TL;DR
Automation platforms are turning into CPUs — they just don’t realize it yet.
Like early processors, most automation systems still execute every trigger in isolation. But as scale, cost, and latency pressures rise, this model breaks down.
The next leap is to treat automation like compilers or LLM engines: detect shared logic, batch similar work (aka hot path), optimize the graph — not just react to every trigger individually.
Batching is often avoided due to fear of side effects from transitive state changes. But under the right conditions — when items share state, logic, and no dependencies — conditional batching becomes both safe and performance-critical. It’s a latent superpower waiting to be unlocked in platforms already strained by their own flexibility.
The Computation Race Is On
Thanks to AI, we’ve entered a new era in workflow automations — one defined by compute budgets, latency races, and infrastructure constraints. Let's call it what it is: the computation race.
Every modern automation engine is locked in a quiet arms race to:
- Cut compute cost per automation.
- Increase throughput under load.
- Reduce lag without compromising correctness.
Why? Because compute cost shapes pricing. The more efficiently automations run, the more generously a platform can structure its plans.
Just look at Make.com’s pricing — the “Ops” selector is one of the first decisions a customer makes:
As users create more workflows and expect real-time responsiveness, the old model — process each item one at a time — starts to break down.
Two sides of the story
There are two angles to automation optimization:
1. User-side: How users structure and trigger automations
2. Platform-side: How the system processes those automations under the hood
Let’s explore both.
User-Side: Real-Time vs. Batch-Aware
Consider this common workflow:
1. Real-Time Per-Item Automation
Trigger: When a project is marked "Complete"
Action: Call external invoicing API to create invoice
If 100 projects are completed in a day, the system fires 100 API calls. Each one carries overhead — auth, retries, latency. It feels immediate, but it’s noisy and costly at scale.
2. Batch-Aware Automation
Trigger: When a project is marked "Complete" → Add to 'Ready to Invoice' queue
Scheduled Automation: Every day, send a single API request with a batch of all pending projects
Same business logic — 100 invoices are created. But now: 1 automation run, 1 API call, fewer retries, less noise, better scaling.
Suggest It in the Builder
Platforms could detect high-volume, per-item trigger patterns and suggest batch-aware upgrades. This is where AI-assisted automation building comes in.
"Looks like you're creating an invoice every time a project is completed. Want to switch to hourly batch invoicing instead?"
The outcome is the same. The performance cost isn’t.
What we can learn from JVM and LLM
I'd go as far as to say that I'd let my automation runtime suggest such "hot path" optimisations as explained above.
In the JVM, HotSpot profiling identifies "hot" paths (frequently executed code), and rewrites bytecode on the fly with faster native instructions. It inlines methods, eliminates dead branches, and reorganizes memory based on real workload characteristics.
In LLM, PyTorch tracks frequently-used computation graphs. It also uses JIT (just in time) compilation (via TorchScript) to trace and compile hot subgraphs.
Operator fusion combines layers into a single fused operations.
Platform-side (computation race)
Let’s switch to the platform's challenge fully: offering efficient automation runs while keeping infrastructure costs low.
Every "when X then Y" hides a chain of processes: queuing, state validation, dependency resolution, retries, failure handling, side-effect isolation. Multiply that by millions of users triggering millions of events, and the difference between linear and batched execution becomes existential.
Batching or ("Hot Pathing") enables:
- Dependency flattening — Resolve shared lookups once, not per item
- State scoping — Compute shared logic across similar items
- Throughput scaling — Use batch APIs, optimize reads, amortize costs
Platforms that lean into batching win — not just in performance, but in pricing, latency, and developer ergonomics. They aren’t stuck paying the per-item tax.
Isolation Is Safe — But Expensive
On the surface isolation makes sense: when each item can have transitive states and trigger cascading actions, batching introduces uncertainty. If updating item A causes item B to change state, then grouping items A and B in a batch could lead to race conditions or misordered side effects.
In other words, individual execution ensures isolation — and isolation ensures safety. That's valuable.
Here is how we can observe per-item-isolation in Monday.com automations where I created a simple trigger of status change from 'Working on it' to 'Stuck'.
In the example above, web socket brings the mutation of each record in a seperate request. This safety comes at a cost: reduced throughput, increased infrastructure usage, and higher latency when dealing with large volumes of items that are eligible for automation at the same time.
So the question becomes:
Can we batch some items conditionally, without sacrificing correctness?
No-brainer batch cases
Some cases are obviously batch-safe:
- “Change status of 250 items to ‘Done’”
- “Assign 10 items to John”
- “Delete 80 items”
- “Archive 12 items”
- “Duplicate 15 items”
- “import item collection”
These can be easily triggered in batch on backend, and still be sequentially replayed on the frontend if needed: (e.g., “Running Automation on items 1…2…3…”) - or updated in one shot (depending on the scenario).
Other cases are trickier. Consider known cascade zones — like a status → status → webhook chain. Detecting and batching those safely takes design foresight.
What Makes Transitions 'Batchable'?
To be safely batchable, a group of items should:
- Be in the same state
- Trigger the same logic
- Have no direct or indirect dependencies
But enabling this isn’t just about checking flags. It requires the automation engine to develop a degree of self-awareness — the ability to reason about:
- Is this automation pure or mutative?
- Does this action cause side effects?
- Is this rule idempotent and order-independent?
In other words, the platform needs to understand what kind of work it's doing — not just execute logic in a blind, item-by-item loop.
This shift — from reactive ticking to introspective execution — is what unlocks batching as a safe, composable primitive in large-scale automation systems.
Why It’s Rare — But Strategically Valuable
Most automation platforms don’t support conditional batching — not because it’s a bad idea, but because it’s hard to retrofit into flexible, trigger-based architectures.
Users can define complex logic, create circular dependencies, or rely on item-specific side effects — all of which make batching risky.
But in many cases — stateless rules, disjoint segments, simple state transitions — batching is not just safe, but better.
Some enterprise tools already expose batch-aware options like:
- “Run once per group”
- “Run once per hour on matching items”
These unlock intelligent scheduling and reduce system load without breaking correctness.
Toward Batching-Aware Automation Models
Implementing conditional batching requires a rethink of automation internals. Options include:
- Static analysis of automation rules to determine side-effect scope
- Dependency graphs between items to detect safe partitions
- Execution labeling, tagging automations as batchable or not
- Explicit batching hints from users or templates
This isn’t trivial. But as automation scales from hundreds to millions of events per hour, platforms will need to get smarter about execution, not just faster at running one-off jobs.
Conditional batching is one such smart move.
Conclusion
Conditional batching and hot pathing is a quiet superpower.
Not a shortcut, but an evolution — from reactive ticking to shared understanding.
It’s how automation platforms can win in the computation race.
Think of this: Platforms like OpenAI or Anthropic couldn’t survive if they treated every token as an isolated compute unit. They batch, compress, and share as much as possible — and that’s exactly what modern automation platforms must do as user workflows scale and become more complex.
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