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Repsol

Enterprise Knowledge Search · IT Operations Automation

Repsol wanted to help employees reinvent workflows, improve working models, and raise productivity in complex internal processes as part of its broader digitalization program.

22 of which have already been scaled up
more than 50 people have been working collaboratively, supervising teams of AI agents
more than 60 use cases in production
providing support for more than four months

Reported impact

Business outcome

Repsol reports that the platform helps teams create new collaborative environments, improve working models, and boost productivity while supporting relevant departmental processes with supervised multi-agent workflows.

  • Productivity Improvement

Solution

Repsol built its own intelligent agent construction platform as a pilot for agent-based AI and multi-agent systems, developed in collaboration with Accenture for the Digitalization and Services department. The platform acts as an orchestration layer where teams can configure AI agents into workflows so the agents can self-organize, coordinate, and perform task groups tied to real departmental processes. To ground those agents in team-specific context, Repsol created large knowledge bases designed exclusively for the teams using the system. It also introduced an interface that adapts language models to a professional, collaborative environment so employees can work with and supervise agent teams rather than use isolated chat interactions. In production use, three teams deployed 22 AI agents, and more than 50 people collaborated with and supervised the agents over more than four months, making the platform a structured internal environment for workflow support and productivity improvement.

Data flow

Team knowledge is organized into dedicated knowledge bases, the intelligent agent construction platform uses that context to configure AI agents into workflows, and employees interact with and supervise the agent teams through an interface that adapts language models to professional collaboration.

Solution architecture

4 components · 4 layers
  1. Compute
    • language models Underlying models are adapted to a professional and collaborative environment for agent-assisted work.
  2. Storage
    • large knowledge bases Dedicated knowledge repositories provide team-specific context so AI agents can support relevant work processes.
  3. Serving
    • innovative interface The interface gives employees a collaborative workspace to work with and supervise teams of AI agents.
  4. Orchestration
    • intelligent agent construction platform Repsol's in-house platform lets teams configure AI agents into workflows for collaborative task execution.

Architecture clues

  • Agents are designed to self-organize, coordinate, and perform tasks
  • Developed in collaboration with Accenture
  • Human users supervise agent teams during live process support
  • Multi-agent system organizes agents into workflows
  • Pilot deployed in Repsol's Digitalization and Services department, which has more than 700 employees
  • Team-specific knowledge bases were created to support agent operation

Evidence from the source

"It has also designed an innovative interface that adapts language models to a professional and collaborative environment."
"Since the launch of its Generative AI Competence Center more than two years ago, Repsol has positioned itself as a pioneer in the global energy sector in the use of generative AI, accumulating experiences and tangible results: more than 60 use cases in production, 22 of which have already been scaled up, and the training of more than 5,000 employees in generative AI or advanced prompting techniques."
"Subsequently, more than 50 people have been working collaboratively, supervising teams of AI agents, thus, achieving numerous efficiencies and improvements."
"The company has taken another step forward in its digitalization process by launching a platform for building intelligent agents that makes it easier for employees to reinvent workflows and improve their productivity."
"The pilot has been carried out in Repsol's Digitalization and Services department, which has more than 700 employees and where three teams with 22 AI agents have been deployed, providing support for more than four months in processes relevant to the area."
"The platform, an innovative pilot project developed in collaboration with Accenture, facilitates the configuration of artificial intelligence agents that are organized into workflows."
"Throughout the planning and development of the multi-agent system, Repsol has created large knowledge bases designed exclusively for teams to get support from the AI agents."
Transparency Evidence & methodology 70/100 reference value · 92% 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 value70/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    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 scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence92%
  • Source authority25/25
    The deployment is described by the adopting organization or a government source.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    7 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    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. Repsol

    Repsol customer story

    First-party
    https://www.repsol.com/en/press-room/press-releases/2025/repsol-launches-intelligent-multi-agent-platform-consolidating-global-ai-leader-energy-sector/index.cshtml
    Source type
    Customer-published story
    Published
    Oct 15, 2025
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Oct 15, 2025 · Repsol · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.