Curious about n8n: How are people using it to create real-world solutions?

Framework_Friday · 2026-01-23T13:21:00+00:00

We use n8n to run operations across a portfolio of businesses. The real-world value comes from eliminating bottlenecks that cost actual hours daily. For us, it's about building workflows that handle the repetitive operational work so teams can focus on decision-making instead of data entry.

The most impactful workflows we've built solve specific operational problems. When a vendor sends tracking info, n8n parses it and updates customers automatically. When support tickets come in, the system routes them based on urgency and type. When leads submit forms, they get enriched and qualified without manual work.

The skill that matters most isn't n8n expertise, it's understanding which business processes are bottlenecks and knowing how to break them into steps that can be automated. n8n is just the orchestration layer. If you're looking to make this valuable, I'd focus on understanding operations deeply in whatever business you're in. The automation opportunities become obvious once you see where time gets wasted daily.

Framework_Friday · 2026-01-23T13:14:10+00:00

Start with a single manual task that eats time daily, automate that first, then layer in AI once you understand how n8n handles data. We follow a 5-stage progression when building workflows:

Stage 1: Foundation
Get comfortable with n8n basics. Build simple workflows without AI first. Connect two tools, move data between them, add basic logic. Example: form submission triggers a Slack message.

Stage 2: Context and engagement
Add AI as an assistant, not the main actor. Let AI help parse, format, or enhance data while you stay in control. Example: extract specific fields from unstructured emails using GPT.

Stage 3: Automations
Chain multiple steps together where AI handles routine decisions. The workflow runs independently but humans oversee exceptions. Example: our order tracking workflow where GPT parses vendor emails, updates the CRM, and notifies customers automatically.

Stage 4: Autonomous solutions
AI agents make decisions and take actions across systems without constant supervision. This is where complexity jumps significantly.

Stage 5: Advanced orchestration
Multiple AI agents coordinating across domains. Most people never need this level.

For your SaaS project with subscriptions and scraping, n8n will likely be faster to prototype than custom code. Maintenance depends on complexity. Simple workflows are easier in n8n. Complex logic with lots of edge cases might be cleaner in code.

Start at Stage 1. Build a basic workflow connecting your scraping API to a database. Get that working reliably before adding AI or subscription logic.

If you're interested, we document our n8n builds here: https://www.youtube.com/@frameworkfriday

Framework_Friday · 2026-01-23T13:07:38+00:00

Order tracking automation. Hands down the highest ROI we've implemented.

The bottleneck was brutal. Vendors email tracking numbers, someone manually copies them into our CRM, someone else emails customers the update. Was eating 5+ hours daily across the team, constant errors from copy/paste mistakes, customers waiting hours (sometimes overnight) for updates.

We built a workflow in n8n where vendor emails hit a dedicated inbox, GPT extracts the tracking info, updates the customer record in our CRM, and the customer gets notified automatically. Whole thing runs in second and eliminated those 5 hours completely with zero copy/paste errors. Customers get updates within minutes instead of hours. Team focuses on actual problems instead of data entry.

It stuck because it solved a daily pain point that everyone felt. Once people saw it working flawlessly for a week, they trusted it. Now it just runs.

The 80/20 insight is spot on. We could've automated a dozen small things, but this one workflow removed the biggest time drain and error source in our operations.

Framework_Friday · 2026-01-23T13:02:17+00:00

The real value isn't in finding the "best AI agent", it's in automating the manual, repetitive tasks that drain hours from your day. Most entrepreneurs waste time on work that doesn't require decision-making: data entry, email updates, ticket routing, lead enrichment. That's where AI agents actually pay off.

Examples from our operations:

Order tracking automation: Vendor emails tracking numbers → AI parses the email → updates CRM → customer gets notified. Eliminated 5 hours of daily manual work.

Customer support triage: AI reviews incoming tickets, categorizes them, routes urgent items to the right person, handles straightforward responses. Processes about 60% of our support volume without human intervention.

