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Google Cloud Energy Germany

Proxima Fusion

To optimize these designs without building highly expensive physical prototypes, Proxima Fusion relies heavily on specialized simulations. However, running these simulations was previously a lengthy process, with individual simulations taking hours or even days to execute.

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Because the agent operates directly on internal engineering systems, monitoring for safety and security issues, like leaking intellectual property or compromising data, is essential. Proxima Fusion used Gemini to build a secondary, independent reviewer agent on the platform. This model acts as a built-in safeguard, inspecting every command the agent intends to execute. If an action looks unfamiliar or attempts to alter a critical database, the reviewer agent pauses the run and messages a human engineer in Slack for explicit approval, ensuring researchers maintain complete control over the design loop.
Proxima Fusion acts as a responsive gateway, processing the message in under a second. To manage the heavy computational workloads that complex physics models require,
Proxima Fusion which aims to put fusion electricity on the grid by the 2030s, with its own working power plant.
To achieve this, Proxima Fusion is designing and building stellarators, which use complex magnetic coils to trap plasma heated to 100 million degrees Celsius.

Reported impact

Business outcome

Automates complex physics simulations to run around the clock; Frees engineering experts to refine core hardware designs; Verifies autonomous tool actions with dedicated review agent; Uncovers data gaps to continuously improve historical dataset.

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Proxima Fusion uses Gemini Enterprise Agent Platform to build engineering agents, accelerating the path to clean fusion energy. To optimize these designs without building highly expensive physical prototypes, Proxima Fusion relies heavily on specialized simulations. However, running these simulations was previously a lengthy process, with individual simulations taking hours or even days to execute. While the company already used extensive automation around its simulations, it needed a way to multiply the number of simulations a user could orchestrate. Interpreting the data and relaunching simulations also required a considerable amount of expert input from the Proxima Fusion team, which slowed its cycle speeds down.

Solution architecture

7 components · 4 layers
  1. Ingest
    • Pub/Sub Moves high-volume events and API traffic between services so the workflow stays decoupled and resilient.
  2. Compute
    • Cloud Run Runs serverless application services and APIs with automatic scaling for bursty workloads.
    • Compute Engine Runs serverless or virtualized application services with elastic scale for production AI workloads.
    • Gemini Powers natural-language reasoning, summarization, generation, or assistance inside the workflow.
  3. Storage
    • Cloud SQL Stores transactional state and application data for the operational workflow at low latency.
    • Cloud Storage Stores large files, lake data, or static assets that feed downstream analytics and applications.
  4. Orchestration
    • Gemini Enterprise Agent Platform Coordinates multi-agent workflows, tool calling, and governed automation across the application.

Architecture clues

  • Cloud Run
  • Proxima Fusion uses Gemini Enterprise Agent Platform to build engineering agents, accelerating the path to clean fusion energy.
  • To bring AlphaClaw to life, Proxima Fusion engineers worked side by side with Google technical specialists over a four-week project. Operating as a single team, they ran daily standups to align their goals and held a dedicated hackathon at
  • We found Gemini models highly capable at navigating technical code frameworks and executing complicated developer steps. They reason through our parameters accurately, which means our engineers can let the agent manage long-running simulati
  • When a researcher types a natural language request into Slack to initiate a simulation,
  • with Gemini agents

Evidence from the source

Automates complex physics simulations to run around the clock
Frees engineering experts to refine core hardware designs
Proxima Fusion uses Gemini Enterprise Agent Platform to build engineering agents, accelerating the path to clean fusion energy.
Proxima Fusion which aims to put fusion electricity on the grid by the 2030s, with its own working power plant.
Uncovers data gaps to continuously improve historical dataset
Verifies autonomous tool actions with dedicated review agent
Transparency Evidence & methodology 60/100 reference value · 75% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value60/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth15/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence75%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity6/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Google Cloud

    Proxima Fusion customer story

    First-party
    https://cloud.google.com/customers/proximafusion
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • Sitemap last modified Jul 22, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
  • Unverified extracted date Jul 22, 2026 · Google Cloud · Unknown Legacy extracted date without source-page confirmation.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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