Cutting Through the Illusion: Why RMM Automation Fails and How Level Gets It Right

‍Introduction

For MSPs and internal IT teams, Remote Monitoring and Management tools have long promised one thing, automation. The idea is simple. Automate routine tasks, handle patching without hands on keyboards, and run scripted workflows so technicians can focus on higher value work. Yet for many teams, automation becomes a source of noise and uncertainty rather than a steady engine of productivity.

How often do you find yourself chasing scheduled tasks that did not run or patch deployments that quietly failed on a subset of devices. These breakdowns are not just inconvenient. They increase risk windows for vulnerabilities, slow response times, inflate ticket queues, and frustrate end users and clients.

This post takes a clear look at why traditional RMM automation often misses the mark, why those misses carry real operational cost, and how Level’s approach delivers reliable, observable, and scalable automation that teams can trust in production.

The Reality Behind “Set It and Forget It” Automation

Automation is the headline feature for most RMM platforms. The daily reality often looks different.

Scheduled jobs fail silently. Cleanup routines, disk checks, and reboot tasks appear as successful in dashboards, yet nothing changed on the endpoint. Success states are sometimes set at schedule creation rather than at verified completion. Teams discover the failure only after a compliance scan flags drift or a user reports degraded performance.

Patch management behaves inconsistently. Patching is not a checklist item. It is a security control with time bound service level objectives. Some platforms mark patches as applied if the installer launched, not if the endpoint actually rebooted and reported the correct build. Others push patches without staged rings, which increases the likelihood of uneven application and rollback headaches.

Scripting environments are clunky. Many tools expose a thin wrapper over system executables and call that automation. Lacking proper parameterization, versioning, output capture, and test harnesses, these environments make it hard to run at scale with confidence. Teams end up building workarounds, which drifts them further from the intended platform value.

Level takes a different stance. Automation is not an add on feature. It is the core infrastructure. When Level reports that a task ran, you get proof, including start and end timestamps, captured standard output and error, exit codes, and device state before and after the run.

Seven Misconceptions That Hold Teams Back

Misunderstandings about automation lead to fragile operations. These are the most common traps we see.

Myth 1, Automation means less work. Bad automation creates more work. Chasing failed jobs, answering client questions, and manually verifying results burns time. Good automation reduces repetitive keystrokes while increasing observability. You still need human review, but review shifts to exceptions, not every run.

Myth 2, All automation engines are the same. They are not. Some platforms bolted on schedulers after the fact. Others treat scripts as opaque blobs. Level designs around dependable execution, verifiable outcomes, and safe testing, which changes the day to day experience for technicians.

Myth 3, If the platform says it ran, it ran. Beware of green states without evidence. You need run level telemetry. That includes pre-flight checks, exit codes, captured logs, and post run validation of the intended change. Without that detail, success is an assumption.

Myth 4, Automate everything. Automation should target predictable, low risk, high frequency actions first. Over extending automation into poorly understood workflows without guardrails increases incident rates. Start with small, idempotent tasks, layer validation, then scale coverage.

Myth 5, Automation is only for large MSPs. Smaller teams benefit sooner. When technicians wear multiple hats, eliminating routine toil has an outsized impact. The key is a platform that does not require a specialist to maintain the system itself.

Myth 6, Automation equals scripting. Scripting is one mechanism. True automation also includes policy enforcement, reusable templates, visual scheduling, pre checks and post checks, and dry runs. If you must write custom code for every outcome, you are acting as the vendor’s missing product team.

Myth 7, If it is not running, it is your fault. Fragile schedulers, unreliable agents, mismatched permissions, and missing dependencies cause many failures. The platform should surface root causes with clarity. Do not accept opaque errors that shift blame to the operator by default.

Why RMM Automation Fails in Production

Moving from a lab to a fleet is where weaknesses appear. The most frequent technical causes look like this.

Unreliable agents. The agent is the job runner. If it crashes, cannot update, or loses connectivity, the scheduler marks jobs as pending or complete with no real execution. Agents need health checks, backoff and retry, and a robust update channel.

