Scaling Workflow Automation

#Workflow Managers! Workflow managers like #Nextflow, #Snakemake, #CWL, #WDL (#cromwell), #ensembl‑hive, and others act as orchestrators/conductors. They: 🔹 Define dependencies between tasks (e.g. FASTQ → alignment → variant calling) 🔹 Use executors to send jobs to HPC, cloud, Kubernetes, etc. (e.g. Slurm, AWS Batch, LSF, SGE) 🔹 Track status, retries, logging, error handling, and provenance 🔹 Allow workflows to be reproduced and resumed, even mid‑execution with caching 🔹 They support containers, resource specs, and automatic parallelisation through portable DSLs or config ➿ Workflow Patterns Workflow managing tools essentially build and run Directed Acyclic Graphs (DAGs). Common execution patterns use asynchronous type communication and include: 🪭 Fan – one task splits into multiple parallel jobs (e.g. process 100 samples). 🍸 Funnel – results gathered and merged back into one downstream task. ⛔ Semaphore or Barrier – wait until all tasks in a stage finish before continuing. ❓ Conditional execution – run tasks only if e.g. QC fails. These patterns enable flexible, parallel, and reproducible pipelines across all major systems. ℹ️ Scaling, Performance & IO Tips 🔸 Batch and Chunk High-Memory or Heavy-IO Jobs/ Divide-and-Conquer Strategy For memory-intensive tools, partition/split data (e.g. chromosomes, bam file regions) and run parallel subprocesses before merging (funnelling) - this is beneficial to reduce RAM requirements and helps to mitigate exit 137 OOM issues. 🔸 Beware Heavy I/O Steps Tasks like indexing or sorting in many tools can saturate disk space. Use local scratch space (e.g. `$TMPDIR`) or use RAM-disks/IO optimised compute instances, and delete intermediate files as soon as they’re no longer needed. 🔸 Specify Resources Explicitly Always define accurate CPU, memory, and time requirements with slight contingency. Overcommitting kills performance; under-allocating introduces job failures. 🔸 Leverage Caching & Resume Features Nextflow, Snakemake, CWL, WDL and ensembl-hive all support resuming where things did not complete or something changed - ideal for long-running or costly tasks. It saves costs and time (and the environment). Watch out for unintended non-deterministic patterns that may break serialisation in Nextflow! (I've been bitten by this!). 🔸 Authorise Executors Thoughtfully Aim for executors that work with containerisation (Docker, Singularity/apptainer etc), but tune your cluster/batch submission parameters (e.g. job arrays vs scatter, progressive best fit, spot allocation etc). 🔸 Avoid Workflow Overhead Thousands of small jobs can slow down the scheduler. Group trivial tasks where possible. Hope this acts as a good reminder/quick guide, let me know in the comments if you have any other workflow-manager-agnostic, or workflow-manager-specific tips and tricks - which workflow manager do you most predominantly use?

Unlocking proper parallel execution in n8n is one of those small tweaks that can transform your automation efficiency. Night and day difference, really. By default, n8n runs subworkflows sequentially, like us Brits queueing up for a bus, but for high-volume tasks, this becomes a proper bottleneck. The trick lies in the 'Execute Sub-workflow' node: simply toggle off 'wait for subworkflow execution' and boom, each subworkflow runs asynchronously. Your tasks process in parallel rather than twiddling their thumbs in a queue. This approach shines when processing items independently, such as bulk data enrichment, multi-channel notifications, or parallel API calls. Instead of watching paint dry while your workflow crawls through each item, everything kicks off at once, slashing execution times. The n8n community has developed several patterns for this, including templates showing how to launch parallel subworkflows and then use a 'wait-for-all' loop to synchronise once the dust settles. Debugging becomes cleaner too, as failed subworkflows won't jam up the entire process. Fair warning though: parallelism demands more resources and adds complexity. Without solid error handling, you might find yourself chasing gremlins across multiple branches. Best suited for folks with some n8n experience, and keep an eye on your server resources. For teams scaling their automations, this parallel workflow pattern delivers substantial value. A practical way to squeeze more juice from your automation infrastructure while keeping operations ticking along nicely.