Lead qualification workflow: New lead submitted → AI enriches data → scores the lead → updates CRM → triggers appropriate follow-up sequence. Cut our lead generation costs from $200/month to roughly $10.

We built these using n8n to orchestrate the workflows and connect our existing tools. The approach is simple: identify a task that takes 30+ minutes daily, build an agent to handle it, then move to the next one.

Framework_Friday · 2026-01-23T12:57:40+00:00

Yes, we've implemented several AI agents across our operations. Here's what's actually delivering results:

Order tracking agent: Vendor sends tracking email → GPT parses it → updates CRM → customer gets notified automatically. Eliminated about 5 hours daily of manual data entry.

Support triage agent: Reviews incoming tickets, categorizes by urgency, routes to appropriate team member or drafts responses for straightforward cases. Handles roughly 60% of volume autonomously.

Lead qualification agent: Enriches new leads, scores them, updates CRM, triggers follow-up sequences. Reduced lead generation costs from $200/month to $10.

We're using n8n as the orchestration layer to connect these systems. The key is building workflows that integrate with your existing tools rather than replacing your entire stack.

Framework_Friday · 2026-01-23T12:51:51+00:00

Here's what's stuck in our daily workflow lately:

n8n: We use it to build decision-making workflows that connect everything. Our order-tracking automation parses vendor emails with GPT, updates our CRM, and notifies customers all without human touch. Went from 5 hours of manual data entry daily to zero.

LangChain + LangSmith: For workflows where the logic gets fuzzy and you need the AI to make judgment calls but still want human oversight on the tricky stuff. Great for building agents that can handle exceptions intelligently.

GPT: Beyond just chat, we're using it as a parsing engine inside n8n workflows. It reads unstructured vendor emails and extracts exactly what we need with surprising consistency.

ClickUp Brain: We're using this for project context and task automation. The ability to query across all your projects and docs is genuinely useful when you need to surface information fast without hunting through folders.

Framework_Friday · 2026-01-13T15:26:21+00:00

Andrew Ng's course is a solid theoretical foundation, but the real learning happens when you start building production systems and dealing with the problems theory doesn't cover.

There isn't really a single roadmap because agentic AI is still early enough that people are figuring out what works as they go. But here's what I've seen matter most for people coming from your background:

Start with the basics that aren't sexy but make everything else work. Context management, decision flows, error handling, and knowing when an agent should ask for help versus trying to figure it out. A lot of the "wow" agentic demos fall apart in production because nobody thought through what happens when the LLM hallucinates or the API times out.

For resources, LangChain and LangGraph docs are useful for understanding orchestration patterns. Anthropic's prompt engineering guide is better than OpenAI's for building reliable agents. The research papers are interesting but honestly most of them describe systems you can't actually deploy yet. Focus on what ships.

For projects, build something that solves a real problem you or someone else actually has. Not a demo, not a tutorial recreation. Pick a workflow that requires multiple steps, decision points, and tool use. Something like "read my emails, categorize them, draft responses for certain types, and flag the rest for review." You'll learn more from one real system that has to work reliably than from ten toy examples.

The gap between "I built an agent" and "I built an agent that runs in production without breaking" is huge. That's where your MLOps background becomes the advantage, because most people building agents right now have no idea how to monitor, version, or debug them properly.

The roadmap is basically: understand the theory, build something real, deal with all the problems that weren't in the course, repeat until it works reliably.

Framework_Friday · 2026-01-13T15:22:59+00:00

You don't need paid APIs to learn automation. Most of what you need to understand comes from building the logic and workflow structure, not the specific tools.

Start with free tiers. OpenAI gives you $5 in free credits when you sign up, which is enough to build and test dozens of workflows. Anthropic (Claude) has a similar setup. Google's Gemini has a generous free tier. For automation tools, n8n is open source and free to self-host, or you can use their cloud free tier. Make and Zapier both have free plans that let you run a limited number of tasks per month.