Weak dependency modeling. Jobs often assume prerequisites are present. Examples include a required service, a disk threshold, or a package version. Without pre checks and dependency graphs, jobs fire, partially complete, and leave the system in an unknown state.

Lack of idempotency. Scripts that are not idempotent cause drift when retried. A script that adds a registry key should be safe to run again. A patch routine should verify the build number before and after. Idempotency is what allows retries without fear.

No staged rollout. Fleet wide changes should never go global first. You need rings, for example pilot, canary, broad, with automatic promotion rules based on success criteria. Without rings, a bad patch becomes a widespread incident.

Poor observability. Logs trapped on endpoints and missing centralized run records make triage slow. Operators need searchable execution logs, device state snapshots, and correlation to alerts and tickets.

Scheduler fragility. Cron like schedulers without drift correction or concurrency controls often stack jobs or skip windows. A production grade scheduler should handle conflicts, missed windows, and maintenance periods predictably.

Privilege management gaps. Running everything as full admin simplifies the first week and complicates the rest. Tasks should run with the least privileges needed. Elevation should be explicit, auditable, and time bound.

The Real Cost of Failed Automation

The cost is not just a missed green check.

• Missed SLAs and compliance lapses. Patch windows slip. Baselines drift. Audits become manual excavations rather than report exports.
• Expanded vulnerability windows. Delays in patching and configuration enforcement leave known issues exposed longer than policy allows.
• Higher ticket volume and burnout. Failures create tickets and require manual fixes. Burnout follows when tools demand more attention than the endpoints they manage.
• Loss of trust. When the platform says done but production says otherwise, teams double check everything. Manual verification eliminates the benefit of automation.

Reliable automation reduces this drag. It also creates the foundation for predictable growth, since every new endpoint inherits known policies and dependable job execution.

What the Future of RMM Automation Looks Like

Modern automation is moving from blind scheduling to context aware orchestration. The following capabilities define the next standard.

Smarter, context aware execution. Jobs should consider CPU load, user presence, battery state, and maintenance windows. A cleanup job should defer during a CPU spike. A reboot should wait for a user to sign out or for a maintenance window.

Feedback loops and self healing. Tasks should adapt to outcomes. If a patch fails, trigger a remediation script, capture diagnostics, and queue a retry with altered conditions. If the second attempt fails, escalate automatically with attached logs.

Built in templates that are safe by default. Operators should not start from zero. Templates for common tasks, such as browser cache cleanup, agent repair, or line of business app updates, should include pre checks, post checks, and rollback notes.

Safer testing and staging. Dry runs and sandboxes allow teams to preview changes without altering systems. Combined with rings, this reduces fear and speeds adoption.

Unified policy and automation. Policies describe steady state, for example required agent version or required patch level. Automation enforces those states. When policy and automation share a model, drift is detected and corrected without manual babysitting.

Human in the loop controls. Critical actions like mass patch approvals and software removals should support approvals, exceptions, and annotations. Controls slow down the right changes and document intent.

How Level Gets Automation Right

Level’s approach is built around dependable execution, rich observability, and ease of use for real fleets.

Automation as core infrastructure. Level’s engine treats each run as a first class object with identity, timestamps, device context, standard output and error capture, and exit codes. Success is recorded only after post run validation passes.

Pre checks and post checks everywhere. Every job can declare prerequisites and verification steps. Examples include disk thresholds, specific services, or required package versions. Verification confirms the intended state is real, not assumed.

Clear, scalable scripting. Scripts are parameterized and versioned. You can scope a script to one device, a dynamic group, or your entire fleet. Dry runs are available for validation. Output is streamed and retained for search and audit.

Rings and safe rollout controls. Level supports canary and ring based deployment. You define promotion criteria, such as success rate or error budget, and Level promotes automatically or halts with alerts when thresholds are not met.

