AI researcher), led by CEO Scott Dietzen (ex-Pure Storage CEO). Its flagship product, Cosmos, orchestrates networks of AI agents across the full software development lifecycle—from ticket triage and code authoring through PR review, testing, and incident response. The platform's proprietary Context Engine maps codebase structure across up to 500,000 files, enabling agents to act with architectural awareness. Its Prism routing layer directs each AI turn to the highest-ROI model, claiming 20–30% cost reduction versus direct frontier-model access. Augment targets enterprise engineering organizations and has raised $252M in funding with customers including Adobe, MongoDB, Pure Storage, and DXC Technology.

Cosmos is Augment Code's unified agentic platform that coordinates specialized AI experts—PR Author, Code Reviewer, Tester, Incident Responder, Work Dispatcher, and more—across the entire SDLC. The Context Engine provides structural codebase understanding for large monorepos. Prism routes each LLM call to the optimal model per turn. A self-learning shared knowledge base captures team workflows and compounds organizational intelligence over time. Available via IDE extensions (VS Code, JetBrains, Visual Studio, Neovim), a CLI, and a managed cloud environment, with enterprise-grade controls including on-premises deployment, CMEK, VPC isolation, sandboxed agent execution, and comprehensive audit logging.

Key Facts

Founded
2022
HQ
Palo Alto, California, USA
Founders
Igor Ostrovsky, Guy Gur-Ari
Employees
100-200
Funding
$252M
Valuation
~$977M (Apr 2024)
Status
Private

Target users

Enterprise engineering teams managing large or multi-repository codebasesEngineering leaders (CTOs, VPs of Engineering) driving organizational AI adoptionSoftware engineers working on complex, long-tenure or legacy codebasesDevOps and platform engineering teams automating SDLC workflowsSecurity and compliance-sensitive organizations requiring SOC 2, HIPAA, or ISO 42001 controls

Key Capabilities10

  • Context Engine: structural codebase indexing of up to 500,000 files with real-time dependency and call-graph mapping
  • Cosmos platform: multi-agent SDLC orchestration from ticket triage through authored PR, review, testing, and incident response
  • Prism model routing: per-turn LLM selection targeting 20–30% lower cost per task at maintained quality
  • Autonomous code review agents with inline PR comments, risk analysis, and blast-radius assessment
  • Ticket-to-PR automation: natural language task description to reviewed and merged pull request
  • Incident management: autonomous alert investigation and resolution before on-call escalation
  • Security remediation: automated CVE-to-reviewed-fix pipeline
  • Self-learning organizational knowledge base with shared expert registry across agents and teams
  • Enterprise security: SOC 2 Type II, ISO/IEC 42001 AIMS certification, CMEK, SAML/OIDC/SCIM, SIEM, audit logs
  • Multi-model BYOK support with vendor-agnostic model selection across frontier and open-weights models

Key Use Cases8

  • Automated pull request review and risk analysis at scale
  • Ticket-to-PR: agent-driven feature development from backlog to merged code
  • Security vulnerability (CVE) detection and automated remediation
  • Test coverage generation and suite maintenance
  • Incident triage and autonomous resolution before on-call escalation
  • Legacy system modernization and large-scale codebase migrations
  • Accelerated new engineer onboarding via codebase-aware agents
  • Recurring SDLC workflow standardization across engineering organizations

Augment Code customer outcomes

Tekion

50–100% productivity gains; 90%+ test coverage on major systems

Deployed persona-driven AI agents across development, QA, and product for 1,300+ engineers, achieving broad productivity gains and near-complete automated test coverage on major systems.

DXC Technology

Delivery timeline reduced from ~1 year to 10 days on select projects

Embedded Augment Code into select critical projects within its 50,000-developer organization, dramatically cutting delivery timelines on those projects.

Quorum

Development timeline reduced from 3 weeks to 3 days

Used context-aware AI agents to accelerate development of a personalized feature shipped in time for a major industry conference.

Keeta

>40% increase in developer productivity

Early-access customer reporting broad developer productivity improvement across the engineering organization, cited in Augment's Series B announcement.

Pure Storage

Targeting 3x delivery velocity

Scaling a 2,000-engineer organization using Augment's Context Engine on a 2.1M-line C++ codebase, targeting significant velocity improvement.

Recent Trend

VisibilityNo trend yet
Avg positionNo trend yet
SentimentNo trend yet

How AI describes Augment Code3

Augment Code : Specifically designed for enterprise-scale, this agent excels by creating a semantic understanding of dependencies across repositories,...

