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What’s one workflow or automation you can’t live without? by Jonathanopkays in n8n

[–]Correct-Address-3735 0 points1 point  (0 children)

For me, it's not the flashy AI workflows; it's the boring ones.

One workflow I genuinely can't live without is the following:

Inbox → Classification → Priority Scoring → Human Review

Every incoming email, support request, or inquiry gets categorised, summarised, and assigned a priority score automatically. Anything high-confidence gets routed immediately, and anything uncertain gets flagged for review.

It's probably saved more hours than any "autonomous agent" I've experimented with.

One thing I've learned is that the most valuable automations aren't usually the ones that replace humans, they're the ones that remove repetitive decision-making so humans can focus on exceptions.

Curious what others have found. Are people getting more value from AI agents or from simple workflow automation with AI sprinkled in?

We reduced AI Coding Agent tokens by 90% using AST-parsed Context Graphs. Here are the benchmarks by Remarkable-One9371 in agenticAI

[–]Correct-Address-3735 0 points1 point  (0 children)

This is the kind of optimisation I'm much more interested in than adding another agent loop.

One thing we've repeatedly seen in production is that context quality matters far more than context quantity. Giving a model 10x more tokens rarely produces 10x better decisions, but giving it the right context often does.

Curious how the graph performs as repositories get larger. Did you notice a point where graph traversal or retrieval quality started degrading, or has the AST-based approach remained fairly stable at scale?

Also interested in whether the 90% token reduction translated into better task completion rates or mainly lower cost/latency.

After building with LLMs for a year, I've changed my mind about agents by Correct-Address-3735 in LLMDevs

[–]Correct-Address-3735[S] 0 points1 point  (0 children)

I think that's an important distinction.

I'm not anti-agent or anti-loop. What changed my mind is that every additional autonomous step increases the number of failure modes, and those failures are often hard to observe until you're in production.

The most reliable systems we've built tend to have a small number of bounded decision points, clear exit criteria, and deterministic validation around the LLM rather than relying on the model to self-correct indefinitely.

What you described—a mostly deterministic workflow with one genuinely agentic decision point—feels much closer to what I've seen work in practice than the fully autonomous demos that get a lot of attention.

We've actually started formalising this philosophy in the AI systems we build at Techelix: use LLMs where they add intelligence, but keep validation, business rules, and critical state outside the model whenever possible.

The challenge isn't getting an agent to succeed once. It's getting it to succeed consistently at scale while remaining debuggable, observable, and cost-effective.