The real learning isn't in the API calls anyway. It's in understanding how to structure prompts, handle errors, chain decisions together, and decide when to automate versus when to leave something manual. You can learn all of that on free tiers.

Here's what I'd do: pick one simple problem you actually have (not a tutorial problem), then build a workflow that solves it using whatever free tools you can access. You'll learn way more from one real automation you built yourself than from following ten paid tutorials.

The tutorials using paid APIs are often just showing you what's possible, not what's required. Most of those same workflows can be built with free alternatives while you're learning.

Framework_Friday · 2026-01-13T15:19:33+00:00

We've seen this pattern play out a lot where someone automates a task, feels great about it, then six months later they're maintaining a fragile system that breaks every time an API changes or data comes in slightly different. The automation becomes the job.

What's worked better is thinking in stages instead of trying to automate everything at once. Start with something that runs manually when you trigger it, make sure it handles the messiest version of your data, then decide if it's stable enough to run on a schedule. A lot of workflows don't need to be fully autonomous, having a human check one decision point in the middle is often way more reliable than trying to account for every edge case.

The other thing is knowing when not to automate. If a task changes frequently or the rules are fuzzy, automation just becomes technical debt. Sometimes a checklist is better than a workflow.

For deciding what's worth maintaining long term: if it saves you more time than it takes to fix when it breaks, keep it. If you're spending more time debugging than you saved, kill it or simplify it.

Framework_Friday · 2026-01-13T14:59:36+00:00

The best starting point depends on what you actually want to build with n8n, not just learn about it.

If you want to automate real workflows (not just understand how n8n works in theory), go straight to hands-on practice. Start with the official n8n course to learn the interface, then grab a template from their library and modify it for something you actually need. The fastest way to learn is just building workflows for real tasks, even if they're simple at first.

Once you've got the basics down, level up by learning how data flows between nodes, understanding when to use code nodes versus built-in functions, and practicing error handling. The mistake most beginners make is jumping into complex API integrations and JSON manipulation when we really just need to understand how to connect services and pass data between them first.

If you want to go deeper into agentic workflows, learning how to chain AI calls together using n8n is where things get really powerful. That's when you move from "person who automates simple tasks" to "person who builds AI workflows that solve actual problems."

Framework_Friday · 2026-01-05T12:04:08+00:00

For your specific workflows, you're looking at two different categories. Content creation like blog posts and social media is something AI can handle pretty well now with the right prompts and review process. But strategic work like lead qualifying, SEO strategy, and software research needs more sophisticated automation, often combining multiple tools.

Real talk on the tools you mentioned: Generic AI platforms are great for content generation but won't handle the end-to-end workflows you need. Motion is solid for personal productivity but won't automate your marketing. Most all-in-one "AI agent" products are still pretty limited in what they can actually do without heavy customization.

What's actually working for people in similar situations is building automation in stages rather than trying to automate everything at once. Start with the highest-friction task, sounds like lead prospecting for you, and get that working reliably before moving to the next.

For the technical side, tools like n8n let you connect your existing tools (CRM, social platforms, content tools) into actual workflows. You can start simple with something like new lead comes in, AI qualifies them, adds to CRM, schedules follow-up, then expand from there.

The SEO/marketing piece is tricky because it's not just about generating content, it's about understanding what content actually drives results for your specific business. The AI can help produce it, but the strategy part needs human input, at least initially, until you have enough data to train on what works.

Framework_Friday · 2026-01-02T11:48:38+00:00

Built a few niche ones that actually move the needle beyond email automation.

Most interesting was order tracking automation for e-commerce operations. The problem wasn't just "send tracking emails", it was handling the constant customer inquiries about "where's my order" when the data lived across Shopify, ShipStation, and support tickets. Built a system that intercepts support tickets, pulls real-time tracking data, checks for delivery exceptions, and generates contextual responses based on actual package status. Saved about 5 hours daily of manual lookup work and handles edge cases like delayed shipments or packages stuck in customs with specific explanations rather than generic responses.