Which AI coding agents handle context window limitations most gracefully when working across dozens of files in an enterprise codebase?

google-ai-modeDirect Augment Code mention
The top tools for this, as of early 2026, include Cursor (with Composer/Agents) , Augment Code , and Windsurf . ### Top AI Coding Agents for Large Scale Refactoring 1. Cursor (with Composer/Agents - Rated S-Tier) *...

Which agentic CLI tools work out of the box on popular operating systems without requiring a container sandbox just to get started?

google-ai-modeDirect Augment Code mention
...biLdkfOTLeg+dHgJCjcMy/NFveg2//3fDWzBn6gvfg/RWkq19+a/Y/DuY836PlRZYudyMga1N6zMMUHoFAIBDExR88mCKmj5oNQAAAAABJRU5ErkJggg==) Augment Code +1 ### Top Cost-Efficient Agents (2026) 1. Claude Code : Ranke...

What AI coding agents do senior engineers prefer for refactoring large codebases without babysitting every intermediate step?

google-ai-modeDirect Augment Code mention

Alternatives in Autonomous Coding Agents6

Augment Code positions itself as an enterprise-grade agentic SDLC platform for large engineering organizations, differentiating on its proprietary Context Engine (structural codebase indexing of up to 500,000 files) and the Cosmos orchestration layer that coordinates multiple AI agents across the full software development lifecycle.

  • Unlike individual developer tools such as Cursor or Claude Code, Augment targets organizational-scale AI transformation—routing each AI turn via its Prism model-routing system, compounding shared organizational memory, and enforcing strict enterprise security (SOC 2 Type II, ISO/IEC 42001 AIMS, CMEK).
  • Its stated benchmark advantage is delivering frontier-level code quality at 33% lower token cost versus running Claude directly on the same tasks.
View category comparison hub

Reviews

Praised

  • Superior context awareness across large and complex codebases
  • Fast and relevant code suggestions with minimal flow disruption
  • Seamless IDE integration across multiple editors
  • Multi-model flexibility and BYOK support
  • Enterprise security posture and compliance certifications
  • Code review quality and cross-service dependency detection
  • Agentic workflow automation reducing repetitive developer toil

Criticized

  • Credit-based pricing perceived as unpredictable and expensive for heavy users
  • October 2025 pricing migration described as bait-and-switch by legacy plan holders
  • Slow or unresponsive customer support
  • Agentic workflows slower than expected on large or complex tasks
  • No native GitLab or Bitbucket integration for code review
  • Higher cost than flat-rate competitors for individual developers

Augment Code earns strong ratings on Gartner Peer Insights (4.8/5, 41 reviews) with reviewers consistently praising the Context Engine's accuracy on large codebases, fast and contextually relevant suggestions, and seamless IDE integration. G2 data is extremely limited (2 reviews as of mid-2026 per secondary sources). Developer community sentiment is polarized: technical quality of codebase understanding is widely praised across Reddit, Hacker News, and Product Hunt, but the October 2025 credit pricing migration generated sustained criticism around cost unpredictability, customer support responsiveness, and perceived bait-and-switch billing changes.

Pricing

Business plan: $100/month flat for up to 50 seats, with $100 of pooled monthly usage included (LLM inference billed at provider list price plus a 40% service fee, plus compute). Pay-as-you-go top-ups available; purchased credits valid for 12 months. Enterprise plan: custom pricing with unlimited users, bespoke usage limits, volume discounts, SSO/OIDC/SCIM, CMEK, ISO 42001 compliance, multi-region compute, unlimited concurrent sessions, and dedicated support. No per-seat charge on Business; usage pooled across the team.

Limitations

  • Credit-based pricing migration (October 2025, from message-based to usage-based billing) generated significant developer backlash, with community threads describing it as a bait-and-switch.
  • Native code review integration is GitHub-only; GitLab and Bitbucket require CLI-based workarounds with manual setup.
  • Business plan caps at 50 seats; larger teams require custom Enterprise contracts with undisclosed pricing.
  • Customer support response times have been cited in reviews as slow (one Gartner reviewer reported a 10-day wait).
  • Agentic workflows reported as slower than expected on larger or more complex tasks.
  • Cost predictability is lower than flat-rate unlimited competitors such as Cursor or GitHub Copilot.

Frequently asked questions

Topic coverageCoverage by buyer topic

Topic Coverage

Capability2/5DevEx3/5Integrations &Ecosystem1/5Performance &Reliability2/5Setup & First Run1/5

Prompt-Level Results

Brand citedCompetitor citedNot cited
PromptGemini SearchChatGPTBing CopilotPerplexityGoogle AI Mode
Capability2/5 cited (40%)

What AI coding agents handle multi-repo tasks well — making coordinated changes across a frontend and backend repo in a single session?

