Factory AI and Augment Cosmos solve different problems: Factory's Droids execute development tasks such as coding, reviewing, and testing, while Cosmos coordinates and governs fleets of agents across a team's entire software development lifecycle. Most engineering organizations end up needing both, not one instead of the other.
TL;DR
Factory ships autonomous coding Droids for feature work, migrations, review, and testing, pulling context from GitHub, Notion, Linear, Slack, and Sentry. Augment Cosmos provides coordinated, durable agent sessions that span the software development lifecycle. Choose based on whether your missing capability is task execution or fleet-wide coordination, and know that a team can reasonably run both.
Why This Isn't Really an Either/Or Decision
I get asked some version of this question constantly: "Should we buy Factory or build toward something like Cosmos?" The honest answer is that the question itself assumes the two products compete for the same budget line, and in practice they don't. Factory Droids read a ticket and ship a pull request. Augment Cosmos, a unified cloud agents platform, sits underneath a team's agents and holds the shared context, memory, and policy those agents need when more than one of them is running at once.
That split shows up in how the money actually moves. Menlo Ventures' 2025 enterprise AI report allocated $19 billion of 2025 spend to the application layer, where user-facing tools like Droids live, and $18 billion to the infrastructure layer, which covers foundation models, training, and the orchestration tooling that connects agents to enterprise systems. Factory's Droids execute development tasks, which are application-layer purchases. Cosmos provides runtime coordination for a fleet of agents, which is closer to the infrastructure side of that split. Treating them as a single decision usually means shortchanging whichever one is evaluated second.
This comparison covers Factory's product scope and evidence of adoption, including its funding history and named customers, as well as Cosmos's architecture for coordinating concurrent agent sessions. It also lays out where the two actually split and how to decide which one your team is missing.
The Agentic SDLC
How teams like Stripe, Ramp, and Uber move from solo coding agents to a coordinated, team-level system.
What Factory AI Ships and Why Enterprises Are Buying It
Factory is an application-layer platform for development-cycle automation. Its Droids pull context from GitHub, Notion, Linear, Slack, and Sentry to turn tickets into merged code. A $50 million Series B in September 2025, at a $300 million post-money valuation, led by NEA with Sequoia Capital, J.P. Morgan, and Nvidia participating, backs the company's momentum, and Factory closed a further $150 million Series C in April 2026 at a $1.5 billion valuation led by Khosla Ventures.
Enterprise customers, including MongoDB, Ernst & Young, Zapier, Bilt Rewards, Clari, and Bayer, back Factory's adoption record, according to NEA's own writeup of the round. NEA describes the scope of what Droids automate as "feature development, migrations, modernization projects, code review, and testing," extending beyond coding alone to encompass the surrounding SDLC work.
Droids automate development-cycle tasks across several areas:
| Use Case | What the Droid Does |
|---|---|
| Ticket to Code | Reads tickets, understands codebase context, implements changes, writes tests, opens PRs autonomously |
| Code Review | Reviews every PR for bugs, security issues, and style violations |
| QA / Test Generation | Analyzes code changes, generates targeted test cases and runs against CI |
| Incident Response | Pulls logs, traces, and recent deploys to identify the root cause |
| Documentation | Updates READMEs, API docs, and architecture guides on codebase changes |
Two verified case studies back these outcomes with real numbers rather than marketing copy. Empower, a fintech company, cut average incident response time by up to 40% after adopting Factory's Review Droid and platform. Nav, another fintech customer, reported a 60% reduction in context-switching time and 2x faster feature development cycles after unifying its engineering context through Factory.
Factory's Missions feature extends single-agent work into multi-agent coordination: given an instruction like "migrate this PHP codebase to TypeScript," a Droid breaks the project into features, assigns work to Droid worker sessions, and coordinates handoffs through git. That coordination stays scoped to Factory's own Droids, which matters when you get to the question of cross-vendor fleet coordination later in this comparison.
Factory's legacy-modernization coverage also stands out. Its Legacy-Bench benchmark targets COBOL, Fortran, and Java 7 workloads, since, as Factory has pointed out, most coding-agent benchmarks evaluate agents only on modern Python and JavaScript. For financial institutions and government contractors sitting on decades-old codebases, that's a real gap in how the rest of the industry measures agent quality, and Factory's benchmark speaks directly to it.
What Augment Cosmos Provides and Why Teams Are Adopting It
Augment Cosmos is generally available and included on all paid plans. It coordinates agent work through what Augment calls Environments, Experts, and Sessions: Environments hold the shared context a team's agents draw on, Experts are the specialized agents doing the work, and Sessions are the durable, days- or weeks-long runs that keep memory and progress intact between them. Underneath all three sits the Context Engine, which indexes 400,000+ files using semantic dependency analysis, so every agent starts from an architectural understanding of the codebase rather than re-deriving it from scratch each time.