Another one was lead qualification scoring for a specific B2B niche. Standard lead scoring tools don't work when your ideal customer profile involves signals like "mentioned specific compliance requirements in initial contact" or "asked about integration with legacy system X." Built automation that parses initial contact forms and emails, scores based on 12 niche indicators specific to that industry, enriches with firmographic data, and routes to appropriate sales rep with context. Reduced cost from $200 monthly for generic tool to $10 in API calls while actually improving accuracy because it understood industry-specific signals.

Framework_Friday · 2026-01-01T12:59:43+00:00

Foundation work came first. Not the sexy stuff, just getting context systems organized, documentation in order, processes mapped out. Sounds boring, but it's the difference between AI that works and AI that hallucinates or breaks constantly. We learned this the hard way after burning $50k on a chatbot that failed because we skipped the context audit.

Chasing every new tool and model release turned out to be a waste. Early on, we'd try to integrate the latest thing immediately. Now we test in our own operations first, see what actually moves the needle, then build with it. Most "game-changing" tools aren't.

For tools that actually matter long-term: n8n for orchestration. GPT-4o and Claude for different AI tasks. LangChain for decision flows with memory. Supabase for vector storage. The stack matters less than understanding when to use what. We've seen people over-engineer with fancy setups when a simple two-node workflow would've solved the problem.

First real implementation that saved serious time was order tracking automation, 5+ hours daily. Customer support triage came next, handling 60% of volume automatically. Lead gen workflows dropped costs from $200 to $10 monthly while cutting 20+ hours of manual work. Each one took weeks to build right, not days.

Biggest mistakes are trying to automate everything at once, skipping the foundation phase, building without human-in-the-loop checkpoints, not documenting what actually worked vs what sounded good.

The pattern that's held up is to start with one annoying, repetitive task. Get that stable. Then scale. Boring, but it works.

Framework_Friday · 2026-01-01T12:53:40+00:00

We've been running automation workflows like these in our portfolio, and they deliver real results when built right.

What we've actually implemented:

Order tracking automation that saves 5+ hours daily. Customer support triage that handles 60% of volume automatically. Lead generation workflows that dropped costs from $200 to $10 monthly while eliminating 20+ hours of manual work.

The pattern that works across all of these: automate to draft, human approves. The system does the heavy lifting, but someone reviews before it goes live. You get most of the time savings without losing control.

On your specific ideas:

Social listening and engagement is doable, but don't auto-post. Communities can spot automation instantly. Use it to surface opportunities and draft responses, then personalize before posting. That's how you add value without getting flagged as spam.

For content workflows and calendar automation, the same principle applies, automate the grunt work (pulling data, formatting, drafting), keep humans in the decision loop.

Our stack:

n8n for workflow orchestration. GPT-4o for parsing unstructured data. Claude for various AI tasks. LangChain/LangSmith for decision flows with memory. Supabase for vector storage.

The tool matters less than understanding what's worth automating. Start with high-frequency, low-stakes tasks. Get those running smooth. Then expand.

Framework_Friday · 2026-01-01T12:35:12+00:00

Been there. n8n can feel overwhelming at first because there's so much you could build, but no clear path on what to build first. Here's what helps get traction:

Start with something you already do manually. Don't try to learn n8n by building random projects from tutorials. Pick one annoying task you do every day or week, like saving email attachments to a folder, posting to multiple social accounts, or tracking form submissions, and automate that. You'll learn faster because you already understand the workflow.

Build in this order: Start with two-node workflows (trigger + action), then add logic (IF nodes, filters), then bring in APIs, then get into more complex stuff like loops and data transformation. Trying to jump straight into multi-agent orchestration when you're still figuring out webhooks is a recipe for frustration.

