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Have you ever wanted to pause an automated workflow to wait for a human decision?

May 13, 2025 at 07:17 am

Have you ever wanted to pause an automated workflow to wait for a human decision? Maybe you need approval before provisioning cloud resources, promoting a machine learning model to production, or charging a customer’s credit card.

In many data science and machine learning workflows, automation gets you 90% of the way — but that critical last step often needs human judgment. Especially in production environments, model retraining, anomaly overrides, or large data movements require careful human review to avoid expensive mistakes.

In my case, I needed to manually review situations where my system flagged more than 6% of customer data for anomalies — often due to accidental pushes by customers. Before I implemented a proper workflow, this was handled informally: developers would directly update production databases (!) — risky, error-prone, and unscalable.

To solve this, I built a scalable manual approval system using AWS Step Functions, Slack, Lambda, and SNS — a cloud-native, low-cost architecture that cleanly paused workflows for human approvals without spinning up idle compute.

In this post, I’ll walk you through the full design, the AWS resources involved, and how you can apply it to your critical workflows.

Let’s get into it 👇

The Solution

My application is deployed in the AWS ecosystem, so we’ll use Aws Step Functions to build a state machine that:

Here is a youtube video showing the demo and actual application in action:

I have also hosted the live demo app here →👉 https://v0-manual-review-app-fwtjca.vercel.app

All code is hosted here with the right set of IAM permissions.

Step-by-Step Implementation

The flow above generates a dataset, uploads it to AWS S3 and if a review is required, then invokes the Manual Review lambda. On the manual review step, we’ll use a Task lambda with an invoke on WaitForTaskToken, which pauses execution until resumed. The lambda reads the token this way:

This Lambda sends a Slack message that includes the task token so the function knows what execution to resume.

2. Before the we send out the slack notification, we need to

I followed the youtube video here for my setup.

3. Once the above is setup, setup the variables into the web-hook step of the slack workflow:

And use the variables with a helpful note in the following step:

The final workflow will look like this:

4. Send a Slack Notification published to an SNS topic (you can alternately use slack-sdk as well) with job parameters. Here is what the message will look like:

This Lambda sends a Slack message that includes the task token so the function knows what execution to resume.

5. Once a review notification is received in slack, the user can approve or reject it. The step function goes into a wait state until it receives a user response; however the task part is set to expire in 24 hours, so inactivity will timeout the step function.

Based on whether the user approves or rejects the review request, the rawPath gets set and can be parsed here: code

The receiving API Gateway + Lambda combo:

Example:

Note: Lambda configured with WaitForTaskToken must wait. If you don’t send the token, your workflow just stalls.

Bonus: If you need email or SMS alerts, use SNS to notify a broader group.Just sns.publish() from within your Lambda or Step Function.

Testing

Once the manual approval system was wired up, it was time to kick the tires. Here’s how I tested it:

I tested all major paths:

Behind the scenes, I also verified that :

I highly recommend testing not just happy paths, but also “what if nobody clicks?” and “what if Slack glitches?” — catching these edge cases early saved me headaches later.

Lessons Learned

Wrapping Up

Adding human-in-the-loop logic doesn’t have to mean duct tape and cron jobs. With Step Functions + Slack, you can build reviewable, traceable, and production-safe approval flows.

If this helped, or you’re trying something similar, drop a note in the comments! Let’s build better workflows.

Note: All images in this article were created by the author

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