Iberdrola enhances IT operations using Amazon Bedrock AgentCore

Iberdrola, one of the world’s largest utility companies, has embraced cutting-edge AI technology to revolutionize its IT operations in ServiceNow. By using different agentic architectures, Iberdrola has transformed the way thousands of change requests and incident tickets are managed, streamlining processes and enhancing productivity across departments.

Through its partnership with AWS, Iberdrola implemented those agents in a groundbreaking solution using Amazon Bedrock AgentCore, targeting three key areas: optimizing change request validation in the draft phase, enriching incident management with contextual intelligence, and simplifying change model selection using conversational AI. These innovations reduce bottlenecks, help teams accelerate ticket resolution, and deliver consistent and high-quality data handling throughout the organization.

Amazon Bedrock AgentCore helps Iberdrola deploy production-ready AI agents seamlessly. With serverless compute capabilities, robust security, and integrated observability, the platform helps Iberdrola scale solutions across departments while adhering to enterprise-grade reliability and compliance standards.

Challenges with change and incident management

Iberdrola has simplified the multi-phase process of change management using AI-powered validation. A group of orchestrated agents make sure requests align with intended modifications while formatting and verifying mandatory fields in real time. This optimized approach avoids manual resubmissions and drastically reduces processing times, helping teams focus on driving impactful outcomes.

Using a swarm of agents to perform contextual enrichment, Iberdrola’s networking department now processes incidents faster and with greater precision. This enrichment lets technicians access configuration item details, review related historical incidents, and categorize tickets by environment and alert types, enhancing response times and enabling teams to swiftly address critical issues.

Solution overview

Iberdrola establishes its agentic AI practice through a layered architecture that separates operational concerns while enabling seamless integration across IT workflows. ServiceNow serves as the primary input source, and a MicroGateway provides intelligent routing to direct requests to relevant agents. A dedicated data layer maintains enterprise information, processing raw ServiceNow data through extract, transform, and load (ETL) pipelines for agent consumption.

The architecture comprises three layers:

• Agentic AI resources – This layer encompasses all agent deployments, Model Context Protocol (MCP) servers for standardized data access, authentication mechanisms, and memory objects that maintain contextual information. The design enables domain-specific agent development while sharing common infrastructure services.
• Inference layer – A streamlined abstraction provides large language model (LLM) inference capabilities from the organization’s portfolio of integrated models. This layer provides consistent model access patterns while supporting experimentation without requiring agent modifications.
• Data layer – A comprehensive information foundation contains operational data, analytical datasets, and transactional records. This layer enriches agent capabilities by providing access to historical patterns, real-time operational status, and contextual information necessary for intelligent decision-making.

This design enables three distinct use cases that address different operational challenges:

• Enhanced change management validation – The first implementation supports the draft phase of Iberdrola’s change management process through a deterministic agentic workflow. Multiple specialized agents work in sequence to validate change model appropriateness and verify that mandatory fields contain correctly formatted information. When validation errors are detected, the system provides clear feedback to requesters before allowing progression to subsequent phases.
• Intelligent incident enrichment – The incident management solution demonstrates multi-agent orchestration for Iberdrola’s Networking department. A master agent receives each incident and selectively engages specialized agents for tagging, contextual enrichment, similarity detection, and change impact analysis. This adaptive approach assists technicians by categorizing incidents, identifying related historical cases, and extracting configuration item details.
• Conversational change model assistant – The third use case addresses the complexity of selecting appropriate change models through a conversational AI assistant. The agent collects information about technology families, change objectives, and deployment environments to recommend suitable change models. The system provides clickable recommendations that open pre-filled change forms, streamlining the change request process.

The conceptual architecture translates into a production-ready implementation through Amazon Bedrock AgentCore, which provides managed primitives for building and deploying enterprise AI agents. The serverless approach of Amazon Bedrock AgentCore enables Iberdrola to focus on agent logic rather than infrastructure management while providing scalability and operational reliability.

Amazon Bedrock AgentCore components

AgentCore Runtime serves as the foundation for agent deployment, accepting containerized agents built with any framework—in Iberdrola’s case, LangGraph—and deploying them through Amazon Elastic Container Registry (Amazon ECR) repositories. AgentCore Runtime maintains serverless characteristics, scaling based on request volume while providing session isolation. Each agent session can run up to 8 hours for complex workflows. Logs and metrics generated by AgentCore Runtime are automatically captured by AgentCore Observability. In addition, Iberdrola has configured explicit logging to their self-hosted Langfuse instance for centralized monitoring.

AgentCore Memory provides contextual continuity across agent interactions by maintaining memory objects per agent session. Using the memory object, agents can store and retrieve session state, conversation history, and intermediate processing results. This capability is essential for Iberdrola’s multi-step workflows where agents must maintain context across validation phases or incident enrichment processes.