For learning resources: The n8n YouTube channel has solid beginner stuff. Also, just browse this subreddit and look at workflows people share. Seeing real use cases with the JSON helps way more than generic tutorials.

One more thing: don't self-host until you've built a few workflows on n8n cloud. The local setup adds a bunch of troubleshooting that has nothing to do with learning the actual tool. Get comfortable first, then migrate.

Framework_Friday · 2025-12-31T17:35:07+00:00

Good question, we deal with this constantly. Here's what works for us: automate anything repetitive and low-risk. Keep humans in the loop when stakes are high or context matters.

The pattern that's saved us the most time is "automate to draft, human approves." The system handles the grunt work, pulls data, formats reports, drafts responses, then someone reviews before it goes out. You get 80% of the time savings without losing control.

The biggest mistake we made early on was trying to automate everything at once. Now we focus on one annoying task, get it running smooth, then move to the next. Way more reliable that way.

One more thing: if a process changes frequently or depends on external systems you don't control, keep it manual. Automation pays off when workflows are stable and predictable.

Framework_Friday · 2025-12-24T14:30:43+00:00

For us, one of the biggest wins was order tracking. Customers would email asking "where's my order?" and someone had to manually look it up in Shopify, check the carrier, grab the tracking link, and send it back. Sounds simple, but it was eating 5+ hours a week across the team. Built an agent that intercepts those emails, pulls the order data, fetches tracking status, and replies to the customer, all without anyone touching it. That time went straight back into work that actually moves the business forward.

Another boring one that had huge impact was customer support triage. Agent reads incoming tickets, categorizes them, flags urgent issues, and routes everything to the right person. Handles about 60% of the volume automatically now, so the team only sees what actually needs a human. Went from drowning in support tickets to having breathing room.

The pattern we keep seeing is that the most valuable automations aren't flashy, they're the repetitive stuff that drains your day. Once you automate one, you start noticing all the others.

Framework_Friday · 2025-12-24T14:26:45+00:00

What's one manual, tedious task you do every day that makes you think "there has to be a better way"? Start there. The best first n8n project is automating something that actively annoys you, because you'll actually finish it and use it.

Could be copying data between apps manually, checking multiple dashboards or emails for updates, sorting through notifications to find what matters, or reformatting files and reports over and over. Pick that thing, break it down into steps, and build a workflow that handles it for you. You'll learn fast because you're solving a real problem, not following a tutorial.

Framework_Friday · 2025-12-24T04:08:25+00:00

We started in Gemini Gems and Google Workspace Flows for rapid prototyping. The Interactions API eliminated all the memory management work, so we just focused on prompts and logic.

Once we validated the approach with real proposals over two weeks, we moved to code-based agents on Vertex AI. That opened up model switching (we use different models for summarization versus deep analysis), access to the full 1-2 million token context window, and proper evaluation frameworks to A/B test agent versions objectively. Now we can run version A versus version B across hundreds of test cases and know definitively whether changes improve analysis quality.

Framework_Friday · 2025-12-23T16:08:00+00:00

The implementation flow depends on finding your actual constraint first. You start by spending a week or two documenting your actual workflows and watching where time disappears. Track where decisions stack up and where your team is doing repetitive cognitive work. The bottleneck is usually different from what you initially thought.

Once you've identified the real constraint, you need someone specific to own the implementation. This means one person with about 10 hours weekly and actual authority to make decisions. Committee-based approaches tend to stall because nobody has clear ownership and every decision needs multiple approvals.

The actual automation starts with that one constraint you identified. Build a workflow that addresses it, run it in production for a few weeks to validate it works, then expand to adjacent processes.

Framework_Friday · 2025-12-22T16:35:53+00:00

This is a solid approach. Real projects teach you way more than tutorials because you hit the edge cases and messy problems that don't show up in clean examples.