This $1.2B firm was losing 60+ hours/month to disconnected systems. The more they grew - the slower they became. The fix? Simpler and cheaper than you'd think: Calderys operates in 100+ global markets. But behind the scenes? A mess of disconnected workflows and manual re-entry. Orders got stuck between systems. Customer complaints piled up. Teams scrambled to reconcile spreadsheets across 30+ countries. Classic enterprise paradox: success creates its own bottlenecks. But the real cost wasn’t time - it was agility. While teams cleaned up data, customers waited. While leaders hunted reports, opportunities slipped. Here’s how they turned chaos into scale... The transformation started with one principle: 👉 Compliance and ops shouldn’t require heroic effort. They should run on rails. Step 1: They mapped every workflow. Over 200 manual touchpoints emerged. One order touched 12 spreadsheets before it shipped. Step 2: They rebuilt those workflows inside Process Street. Now data flows automatically. Tasks trigger in sequence. Every step gets tracked - no chasing, no chaos. Step 3: They rolled out real-time dashboards. Country managers got hours back each week. Executives saw risk early and acted faster. 📊 In 6 months, Calderys achieved: • 60+ hours/month reclaimed • Manual errors eliminated • New market expansion without adding ops headcount • Audit prep time cut from 2 weeks to 2 hours • 40% faster employee onboarding The turning point? They stopped trying to fix everything. And started by automating one painful process: order intake. That win changed everything. They had thought the challenge was tech... It was change. Once people saw their daily pain disappear? They became their biggest advocates. And that’s what Process Street enabled: Simple, scalable, audit-ready workflows that drive real results. --- If you’re scaling fast but feel slower than ever... If operations still run on spreadsheets and hope... If audits, onboarding, and reporting all require heroics... You’re not the problem. Your system is. At Process Street, we’ve helped firms like Calderys turn operational drag into acceleration. Interested in finding out how we can apply the same for your organization? Drop me a message, or check out the link in the comments below!

When it comes to AI agents and automation workflows, bigger isn't better. A large AI workflow might look impressive… but underneath the surface? 🔧 Hard to maintain. 🧩 Hard to debug. 🔁 Impossible to reuse. In theory, centralizing everything into one mega-workflow sounds powerful. In reality, it's a technical debt time bomb. 🧨 Large workflows often lead to: 🚫 Slower execution 🚫 Poor error handling 🚫 Messy dependencies 🚫 Blocked scaling Instead, embrace modularity: ✅ Smaller reusable agents ✅ Isolated responsibilities ✅ Clear inputs/outputs ✅ Scalable architecture The future of AI automation lies in clean, composable design, not visual chaos. 📌 Ask yourself: Are you building to impress, or building to scale? Let’s stop glamorizing bloat and start promoting smart systems. Thoughtful architecture > Impressive screenshots. 👉 Start building reusable agents with one of Airtop's free, ready-to-use templates - https://lnkd.in/gjBNhdBV

AI at work is no longer experimental. The leaders are scaling workflows into operating leverage. According to the World Economic Forum, 65% of companies now use generative AI in at least one function. Adoption is up. Budgets are rising. But scaling? That’s where most stall. Why? Because scale isn’t about tools, it’s about systems. The top performers do three things well: ✅Start with one high-value workflow ✅Build tight loops: clean inputs, clear finish lines ✅Track outcomes: hours saved, retries, $ per run They don’t boil the ocean. They ship, measure, and expand. If you’re a small firm wondering where to begin, here’s the play: ✅Choose one use case ✅Add relevance checks and human review ✅Measure ruthlessly ✅If it works, scale it Context + capability = compounding ROI. Start small. Ship it. Then scale. Follow me for more AI news and share this with your network.