Accelerate Operations with AI: New Splunk and AWS Integrations

For engineering and data science teams, Splunk often serves as the definitive source of truth, containing rich, real-time data across security, operations, and business applications. The core challenge is bridging this valuable data with advanced AI and automation services to drive action. Two new integrations with AWS are designed to solve this by creating seamless workflows that activate your Splunk data where it lives, removing friction and accelerating time-to-value.

This post will cover two distinct but complementary integrations:

• Automating Incident Response with Splunk MCP and the AWS DevOps Agent
• Streamlining Machine Learning with the Splunk AI Toolkit and Amazon SageMaker

Automating Incident Response: Splunk MCP with the AWS DevOps Agent

For engineering teams, the goal is to move from reactive firefighting to proactive operational excellence. This requires not only data but also intelligence to act on it autonomously. The built-in integration between Splunk and the AWS DevOps Agent is designed to bridge this gap, turning Splunk's rich observability data into automated action.

Effective automation relies on a high-quality, centralized data source. Splunk serves this function by ingesting and normalizing data from diverse sources, including AWS services, application logs, and CI/CD pipelines. This creates a unified data plane where complex operational patterns can be identified using data in Splunk.

How the Agent Leverages Splunk's Intelligence

The AWS DevOps Agent acts as an autonomous AI teammate that thinks and works as an experienced operations engineer. It connects directly to your Splunk environment via the Splunk Model Context Protocol (MCP) server, requiring no changes to your existing workflows.  The integration architecture is designed for seamless intelligence:

• Topology and Context Mapping: The agent doesn't just see logs; it understands context. It actively maps your application resources and learns the relationships between Splunk log groups, infrastructure and service information from Splunk Observability and other entities in your application topology (e.g., EC2 instances, Lambda functions, code repositories).
• Automated Investigation: When an incident occurs, the agent begins an investigation. It autonomously constructs and executes complex SPL queries to introspect the relevant logs in Splunk, correlating telemetry with recent code deployments and configuration changes.
• Root Cause Analysis and Mitigation: By analyzing the results from Splunk against its topological map, the agent pinpoints the root cause. It then generates a detailed mitigation plan to resolve the issue, which is presented to the on-call engineer for review and approval.

From Reactive Alert to Proactive Resolution

Consider a critical incident, like a service latency spike, and how it might be handled. Let us look at two contrasting scenarios: the old way versus a modern, Agentic AI-driven solution.

The Old Way: Panic sets in. An on-call engineer is paged, interrupting their focus. They rush to their workstation, logging into Splunk, their mind racing to recall the most relevant queries. They painstakingly begin to manually sift through mountains of metrics, logs, and recent deployment data, trying to piece together a coherent picture. Each query is a hypothesis, each result a clue, but the process is slow, demanding deep expertise and a keen eye for correlation. Precious minutes, sometimes hours, tick by as they hunt for the needle in the haystack, all while the service continues to degrade.

Now, picture the Agentic AI Way: The same latency spike occurs, but this time, an alarm instantly triggers the AWS DevOps Agent. This is not just another monitoring tool; it is an intelligent teammate. The agent, already possessing a deep understanding of the service's intricate dependencies and topology, springs into action. It autonomously queries Splunk, with targeted precision, looking for correlated events within the incident window deployment markers, sudden error log surges, configuration changes, downstream dependencies and identify service errors.

Within moments, the agent processes the vast data. Its analysis reveals a clear correlation: the latency spike aligns perfectly with a recent deployment that subtly alters a critical database connection parameter. The root cause is identified, not through human intuition, but through rapid, data-driven analysis.

The agent then does not just stop identification; it formulates a precise mitigation plan: "Roll back deployment [commit_hash] for service [service_name]." This actionable recommendation is presented to the on-call engineer. With a quick review and a single approval, the action is taken. The incident, which once consumed hours of stressful, manual effort, is resolved in mere minutes, allowing the engineer to focus on innovation rather than firefighting.

Outcomes: Always-On Operations and Democratized Insights

This integration delivers more than just speed; it enables a fundamental shift in operations. An "always-on" AI agent proactively identifies and prevents incidents by constantly querying Splunk data, fostering foresight. It also democratizes expertise, allowing any team member to gain deep insights from Splunk without needing complex query skills. The result is significantly enhanced business resilience, as reduced Mean-Time-to-Resolution (MTTR) and proactive detection ensure reliable services.

Setting up this integration is straightforward with native support for Splunk MCP in AWS DevOps agent.

By connecting the AWS DevOps Agent to Splunk, you empower your teams with continuous, actionable intelligence.

Smarter AI Through Streamlined Integration: Splunk AI Toolkit and AWS SageMaker

Effective AI requires context. The Splunk AI Toolkit and AWS SageMaker integrate seamlessly, connecting real-time operational data with business context to power faster, smarter AI-driven decisions across security, IT, and business operations. This integration streamlines the entire workflow from Splunk-powered data prep to SageMaker model deployment making advanced AI accessible and accelerating time-to-value.

For example a data scientist needs to analyze streaming transactions for fraud risk in real time without slowing down the customer experience.  Already a user of Splunk AI Toolkit (formerly known as Splunk MLTK), they can easily integrate with SageMaker: Splunk extracts features and streams them to SageMaker, which instantly returns risk scores natively in a Splunk search. They then add historical context and triggers alerts for the fraud team, stopping threats before they escalate.

This integration can help with customer success use cases, too. They use Splunk to preprocess app usage logs and feedback, then build a sentiment analysis model in SageMaker for churn prediction. They register the SageMaker model in AI Toolkit to flow churn predictions back into Splunk dashboards, giving them real-time customer insights with no custom ETL, just immediate business agility.

Ready To Accelerate Your AI Success?

The partnership between Splunk and AWS is built for simplicity and impact. Our integrations are seamless and non-disruptive, connecting directly into your existing workflows and amplifying the intelligence of your Splunk deployment. This means every team, whether focused on security, IT, or observability can work collaboratively with a unified, enriched view of the enterprise.

As your organization grows, secure real-time data flows between Splunk and AWS allow you to confidently scale AI initiatives. Advanced analytics and machine learning models can be applied to your most critical data without ever interrupting business as usual. The result: increased operational efficiency, faster innovation, and digital resilience that keeps you ahead of change.

Whether you are unlocking insights, automating protection, or driving business agility, Splunk and AWS provide the foundation for AI-powered transformation. By delivering trusted data, enterprise-ready workflows, and agentic automation, this partnership accelerates your journey from data to decision and from insight to action.

Ready to accelerate your AI success? Explore the combined power of Splunk and AWS, and empower your teams with the intelligence, speed, and resilience to thrive in a dynamic world.