AgentCore Gateway simplifies tool integration by acting as an MCP server that “MCPifies” external tools and services. Rather than requiring custom integration code for each data source, AgentCore Gateway provides standardized interfaces that agents can consume consistently. This approach is particularly valuable for Iberdrola’s ServiceNow endpoint connections.

AgentCore Identity manages both inbound and outbound authentication flows, integrating with Entra ID through OAuth 2.0 protocols. For inbound requests, AgentCore Identity validates bearer tokens and authorizes access to underlying resources. For outbound operations, it handles token acquisition and manages secure communication with downstream tools.

AgentCore Observability captures telemetry data from agents using OpenTelemetry standards and surfaces this information through Amazon CloudWatch. This integration provides comprehensive monitoring of operational metrics without requiring additional instrumentation.

Technical implementation

The implementation uses LiteLLM as a proxy layer for consistent access to Amazon Nova and Anthropic Claude models through Amazon Bedrock and various other models. This abstraction enables agents to interact with different model variants using standardized API calls while Amazon Bedrock Guardrails provides safety controls for model outputs.

The architecture addresses Iberdrola’s enterprise security requirements through a virtual private cloud (VPC) configuration within AgentCore Runtime, so agents can securely access internal resources while maintaining network isolation. VPC endpoints provide secure communication with internal data sources without exposing traffic to the public internet.

Users initiate requests through ServiceNow, which communicates through a REST API to the MicroGateway that routes requests to appropriate use case agents. The data architecture implements a hybrid approach combining real-time operational access with enriched analytical datasets. Raw ServiceNow data flows through ETL processes into Amazon Simple Storage Service (Amazon S3) storage, then into Amazon Relational Database Service (Amazon RDS) databases enhanced with pgvector extensions for semantic search.

The logs and metrics generated by the agents deployed in AgentCore Runtime can be monitored using AgentCore Observability. In addition, Iberdrola uses self-hosted Langfuse on Amazon Elastic Kubernetes Service (Amazon EKS) for a holistic view of spans and traces generated by the LLMs and the agents.

Use case details

In this section, we discuss the implementation of two use cases mentioned earlier: enhanced change management and intelligent incident management.

Enhanced change management

The first use case demonstrates an agentic workflow that supports the draft phase of Iberdrola’s change management process through sequential agent execution within a single AgentCore Runtime. The workflow processes change requests through four specialized agents—Rule Extractor, Content Validator, AIM Model Analyst, and Phase Transition—with each agent receiving context from the previous step.

The implementation consists of the following key components:

• Single runtime context flow – Agents operate within one AgentCore Runtime instance, maintaining seamless context and session state across the entire validation pipeline
• LangGraph orchestration – Agents are defined as a graph structure, enabling visual workflow representation, conditional branching based on validation results, and comprehensive audit trails
• Vector-enhanced validation – Pgvector-enabled PostgreSQL supports semantic similarity searches, enabling the AIM Model Analyst agent to match change models based on technical descriptions rather than keyword matching
• Consistent processing – Change requests follow identical validation steps, meeting compliance requirements and quality standards

Intelligent incident management

The second use case demonstrates intelligent multi-agent orchestration for incident management, where a Smart Solver Agent analyzes incoming incidents and selectively engages specialized agents based on contextual needs. This implementation adapts processing steps to each incident’s unique characteristics, optimizing resource utilization while providing comprehensive enrichment when needed.

The implementation consists of the following key components:

• Intelligent orchestration – The Smart Solver Agent analyzes incident content and determines which specialized agents to invoke based on missing context and potential value-add
• Specialized agent engagement – Five specialized agents (Tag Classifier, Incident Similarity, Incident Associator, Change Associator, Context Retriever) are available to provide enrichment based on the detail and complexity of the incident
• Adaptive processing – The system adjusts enrichment activities based on incident complexity—simple incidents might only require tagging, whereas complex issues receive full contextual analysis

Lessons learned

The implementation of AI agents at Iberdrola demonstrates how the managed primitives of Amazon Bedrock AgentCore significantly accelerate enterprise AI deployment. Amazon Bedrock AgentCore minimized the infrastructure complexity typically required for agentic AI, helping teams focus on agent logic while achieving scalable and secured cloud resources.“At Iberdrola, we’re extending our production AI platform with a new agentic capability powered by Amazon Bedrock AgentCore,” says Iñigo Gutierrez, AI Global Expert Engineer at Iberdrola. “By using a managed serverless runtime with built-in identity, memory, and observability, we can ship LangGraph-based agents that plan, call tools through MCP-style gateways, and operate securely inside our VPC. This feature moves us from point automations to reusable, production-grade agents—reducing engineering cognitive load and accelerating safe delivery across IT operations.”

