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At RAISE Summit Paris on July 8, 2026, Cognition CEO Scott Wu described two new Devin launches as the most significant upgrade to the platform since its debut. Both shipped on July 1 — one targeting your security backlog, the other targeting your AI coding budget.

Here’s what they do and when to use each.

Devin Security Swarm: Agentic MapReduce for Vulnerability Backlog

The problem Security Swarm is solving is specific: AI coding tools have been generating code faster than security teams can audit it. Most enterprises running Devin, Claude Code, or Cursor at scale are accumulating a security debt they can’t clear with traditional scanners — which catch pattern-matched issues but miss the logic-level vulnerabilities that chain across services.

Security Swarm is Cognition’s answer. It runs multiple Devin agents in parallel across your codebase using an agentic MapReduce architecture: parallel scan agents reason across files simultaneously to catch authentication bypasses, business logic gaps, and cross-service chains that require holding multiple files in context at once.

How the Architecture Works

The scan-verify-patch loop has three steps:

  1. Parallel scan: Devin agents fan out across files and modules. Each agent specializes on what it’s reading — one might hold your auth middleware, another the session management layer, another the API gateway. The parallel spread is what lets the swarm catch vulnerabilities that span subsystems.

  2. Sandbox confirmation: Every finding is reproduced in an isolated sandbox before it’s reported. Devin only surfaces vulnerabilities it can actually trigger at runtime. This is the key differentiator from static analysis tools: you’re getting confirmed-exploitable issues, not pattern-match guesses.

  3. Patch + PR: For every confirmed vulnerability, Devin writes the fix and opens a pull request. You review and merge.

What the Benchmarks Show

Cognition ran Security Swarm against 50 real-world vulnerabilities tied to published GitHub Security Advisories across 14 languages:

  • 72% recall — Devin found 36 of the 50 vulnerabilities, the highest of any tool tested
  • 30% lower cost per verified finding than the next most accurate alternative
  • Three critical vulnerabilities only Devin found: a PHP sandbox bypass via template injection, an argument injection through metadata value parsing, and an overly broad deserialization surface in Spring Kafka

The categories missed by other scanners — the ones Devin caught — are all logic-level issues requiring cross-file reasoning. PHP template injection chains through multiple template rendering layers. Argument injection exploits metadata handling the scanner didn’t trace. Spring Kafka deserialization is a well-known risk class but requires understanding the specific configuration context to flag correctly.

Access and Pricing

~$90 per scan. Available globally to enterprise customers.

For teams with a significant backlog, Cognition also offers the Devin Security Vulnerability Remediation Program — a structured six-week engagement to assess your application security posture and clear the existing queue. This is consultant-hours-plus-Devin, not self-service.

Scan profiles can be generated directly from existing threat model documentation. Scans run on a daily, weekly, or custom schedule without per-repository configuration.

When to Use It

Security Swarm is the right tool when:

  • You have a vuln backlog and want to automate triage and patch generation, not just discovery
  • You need logic-level coverage your SAST tools miss (cross-service chains, business logic bypasses)
  • You generate a lot of code via AI assistants and want security to keep pace
  • You can operate with enterprise pricing (~$90/scan at scale)

It is not a replacement for a security team. Devin writes patches — you still review them. The confirmation sandbox reduces false positives, but you own the merge decision.

Devin Fusion: Frontier + Sidekick at 35% Lower Cost

The core insight behind Fusion is that most of what Devin does in a session doesn’t need frontier-level intelligence. Planning? Yes. Handling ambiguity? Yes. Multi-file architectural reasoning? Yes. But routine file modifications, boilerplate generation, and incremental progress on clearly-scoped subtasks — these don’t need to run on the most expensive model in your stack.

Fusion expresses that insight as infrastructure.

The Hybrid Harness Architecture

Fusion pairs two agents:

  • Frontier agent: Owns planning, handles ambiguity, does final review, and manages the session context. This is Devin’s existing agent, running on a high-capability model.

  • Sidekick agent: A smaller, cheaper model running in parallel. The frontier agent delegates routine subtasks to the sidekick and monitors its progress. The sidekick does the mechanical work; the frontier does the thinking.

The routing is dynamic mid-session, not predetermined. This matters because the difficulty of engineering tasks often reveals itself late. A “simple bug fix” turns into a multi-file race condition investigation. Fusion can shift work back to the frontier model when complexity escalates — including around context-compaction moments, where switching models can be significantly cheaper than holding expensive context.

What the Numbers Show

  • 35% cost reduction vs. running Devin at full frontier level for equivalent tasks
  • 88% of Cognition’s own merged PRs are now driven by the Fusion router — Cognition runs Fusion on their own codebase in production

The 88% internal adoption number is notable. It means Cognition’s engineering team trusts Fusion’s routing decisions enough that it’s the default for nearly all of their AI-generated PRs. That’s a stronger signal than a vendor benchmark — it’s the team that built the tool using it on their own code.

Access

Fusion is available in preview at app.devin.ai/signup.

When to Use It

Fusion makes sense when:

  • You run Devin at significant scale and cost is a real consideration
  • Your tasks are a mix of routine and complex work (most engineering queues are)
  • You want the insurance of frontier-level quality without paying frontier rates across the board
  • You can tolerate preview-tier reliability while the router matures

The tradeoff: Fusion’s quality depends on the router making good decisions about when to escalate. For the 88% of PRs Cognition routes entirely through Fusion, it works. For the 12%, a human presumably catches what the sidekick misses in review. Your merge-time review process is the safety net.

The Bigger Pattern: Cognition Is Building a Platform, Not a Tool

Scott Wu framing these launches as “most significant upgrade since debut” is deliberate positioning. The original Devin pitch was “AI software engineer” — one agent, one task, one human in the loop. What’s emerged is more layered:

  • Security Swarm = parallel agent orchestration for specialized domain work
  • Fusion = hierarchical agent orchestration for cost efficiency
  • Devin’s $492M ARR and 89% self-coding stat (from the May 2026 funding round) = the flywheel: Devin’s training data is Cognition’s own production codebase

The MapReduce scan pattern in Security Swarm is the same architecture that makes large-scale agentic coding viable at all: fan out to parallelize, coordinate to synthesize, confirm before surfacing. Fusion is a narrower version of the same idea applied to cost optimization rather than coverage.

Builders who adopt Devin for coding are now getting access to the same orchestration patterns Cognition uses internally — which is the strongest argument for why these tools work.

Builder Decisions

Situation What to Use
Security backlog, want patched PRs not just reports Devin Security Swarm
Running Devin at scale, cost is growing faster than value Devin Fusion (preview)
Need SAST-style pattern matching Traditional tools — Security Swarm targets logic-level issues
Need frontier quality on every line Skip Fusion, use Devin directly
Single-repository, small team Security Swarm at $90/scan may not pencil out; evaluate first

If you’re already using Devin, Fusion is a zero-downside try — preview access, same interface. If you have a growing vulnerability backlog from AI-generated code, Security Swarm addresses the problem that conventional scanners don’t: the logic-level issues AI assistants are most likely to introduce.