Key Takeaways

  • AI agents are already driving significant ROI: Companies are seeing savings of $1,000 to $5,000 per engineer by deploying agents for specific tasks, some even higher.
  • SRE and auto-triage are the easiest entry points: Agents excel as first responders to alerts in platforms like Slack and DataDog, diagnosing issues before humans even see them.
  • Non-engineers are now contributors: Product managers and marketing teams are skipping issue tickets and directly prompting AI agents via Slack to create pull requests.
  • Continuous security is a major use case: AI agents are taking on the relentless task of security scanning and review, ensuring systems stay compliant and secure around the clock.

Your First Responder for Alerts Is Now an AI

You're probably used to alerts hitting your team's Slack channel or DataDog dashboard, demanding immediate attention. What if the first responder to that alert wasn't a human, but an AI agent already diagnosing the problem? Walden Yan says this is now the “easiest and most common use case I see across everyone: SRE use cases.”

Imagine an agent integrated directly into your existing communication platforms. An alert fires, and instead of a human engineer dropping everything, the agent steps in. It analyzes the alert, checks logs, runs diagnostics, and perhaps even suggests a fix or opens a detailed incident ticket. This isn't theoretical; it's happening. The agent becomes a force multiplier for your SRE team, buying precious minutes—or hours—and freeing up skilled engineers for more complex work. This shift means your team isn't just reacting faster; they're reacting smarter, with AI providing the initial triage.

Your PMs Are About to Ship Code (Without Coding)

For most founders, getting non-technical teams to contribute directly to product development is a pipe dream. Product managers write specs, designers create mockups, and then engineers translate it all into code. Walden Yan revealed a quiet revolution happening right now: “the PM is not creating an issue anymore. The PM is just prompting through Slack and the pull request is then being created.”

This isn't about PMs learning Python. It's about AI agents acting as intelligent interpreters, turning natural language prompts into executable code changes. A PM sees a small bug on the marketing site or a tweak needed on a product page, drafts a prompt in Slack, and the agent generates the pull request. This bypasses the typical ticketing system and engineering queue for minor changes, shrinking feedback loops and speeding up iteration cycles. It changes the nature of cross-functional collaboration, making “everyone is a builder” more real than ever.

Beyond Code: Security and Serious Cost Savings

The impact of AI agents extends beyond direct code generation and incident response. Cole Murray pointed to “continual security scanning continual security review” as another major area where agents are making a difference. In complex enterprise environments, keeping up with security vulnerabilities and compliance requirements is a constant, resource-intensive battle. Agents can continuously monitor systems, identify potential weaknesses, and even suggest patches, providing an always-on security layer that would be prohibitively expensive with human effort alone.

And let's talk numbers. This isn't just about efficiency; it's about significant cost savings. Walden Yan reports that “common numbers that I hear are anywhere from a thousand an engineer up to five thousand an engineer” in typical spending on AI agents. This kind of investment translates directly into operational leverage, often by offloading repetitive or front-line tasks. While integration into "complex enterprise ecosystems" with their compliance and access control hurdles remains a challenge, the clear economic incentive means companies are actively working to overcome these barriers.

What to Do With This

This week, pick one high-volume, low-complexity workflow on your SRE or internal support team. Challenge your team to define how an AI agent could act as the "first responder" to alerts in Slack or DataDog. Simultaneously, ask a product manager or marketing lead to identify one minor code change they'd love to see pushed live without creating a ticket, and then explore how an agent could generate that pull request from a simple prompt. Calculate the potential time savings and map it to Walden Yan's $1,000-$5,000 per engineer range to build a compelling internal business case for your first agent pilot.