Augment has reported a 70.6% SWE-bench Verified result for a harness built on that Context Engine, building on an earlier 65.4% open-source SWE-bench Verified submission that used off-the-shelf models, which Augment's own tuning improved further.
I've watched a hallucination-prone implementation task get resolved not by a bigger model but by Augment's Prism model router quietly shifting the work to a model better suited to that specific task, without anyone on the team noticing or stepping in. That kind of behind-the-scenes coordination is the thing Cosmos is actually selling: not a single smarter agent, but infrastructure that makes a fleet of them behave consistently.
Where the Architecture Actually Splits
Factory Droids execute software development tasks at the application layer. Cosmos provides the runtime, shared memory, and governance underneath concurrent agent sessions, with a design target of hundreds of concurrent agents per organization and sessions that stay durable across days- and weeks-long runs. That's what makes the two products complementary rather than competing: one does the work; the other provides the context and rules that the work runs under.
The CI/CD control-plane analogy is the clearest way to think about this. A pipeline doesn't write your code; it decides what runs, in what order, and what has to pass before a change ships. Cosmos plays that role for a fleet of agents, holding the shared context and the rules those agents operate. Factory Droids consume that kind of infrastructure as task-executing agents rather than supplying it themselves.
For autonomous code execution specifically, Factory's own reported results on SWE-bench Full and SWE-bench Lite are worth examining directly, and independent coding-agent benchmarks let you compare Factory against Devin, Claude Code, and GitHub Copilot on a level playing field. For runtime, memory, context, observability, and governance, teams evaluating Cosmos are typically comparing it against a DIY orchestration build rather than another packaged product, since few vendors compete directly at that layer yet.
Comparing Factory AI and Augment Cosmos Across Five Dimensions
The buying distinction between these two platforms is clearest in how each handles scope, customer profile, data ownership, multi-agent scale, and observability.
| Dimension | Factory AI (Application Layer) | Augment Cosmos (Infrastructure Layer) |
|---|---|---|
| Use case scope | SDLC tasks: coding, review, testing, incident response | Coordination for agent fleets, designed for hundreds of concurrent agents per organization across the SDLC |
| Customer profile | Teams needing autonomous coding agents | CTOs and platform leads coordinating agent fleets as Fortune 500 average deployments scale |
| Data plane ownership | Single-tenant VPC, Zero Data Retention on Business plan | Runs inside the customer's own perimeter, on customer keys |
| Multi-agent scalability | Missions coordinate Droids via git | Four coordination patterns for concurrent agent sessions |
| Observability | OpenTelemetry scoped to Factory's own Droids | Every action emits a structured event as a platform property |
Use Case Scope
Factory Droids read a ticket and ship a pull request. Cosmos supplies the environment for long-running work that spans multiple tools, commands, and files. The New Stack frames Cosmos's coordination role as covering "triage, spec, implementation, review, testing, deployment, and feedback, coordinating with one another and bringing in a human when judgment matters," which is a genuinely different scope than a single Droid completing one ticket.
That distinction matters most once work spans more than one session. When I ran Auggie CLI against a multi-file refactor that touched five services, the agent planned the sequence up front, executed the approved terminal commands itself, and pulled in context from files I hadn't even opened yet, so I never had to manually stitch the steps back together between tools.
Customer Profile
Factory targets engineering teams that need autonomous coding capacity, particularly compliance-heavy enterprises and organizations carrying legacy codebases. Cosmos targets CTOs, VPs of engineering, and platform leads who need to coordinate fleets of agents rather than manage one at a time. The scale pressure behind that is real: Gartner projects that the average Fortune 500 enterprise will run more than 150,000 AI agents by 2028, up from fewer than 15 in 2025. At that scale, the question stops being which agent to buy and starts being how to govern the agents you already have.
When I used Augment Code to get oriented in a service I'd never touched before, Context Engine surfaced the conventions and architectural relationships I would otherwise have had to piece together from scattered tickets and a senior engineer's memory, and it pointed me straight at the parts of the codebase that actually mattered for the task in front of me.
Data Plane Ownership
Factory offers a sandboxed single-tenant environment with a dedicated VPC, AES-256 encryption at rest, and Zero Data Retention on its Business plan. Its GitHub Action checks out code transiently and discards it after execution, auto-revoking tokens after each run. Cosmos runs inside the customer's own perimeter on customer keys, with Augment describing the resulting audit trail as a property of the platform itself rather than something bolted on afterward.