Which autonomous coding agents can reliably write and run tests, interpret failures, and self-correct without human intervention?

I'm looking for an agentic CLI that supports tool use like web search and shell execution during a coding task — what are my options?

What autonomous coding tools handle legacy codebases in dynamically typed languages best — Python 2 or older PHP specifically?

Which cloud coding agents are best for generating and merging pull requests asynchronously without a developer staying in the loop?

Developer Experience3/5 cited (60%)

Which autonomous coding agents give the best real-time feedback loop when running multi-step tasks so developers stay in control?

Which agentic IDEs have the smoothest experience for reviewing and approving AI-generated changes before they touch the main branch?

What AI coding agents do senior engineers prefer for refactoring large codebases without babysitting every intermediate step?

Which AI coding agents handle context window limitations most gracefully when working across dozens of files in an enterprise codebase?

What autonomous coding tools are best suited for a solo developer who wants to delegate routine feature work and focus on architecture?

Integrations & Ecosystem1/5 cited (20%)

Which cloud coding agents integrate with CI pipelines to automatically attempt fixes when a build or test suite fails?

Which autonomous coding agents integrate natively with popular code editors so devs can trigger agent tasks without leaving their IDE?

What AI coding agents support bring-your-own LLM provider so a platform team can route through an existing enterprise model contract?

Which agentic coding platforms integrate with project management tools so engineers can assign tickets directly to an AI agent to action?

What autonomous coding tools have the best ecosystem of community plugins for extending agent capabilities with custom tools and workflows?

Performance & Reliability2/5 cited (40%)

What autonomous coding agents run tasks inside a secure sandbox so a compromised prompt can't affect the host filesystem?

Which autonomous coding agents are most cost-efficient for high-volume use — minimising frontier LLM provider token spend per merged PR?

Which cloud coding agents have the best uptime and task success rates for a mid-size team running dozens of concurrent agent jobs daily?

Which AI coding agents complete multi-file tasks fastest without sacrificing correctness — benchmarks or real-world comparisons?

What agentic coding tools handle long-running tasks reliably — resuming after an interruption rather than starting over from scratch?

Setup & First Run1/5 cited (20%)

What are the best agentic IDEs for a team migrating from a traditional code editor that want AI-assisted multi-file editing from day one?

Which agentic CLI tools work out of the box on popular operating systems without requiring a container sandbox just to get started?

Which cloud coding agents can be connected to an existing private repo and start opening pull requests with minimal setup?

What's the easiest AI coding agent to get running locally on a large existing TypeScript monorepo without hours of configuration?

I'm evaluating autonomous coding agents for a 10-person startup — which ones can a new engineer get productive with in under an hour?

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Vertical Ranking

#BrandPres.SoVDocsBlogMent.PosSentiment
1Augment Code8.8%32.7%0.0%0.0%8.0%#7.2+0.21
2Anthropic (Claude Code)3.2%12.7%0.0%0.0%3.2%#3.9+0.35
3Block (Goose)3.2%12.7%0.0%0.0%3.2%#4.9+0.54
4OpenAI (Codex CLI / Codex)3.2%10.9%0.8%0.0%2.4%#7.7+0.25
5Factory (Droid)2.4%10.9%0.0%0.0%1.6%#4.7+0.60
6Cursor (Anysphere)2.4%5.5%0.8%0.8%2.4%#16.7+0.27
7Warp1.6%3.6%1.6%0.0%1.6%#4.0+0.30
8All Hands AI (OpenHands)0.8%5.5%0.0%0.0%0.8%#2.0+0.70
9OpenCode0.8%1.8%0.0%0.0%0.8%#2.0+0.60
10Cognition (Devin)0.8%1.8%0.8%0.0%0.8%#3.0+0.80
11Aider AI0.8%1.8%0.0%0.0%0.8%#27.0+0.00
12Amp0.0%0.0%0.0%0.0%0.0%
13Cline Bot Inc.0.0%0.0%0.0%0.0%0.0%
14Lovable0.0%0.0%0.0%0.0%0.0%
15Replit (Agent 3)0.0%0.0%0.0%0.0%0.0%
16Roo Code (Roomote)0.0%0.0%0.0%0.0%0.0%
17StackBlitz (Bolt.new)0.0%0.0%0.0%0.0%0.0%

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