If you've used Spring Batch in a production environment, you've likely received this advice countless times. Sometimes it succeeds. Many times it does not. When it does fail, it can lead to unexpected issues like timeouts, database overload, memory strain, or unnoticed slowdowns that only become apparent when service level agreements (SLAs) are not met.
The real problem isn’t Spring Batch. It’s the assumption that one static thread pool size fits all runtime conditions.
In this article, I’ll show:
- why static tuning fails in production
- how to design a safe, adaptive concurrency layer in Spring Batch
- and exactly where AI (or an LLM) can be plugged in without risking production stability
This is not an “AI hype” article.
It’s about building the right control surface first, then letting AI assist responsibly.
Why Thread Pool Guessing Fails
Spring Batch jobs don’t run in isolation. Their performance depends on:
- database contention
- JVM and GC behavior
- data volume growth
- external API latency
- traffic patterns based on the time of day
A thread pool size that is effective at 2 a.m. could ruin your database at 10 a.m.
Yet many batch jobs still depend on:
- hard-coded thread counts
- redeployments to tune performance
- tribal knowledge instead of feedback loops
This isn't tuning. It’s just guessing.
Primary Principle of AI in Production: Control Comes Before Intelligence
Before getting into AI discussion, would like to clarify a important thing:
AI cannot safely tune a system that does not have explicit, bounded control points.
That’s why the codebase for this article does not start with AI.
Instead, it establishes three critical foundations:
- A single concurrency control point
- Correctness under dynamic concurrency
- Hard safety guardrails
Only after those exist does AI make sense.
Part 1: Executor-Driven Concurrency
Spring Batch 5 deprecated throttleLimit() for a reason.
Concurrency should be controlled in one place: the executor.
@Bean
public ThreadPoolTaskExecutor batchTaskExecutor() {
ThreadPoolTaskExecutor exec = new ThreadPoolTaskExecutor();
exec.setCorePoolSize(4);
exec.setMaxPoolSize(8);
exec.setQueueCapacity(200);
exec.setThreadNamePrefix("batch-");
exec.setRejectedExecutionHandler(
new ThreadPoolExecutor.CallerRunsPolicy()
);
exec.initialize();
return exec;
}
This gives us:
- bounded memory usage
- backpressure instead of overload
- a single knob AI can safely influence later
Without this, AI has nowhere to act.
Part 2: Correctness Under Concurrency
Most Spring Batch concurrency bugs don’t show up in development. They appear only under load.
A classic example:
- enabling parallel processing
- using a non-thread-safe ItemReader
- random NullPointerException at runtime
The fix is simple but essential:
SynchronizedItemStreamReader<Integer>
This guarantees correctness even if concurrency changes dynamically at runtime.
AI + unsafe readers = outages.
Part 3: Guardrails Are Non-Negotiable
Before AI enters the picture, we enforce hard limits:
- maximum thread count
- bounded queues
- backpressure policy
- JVM-safe defaults
This means:
Even a bad AI recommendation cannot crash production.
This distinction matters more than the AI itself.
Where AI Actually Fits (The Right Way)
Now we can talk about AI—specifically where it plugs in.
The Control Loop
The architecture looks like this:
Runtime Metrics
↓
Decision Engine (Rules → ML → LLM)
↓
Guardrails & Bounds
↓
ThreadPoolTaskExecutor
AI is not the controller. AI is the advisor.
Phase 1: Rule-Based “AI” (Deploy This First)
Before implementing ML or LLMs, most teams should begin here.
if ( queueDepth > 100 && cpuLoad < 0.7 ) {
scaleUp();
}
if ( queueDepth == 0 && cpuLoad > 1.2 ) {
scaleDown();
}
Reasons for this importance:
- deterministic
- explainable
- production-safe
- easy to audit
This approach already surpasses static tuning.
Phase 2: ML-Based Recommendations
Looking at historical metrics for queue depth, throughput and latency, one can create a basic model:
int recommendedThreads = model.predict(metrics);
However, pay attention to what follows:
int safeThreads = clamp(recommendedThreads, MIN, MAX);
executor.setMaxPoolSize(safeThreads);
The model always adheres to safety limits.
Phase 3: LLM-Assisted Tuning (The Safe Pattern)
LLMs are strong—but risky if they have direct control.
The right pattern is:
String recommendation = llm.analyze(metricsJson);
int proposed = parseThreadCount(recommendation);
int bounded = clamp(proposed, MIN, MAX);
executor.setMaxPoolSize(bounded);
Key principle:
LLMs provide advice. Code implements it. This allows LLMs to be used in production systems.
Why This Approach Scales:
- Engineers can reason about behavior
- Operations teams retain control
- AI improves decisions without owning them
- Failures degrade safely
This is how adaptive systems survive real production environments.
When You Should NOT Use AI Here
Do not apply this pattern if:
- your reader requires strict ordering
- your writer is not idempotent
- you rely on cursor-based DB readers without partitioning
- your batch job is tiny and predictable
AI is not a silver bullet. It’s a multiplier, good or bad.
Why This Matters Beyond Performance
This approach isn’t just about speed.
It demonstrates:
- original system design
- production-grade thinking
- responsible AI integration
- measurable impact on reliability
These qualities give the engineering leadership edge and distinguish them from scripting.
Final Thoughts
Setting the correct thread pool size shouldn’t be a guessing game. In real production systems, workloads shift, data grows, and downstream services experience varying levels of pressure. Under these conditions, fixed concurrency settings become outdated very quickly.
By bringing all concurrency control into the executor, making sure the system behaves correctly under parallel execution, and putting clear safety limits in place, thread management can become adaptive rather than static. At that point, AI can play a meaningful role not as something that takes over the system, but as a guide that helps inform better decisions.
The result isn’t just faster batch processing. System which are stable, having flexibility and can adapt the changes as we go without redeploying the changes frequently. Its about minimizing the assumptions and enhancing the control within the system and allowing the system to adopt gradually intelligently.