On certifications, Factory holds SOC 2 Type I (certified April 8, 2024) and ISO 42001, and is GDPR- and CCPA-compliant; Augment Code holds SOC 2 Type II and ISO/IEC 42001. Type I is a point-in-time assessment rather than a continuous operational audit, so regulated-industry buyers should confirm directly with Factory whether it has since issued a Type II report. Worth noting either way: a Cloud Security Alliance analysis of non-human identity governance points out that SOC 2 certification on its own doesn't validate how well a platform actually governs agentic identities and permissions, a caveat worth raising with either vendor during procurement rather than assuming certification alone covers it.
Multi-Agent Scalability
Factory coordinates its Droids through the Missions feature and Droid Computers, using git handoffs between worker sessions. Cosmos's coordination patterns include supervisor, pipeline, hierarchy, and event-driven models, with sessions durable across days- and weeks-long runs. Factory's coordination stays within its own Droids; Cosmos is built to coordinate agents across teams and vendors.
That distinction lines up with broader research on multi-agent coordination. Redis's research on AI agent orchestration found orchestrated multi-agent approaches produced actionable recommendations 100% of the time, compared with just 1.7% for uncoordinated single-agent systems, with roughly 80 times the improvement in action specificity and 140 times the improvement in solution correctness. Numbers like that are why platform leads increasingly evaluate shared coordination infrastructure rather than treating each agent run in isolation.
Observability Independence
Factory natively integrates OpenTelemetry with Datadog, Honeycomb, and Grafana, but that observability stays scoped to Factory's own Droids rather than extending across other agents in a stack. Agent observability differs under Cosmos, where every action emits a structured event rather than relying on instrumentation bolted on after the fact. When teams run agents from two or more vendors, tool-scoped observability forces someone to manually reconcile separate event streams across multiple agent platforms; platform-native auditability avoids that reconciliation step because the audit trail already spans agent boundaries.
How to Choose Between Factory AI and Augment Cosmos
Choose Factory when you need Droids to complete development tasks such as pull requests, reviews, tests, and documentation updates. Choose Cosmos when you need runtime coordination, shared memory, policy, and observability across an agent fleet. Teams that need both can run Factory Droids as task executors while using Cosmos for memory, policy, observability, and coordination across the whole fleet.
| Choose Factory AI if you need | Choose Augment Cosmos if you need |
|---|---|
| Autonomous agents that read tickets and ship pull requests | A control plane coordinating agent fleets across the software development lifecycle |
| Legacy modernization for COBOL, Fortran, or Java 7 codebases | Shared context and memory that persist patterns, conventions, and corrections across sessions, durable for days or weeks |
| Model-agnostic coding execution with pre-configured MCP servers | Governance and observability are built into the platform structure |
| A single-purpose coding agent in a sandboxed single-tenant VPC | Environments, Experts, and Sessions coordinating concurrent agent work under organization policies |
Factory supports 40+ pre-configured MCP servers for model-agnostic code execution, alongside AES-256 encryption at rest and compliance materials that reference SOC 2 and ISO 42001.
These criteria separate the execution decision from the platform decision. Factory maps to the agent that performs the development work; Cosmos maps to the runtime, memory, policy, and auditability that surround agent fleets. Most 2026 enterprise AI budgets end up split the same way: buy the compliance-heavy execution layer, then build or buy the coordination layer that governs it.
The factory's own documentation directly supports this layering. It describes Droid Exec for running Droids non-interactively inside CI/CD pipelines, meaning Factory designed its coding agent to plug into a broader orchestration layer rather than assuming it would be the only thing running.
Match the Layer to the Problem You Actually Have
Start by mapping where your agent output actually breaks down. If the gap shows up at generation, when nobody's shipping code fast enough, Factory's Droids deliver pull requests, code review, tests, and incident-response workflows, and the named customers and funding history back that positioning. If the gap shows up across separate sessions, it means context and decisions get lost every time an agent's work ends. The missing pieces are shared context, memory, and enforceable policy, and that's Cosmos's territory.
Cosmos reuses Context Engine analysis across every agent session it runs, carrying that architectural understanding forward instead of resetting it each time a task ends.
Frequently Asked Questions About Factory AI and Augment Cosmos
These are the questions engineering leaders ask when they're deciding whether they need a coding agent, a coordination layer, or both.
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Written by
Paula Hingel
Technical Writer
Paula writes about the patterns that make AI coding agents actually work — spec-driven development, multi-agent orchestration, and the context engineering layer most teams skip. Her guides draw on real build examples and focus on what changes when you move from a single AI assistant to a full agentic codebase.