Birol Yildiz on Autonomous Incident Response and the Future of AI SRE | Harness Blog

At SREday NYC 2026, the ShipTalk podcast welcomed Birol Yildiz, Co-founder and CEO of ilert, for a conversation about the next evolution of incident response.

In the episode, ShipTalk host Dewan Ahmed, Principal Developer Advocate at Harness, spoke with Birol about how artificial intelligence is transforming reliability engineering—from simply assisting engineers during incidents to autonomously diagnosing and resolving outages.

For many SRE teams, the goal has always been clear: fewer late-night pages and faster recovery times. According to Birol, the next wave of tooling may finally make that possible.

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The Shift Toward Autonomous Incident Resolution

For years, AI tools in operations have focused mainly on post-incident assistance—summarizing alerts, analyzing logs, or helping generate incident reports.

But Birol believes the industry is now moving beyond that stage.

Instead of just helping engineers understand what happened, AI SRE agents are beginning to actively resolve incidents in real time.

These systems ingest signals from multiple sources, including:

• Observability data and system metrics
• Deployment and infrastructure changes
• Application logs and traces
• Code context and service dependencies

By correlating these signals, an AI agent can detect the root cause of an outage and automatically execute remediation steps—often within minutes.

The result is a dramatic shift in incident response.

Rather than waking up engineers with alerts in the middle of the night, the system can often resolve the issue first and present a clean incident report afterward.

How AI Combines Observability, Deployment Context, and Code Intelligence

One of the biggest challenges for SREs during incidents is context switching.

Engineers typically jump between multiple tools to investigate problems:

• Observability dashboards
• Log aggregation systems
• Deployment pipelines
• Infrastructure changes
• Application code

Each system provides only part of the picture.

According to Birol, modern AI agents work by aggregating all of that context into a single reasoning layer.

Instead of humans manually stitching together signals, the system continuously evaluates relationships between events. For example:

• A deployment happened minutes before a spike in latency
• A specific service dependency began failing
• Error rates correlate with a configuration change

By combining these insights, the AI can determine whether the correct response is to:

• Roll back a deployment
• Restart a failing service
• Scale infrastructure resources
• Route traffic away from a problematic component

To prevent risky actions, these systems operate within carefully defined guardrails and remediation policies, ensuring automation helps rather than harms production environments.

The Rise of the “Product-Minded” SRE

Birol’s perspective on reliability engineering is shaped by his background as Chief Product Owner for Big Data products at REWE Digital before founding ilert.

That experience gave him a product-centric lens on operations.

Instead of treating incidents purely as operational events, he sees them as product experience problems.

From that viewpoint, reliability engineering becomes less about firefighting and more about designing systems that:

• reduce operational toil
• improve developer productivity
• accelerate recovery times
• minimize customer impact

As autonomous agents take on more of the routine incident work, the role of the human SRE will likely evolve.

Rather than spending most of their time responding to alerts, engineers will increasingly focus on:

• defining automation policies
• improving observability coverage
• designing safer remediation workflows
• validating AI-driven incident responses

In other words, the SRE of the future may look less like a firefighter and more like a systems architect overseeing intelligent automation.

Building Toward a World Without 3 A.M. Pages

For many engineers, being on-call remains one of the most stressful parts of the job.

Birol believes that autonomous incident resolution can fundamentally change that experience.

If AI agents can reliably detect, diagnose, and remediate common failure scenarios, teams can dramatically reduce the number of alerts that require human intervention.

The long-term goal isn’t to remove humans from operations entirely. Instead, it’s to eliminate the repetitive operational toil that prevents engineers from focusing on higher-value work.

When systems resolve routine incidents automatically, teams gain the freedom to spend more time on:

• improving system architecture
• building better developer tooling
• shipping new features
• innovating on reliability practices

Final Thoughts

Birol Yildiz’s vision for the future of SRE reflects a broader shift happening across the industry.

Observability, automation, and AI are converging to create systems that can understand infrastructure and respond intelligently to failures.

If that vision succeeds, the next generation of reliability engineering might look very different from today.

Fewer dashboards.
Fewer manual investigations.
And far fewer 3 a.m. incident pages.

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