Sensible defaults with flexibility. Level ships with usable templates for common tasks. You can customize or extend them without building a private library from scratch. Defaults include log capture, timeouts, and retries that match production needs.

Agent reliability and health. The Level agent includes self update, watchdogs, and connectivity fallbacks. Health signals are visible, which makes it clear when a device cannot receive work and why.

Always on transparency. Green states are earned, not granted. Every success and failure has evidence attached. When you review a job, you see what happened and what changed.

A Practical Implementation Checklist

If your current environment struggles with automation reliability, use this checklist to improve outcomes, whether you migrate to Level or stabilize your current stack.

1. Define success for each job. Write success criteria in operational terms. For example, build number equals X, service Y is running, disk free space is above Z.
2. Make scripts idempotent. Ensure repeated runs do not cause harm. Check before you change. Validate before you exit.
3. Add pre checks and post checks. Validate prerequisites. Confirm outcomes. Treat missing checks as failures that require attention.
4. Use rings. Start with a pilot group. Promote to broader rings only when defined metrics pass.
5. Capture output centrally. Store standard output and error, exit codes, and key device metrics for every run.
6. Instrument retries with backoff. Retries should not hammer endpoints. Use exponential backoff and jitter. Stop after a sensible limit and escalate with context.
7. Separate privilege from logic. Run tasks with the least privilege required. Elevate only for the steps that need it.
8. Document rollback. Every change should include a reversal path. Practice rollback on pilot rings.
9. Measure automation reliability. Track success rate, time to completion, and mean time to detect and resolve failed runs.
10. Review weekly. Use a short review to examine outliers, tune thresholds, and retire brittle jobs.

Metrics That Prove Automation is Working

Operational outcomes matter more than feature lists. These metrics tell you if your automation is creating value.

• Automation success rate. Percentage of jobs that complete with validated outcomes.
• Patch compliance within the window. Fraction of endpoints at the required level within the defined time.
• Mean time to remediate failed runs. How quickly your team resolves exceptions.
• Ticket deflection. Reduction in repetitive tickets tied to automated fixes.
• Technician focus time. Hours per week shifted from toil to project work.
• Change failure rate. Percentage of automated changes that cause incidents or require rollback.

Improvement across these measures correlates with happier users, cleaner audits, and steadier service margins.

A Short Story From the Field

An MSP managing just under eight thousand endpoints arrived with a familiar complaint. Their team spent evenings chasing missed patches and broken tasks. The RMM dashboard was green most days. Client phones were not. They began with a two week Level pilot across a canary ring of two hundred devices. The pilot focused on three items, monthly patching, browser remediation, and agent health repair.

By week three they moved to a broader ring after success criteria were met. Patch compliance within the window moved from sixty nine percent to ninety six percent. Browser cleanup jobs stopped creating follow on tickets because verification caught incomplete runs. Agent repair used a standard template with pre checks, which removed guesswork. After full rollout, repetitive tickets dropped by twenty three percent and engineers reclaimed about six hours per week for project work. Most notably, no one needed to stay late just to watch the scheduler.

Final Thoughts, Simplicity Builds Confidence and Efficiency

Automation should make teams faster and calmer. If your RMM requires constant double checking, you are managing risk rather than managing endpoints. Reliable automation is not magic. It is a careful design around execution, validation, and visibility.

Level’s philosophy is direct. Build automation that works as expected, prove it with evidence, and make it easy to use at fleet scale. That frees your team to focus on outcomes, reduces stress, and raises client satisfaction.

If you spend more time managing workarounds than managing results, it is time to rethink your approach to RMM automation. With Level, when the platform says the job is done, you can move forward.

Further Reading and Sources

• NIST SP 800 40 Revision 4, Guide to Enterprise Patch Management.
• Verizon Data Breach Investigations Report, latest edition.
• SANS Institute white papers on automation and incident response.
• Microsoft Security Response Center guidance on update quality and servicing.
• Google SRE Workbook and related materials on change management, error budgets, and reliability practices.