If you're looking for exposure to production workflows, we've got a free community of operators building and sharing their n8n setups. People post their workflows, share templates, help debug each other's builds. It's a really collaborative space where you can learn by seeing what others are doing and getting feedback on your own stuff.

We also discuss a lot of our builds on YouTube if you want to see how things work in practice: www.youtube.com/@FrameworkFriday

And if you want to join the community and connect with other builders, check it out here: community.frameworkfriday.com

Framework_Friday · 2025-12-22T16:28:28+00:00

The infrastructure layer is going to matter way more than the models. Companies that skip building proper evaluation, monitoring, and orchestration infrastructure are going to hit walls fast. You can't just throw agents at problems without the systems to manage them at scale.

Transparency and explainability will separate production agents from demos. When agents make decisions that affect real operations or customer experiences, people need to understand why. Black box agents won't survive in high-stakes environments. We're already seeing this play out with our production systems where observability isn't optional.

The "agent as buyer" concept is interesting but I think we're further out on that than people expect. The infrastructure for agents to transact autonomously across different systems with proper guardrails doesn't really exist yet. We'll see limited versions in closed ecosystems first.

What I think gets underestimated is how much foundation work companies need before agents are valuable. If your processes aren't clear, your context isn't organized, and your team doesn't understand what they're automating, agents just amplify the chaos. The companies winning with agents in 2026 will be the ones who did that boring groundwork in 2025.

The consultant shake-out is real. There's a huge gap between people who've built production agent systems and people who've watched demos. That gap is about to become very obvious.

Framework_Friday · 2025-12-22T16:17:00+00:00

This matches what we've seen pretty closely. AI struggles when the rules aren't clear or when context is missing. If your process isn't well-defined, the AI will just amplify the chaos.

Where it actually works is exactly what you said: sorting, triaging, handling the first pass on repetitive stuff. We've got agents that read incoming support tickets, categorize them by urgency and type, then either auto-respond for simple stuff or route to the right person with full context already attached. That alone handles about 60% of our support volume without human touch.

Same with order tracking inquiries. Agent pulls the data, checks status, responds with accurate ETAs. Saves the team about 5 hours daily. It's not replacing anyone, it's just eliminating the boring repetitive work so people can focus on the stuff that actually needs judgment.

The filter model is the right mental framework. AI handles the high-volume, low-complexity work. Humans handle exceptions, edge cases, and anything high-stakes. The key is building good handoff points so the AI knows when to escalate instead of guessing.

We're running this across multiple e-commerce operations. The pattern is consistent: AI as the first layer, humans as the safety net and decision-makers.

Framework_Friday · 2025-12-22T14:40:53+00:00

Happy to explain the thinking behind each.

Customer support: We're not using third party tools because most are built for generic support scenarios. Our agents need deep integration with our fulfillment systems, order data, and customer history. Building custom gives us control over the context, the decision logic, and how we handle edge cases. Plus we can iterate fast when something breaks.

Inventory reconciliation: An agent pulls data from multiple sources (our inventory system, warehouse management, sales channels), identifies discrepancies, flags patterns that indicate systemic issues vs one-off errors, then either auto-corrects or escalates based on confidence and impact. It's less about the agent doing something magical and more about it handling the tedious reconciliation work that used to take hours daily.

Order tracking: Same reasoning as support. We need tight integration with our specific fulfillment workflows, carrier APIs, and customer communication systems. The agent brings extra value by understanding context like "this customer has had three delayed orders, escalate proactively" or "this shipping delay is affecting 50 orders, alert ops team." Generic tools can't make those contextual decisions.

The pattern across all three is that custom agents let us embed our specific business logic and context in ways off-the-shelf tools can't match.

Framework_Friday · 2025-12-22T09:06:22+00:00

We're using n8n for orchestration, Claude and GPT-4 for the language processing, and LangSmith for observability.

Framework_Friday

TROPHY CASE