Key success factors

The solution offers the following key benefits:

• Purpose-built runtime – AgentCore Runtime provides a fully-managed quick start environments to host AI agents with complete session isolation. Additionally, out-of-the-box streaming and MCP and A2A support from AgentCore Runtime alleviate the need to develop custom solutions and build support for these protocols.
• Managed infrastructure – The serverless compute runtimes, identity, and memory services of Amazon Bedrock AgentCore minimize custom development overhead for enterprise-grade capabilities.
• Enterprise security – VPC support and comprehensive tagging aligns with stringent IT requirements, accelerating development without compromising security standards.
• Open and framework-agnostic – Amazon Bedrock AgentCore fits well with development guidelines because you can choose the development framework, such as LangGraph, by adding a simple decorator. Furthermore, it has no restrictions on using third-party or open-source solutions like Langfuse.
• Scalable tool discovery – AgentCore Gateway automatically indexes tools and provides serverless semantic search, scaling from tens to hundreds of targets, totally managed.

Future roadmap

Iberdrola is considering the following future enhancements to the solution:

• Agent catalog – Improve governance and discovery of agents seamlessly integrated into the Amazon Bedrock AgentCore ecosystem
• New supported protocols and standards – Evolve Iberdrola’s agent development to use new protocols supported (such as A2A) by AgentCore Runtime and other managed services
• Managed orchestration and real-time flow monitoring – Build platform-provided dashboards that automatically manage and monitor complex interactions between multiple AI agents, tools, or workflows

Conclusion

Iberdrola’s innovative implementation showcases its leadership and vision in using advanced AI technologies to transform its operational workflows. By adopting Amazon Bedrock AgentCore, Iberdrola has demonstrated how organizations can deploy production-ready AI agents with remarkable efficiency while meeting robust enterprise security and scalability standards. Through its strategic use of Amazon Bedrock AgentCore managed primitives, Iberdrola has realized substantial productivity gains and unparalleled improvements in data quality across its change and incident management processes. This successful transformation underscores Iberdrola’s commitment to excellence in using intelligent solutions to solve complex operational challenges. It also highlights the unique value proposition of Amazon Bedrock AgentCore: industry-first serverless compute for AI agents, integrated enterprise-grade security, and adaptable deployment patterns that accommodate diverse processing requirements. The platform’s ability to streamline infrastructure complexity while supporting specialized workflows makes it an ideal foundation for enterprise AI initiatives.

Organizations looking to implement AI agents in production environments can draw inspiration from Iberdrola’s architectural patterns and its effective execution of AI-driven solutions. Iberdrola’s success serves as a blueprint for accelerating deployments and achieving operational excellence with an Amazon Bedrock AgentCore managed approach, which reduces time-to-value and supports the scale and reliability demanded by enterprise AI systems.

About the authors

Talha Chattha is a Sr. Agentic AI Specialist SA at AWS, based in Stockholm. With 10+ years of experience working with AI, Talha now helps establish practices to ease the path to production for Agentic AI workloads. Talha is an expert in AgentCore and supports customers across entire EMEA. He holds passion about meta-agents, async patterns, advanced hierarchical solutions and optimized context engineering for agents. When not shaping the future of AI, he explores the scenic European landscapes and delicious cuisines. Connect with Talha at LinkedIn.

Unai Bermejo is a Global Expert AI Engineer at Iberdrola. With 10 years of experience in applied AI, AI research, and software engineering, Unai now helps Iberdrola establish best practices and frameworks in AI and agentic initiatives, aligned with corporate platforms and business needs. He acts as a technical bridge between AI technology, Cloud engineering teams, and business developers, driving the adoption of scalable, responsible, and high‑impact AI solutions across the organization.

Xabier Muruaga is the Global Head of AI and Data at Iberdrola. With over 15 years of experience in AI/ML and data‑driven architectures, he leads the company’s strategy and governance for secure, cloud‑native, and production‑ready AI platforms. His background across architecture, digital transformation, and energy technologies enables him to drive responsible, high‑impact AI and agentic initiatives across the organization.

Iñigo Gutierrez is a Global Cloud AI Engineer at Iberdrola with five years of experience in Cloud architecture, platform engineering, and AI enablement. Based in Bilbao, he is responsible for the design, evolution, and governance of the company’s corporate Cloud platforms, ensuring they provide a secure and scalable foundation for AI and digital transformation initiatives. Iñigo acts as a technical enabler between Cloud engineering teams, AI projects, and business units, promoting standardized practices, operational excellence, and the adoption of responsible, high‑impact AI solutions across the organization.