Why current IFTTT tools fall short for DevOps and SRE Teams

While IFTTT platforms look great in demos, the realities of running them for DevOps and SRE are very different. Here's where these tools fail to deliver.

TLDR:

The phrase If This, Then That, better known as IFTTT, captures a simple idea: if a specific condition happens, then trigger a defined action. At the consumer level, this usually looks like linking two services together in a recipe: “if I get an email attachment, then save it to Google Drive” or “if it's going to rain tomorrow, then send me a Slack reminder to bring an umbrella.”

The appeal is obvious. No coding, no custom integrations. Just drag and drop workflows that connect APIs and automate repetitive tasks. In recent years, this IFTTT-style of automation has grown beyond personal productivity into enterprise platforms like Tines, n8n, and Make. These tools promise teams a faster path to efficiency by connecting disparate systems through visual rules.

For DevOps and SRE teams, the attraction is equally strong. On the surface, IFTTT tools look like a way to reduce operational toil. Instead of chasing alerts across dashboards or following lengthy runbooks by hand, you wire up a chain of triggers and responses and let the system take care of the rest. Tools such as Rundeck and StackStorm go further by positioning themselves squarely in the DevOps space. Rundeck wraps your scripts and commands in repeatable, self-service jobs. StackStorm extends the IFTTT model into an event-driven automation platform for incidents and infrastructure. To teams drowning in manual work, these platforms promise reliability, speed, and scale through automation. At first glance, it looks like the answer.

The problem is that while IFTTT platforms look great in demos, the realities of running them for DevOps and SRE are very different. Below are the three biggest areas where these tools fail to deliver.

Control and Governance

In consumer automation, anyone can trigger a recipe. In operations, anyone pressing the wrong button can take down production. This is where IFTTT platforms are weakest: governance and security. A study of OAuth in IFTTT tools found that 75 percent of connected services granted more permissions than necessary, meaning a single compromised automation could expose entire systems.

In SRE and DevOps, access must be tightly scoped: who can restart a service, who can deploy to production, and under what conditions. That requires role-based access controls, audit trails, and human approvals. Most general-purpose IFTTT platforms treat these as afterthoughts.

The lack of human-in-the-loop controls is another gap. Many operational tasks cannot be fully automated. They require judgment at critical points. For example, a remediation script might fix 80 percent of alerts automatically, but for the 20 percent that are ambiguous, you need an engineer to approve or guide the next step. Gartner forecasts that by 2027, 42 percent of business tasks will be automated, but reasoning and decision-making tasks will still require oversight. Without the ability to embed pauses, approvals, or overrides, these tools push teams into an unsafe all-or-nothing model.

Deciding What to Automate

The promise of IFTTT workflows is that they reduce toil. The challenge is deciding which toil to eliminate. A 2022 survey of over 1,000 DevOps, ITOps, and SRE professionals found that 48 percent said automation was key to reducing MTTR. That shows the upside, but also the risk: the wrong automation does not just waste time, it undermines reliability if it misfires during an incident.

The IFTTT model encourages a recipe mindset: pick a task, wire it up, move on. What is missing is a framework to identify high-value, low-risk automation opportunities. In practice, this means starting with deterministic tasks like log rotation, cache clearing, or disk space checks. It means steering clear of edge cases where automations break more often than they help. And it means embedding automation gradually into workflows instead of trying to automate everything at once.

Friction is another barrier. If designing an automation takes longer than just doing the task by hand, people do not build it. If testing an automation requires spinning up complicated mock environments or trial and error in production, people avoid it. If maintaining an automation means debugging every time a dependency changes, people abandon it. In theory, everything can be automated. In practice, only the tasks that are easy to reason about, quick to validate, and stable over time actually get automated.

This creates a Catch-22. The easiest automations are often the least impactful, while the most valuable ones are too complex to design and test. Without tools that help teams bridge this gap, organizations end up with many small, disconnected automations that do not improve reliability. The hardest work remains stuck in manual runbooks.

None of the existing IFTTT tools for DevOps teams solves this problem.

From Paragraphs to Decision Trees

Runbooks are the backbone of operations. They capture institutional knowledge, guide incident response, and ensure consistency across shifts. But while IFTTT tools can technically represent branching workflows, the gap is not in basic conditional logic. The gap is in how these tools handle the messiness of real operational runbooks.

A typical runbook is not just nested if/else statements. It is a decision tree with conditional checks, diagnostics, escalation rules, rollback strategies, and human judgment calls. Tools like n8n or StackStorm can model branches, but once a workflow grows beyond a few dozen nodes, it becomes unwieldy. It is hard to visualize, harder to test, and almost impossible to maintain as services evolve. What starts as a neat flowchart quickly turns into spaghetti automation. No one fully trusts it in an edge case.

Context and improvisation are also missing. A human following a runbook can stop, evaluate what they see, and deviate from the script if necessary. An automated workflow, even with branches, only knows the paths it was given. If the situation falls outside those paths, the automation fails silently, loops endlessly, or triggers the wrong action. This brittleness is why teams hesitate to fully automate their most critical runbooks, even though manual runbooks are slow, error-prone, and expensive in downtime.

Conclusion

The ideal platform for DevOps and SRE automation would bridge the gap between today's brittle recipes and the realities of operating complex systems. It would allow teams to define automations along a spectrum, from fully deterministic workflows for routine tasks to more agentic, AI-driven processes that can apply real-time reasoning when conditions are ambiguous. It would provide a tight, enterprise-grade RBAC system to ensure only the right people can trigger, approve, or modify critical automations, with full auditability. Instead of relying on engineers to manually identify what to automate, it would surface opportunities automatically by mining signals from tools like Slack, Jira, and monitoring systems to highlight repetitive patterns and sources of toil. Finally, it would leverage AI to dramatically reduce the time and friction required to create and test new automations, allowing teams to safely experiment, validate, and deploy them at scale. In short, the future of technical operations automation is not another recipe builder, but a platform that is safe, intelligent, and adaptive enough to evolve alongside the systems it manages.

This is what we're building at Forge.

If you'd like your team to be an early user, reach out at team@a37.ai.