I built an autonomous, end-to-end AI Sales Development Representative (SDR) engine entirely in n8n. On a scheduled trigger, the agent calculates targeting parameters, reads long-term CRM memory to avoid duplicate outreach, searches for and qualifies prospective leads, scrapes company websites for buying signals, drafts tailored outreach emails, writes structured relational data to PostgreSQL, and reports execution summaries through Telegram—with zero manual intervention. The system is currently deployed in production for a B2B agricultural export business, generating qualified international wholesale leads on a recurring schedule. Most AI automations rely on simple linear scripts or break down when handling complex agentic tool workflows. This system addresses three common failure points: - High API costs: Re-sending large system prompts and tool schemas on every agent iteration drains tokens. - Context blindness: Agents without memory of previous contacts can send duplicate outreach. - Database crashes: Agents may hallucinate ENUM values or fail to insert nested one-to-many arrays into relational tables. The workflow uses a Cloudflare-proxied Claude Sonnet 4.6 model with prompt caching, persistent CRM memory reads, and a fault-tolerant parallel database-write architecture. The stack includes n8n as the orchestrator; Claude Sonnet 4.6 through a Cloudflare Worker proxy as the LLM core with ephemeral prompt caching; PostgreSQL for CRM contacts, intelligence, and outreach tables with custom ENUMs; SerpAPI for prospect discovery; Firecrawl for website content extraction; and Telegram for execution reporting. The workflow exposes these tools to the n8n agent: - `read_relationship_memory`: Read-only SQL access to historical contact and outreach data, preventing duplicate prospecting. - `Lead_Finder`: Searches for and identifies target prospects by country and sector. - `Scrape_Website_Content`: Extracts website content, buyer-intent signals, and objections from discovered domains. - `write_relationship_memory`: Writes leads, intelligence facts, and drafted emails to Postgres in one resilient call. Step-by-step: 1. A Schedule Trigger feeds a JavaScript “Country Calculator” node that resolves the day’s targeting parameters—region and industry focus—using ISO week rotation. This cycles outreach across markets automatically. 2. The AI Agent connects to an OpenAI Chat Model node whose Base URL points to a custom Cloudflare Worker. The worker translates OpenAI-formatted requests into Anthropic’s Messages API, enabling Claude Sonnet 4.6 while injecting ephemeral cache-control headers into the system prompt and tool definitions to reduce repeat-token costs. 3. Before researching, the agent calls `read_relationship_memory` to check relationship status and outreach history, preventing duplicate contact attempts. 4. `Lead_Finder` searches target sectors in the day’s region and returns seven filtered candidates. `Scrape_Website_Content` then visits each domain, extracts clean page text, and surfaces offerings, value propositions, and likely objections. 5. The workflow writes nested one-to-many data—multiple facts and one outreach log per contact—without item duplication or ENUM crashes. The tool schema requires a strict JSON array with exact ENUM string choices spelled out in the description. 6. A sub-workflow triggered by “When Executed by Another Workflow” splits the array, then flattens nested `contact.*` fields to root keys using JavaScript. 7. An upsert query, `ON CONFLICT (email) DO UPDATE`, writes the contact, increments `email_count` for repeats, and returns `contact_id`. 8. A “Re-attach Context” node merges `contact_id` back with the original intelligence array and outreach payload because n8n strips extra data through single-row database nodes. 9. Two parallel branches run: one inserts the outreach log with `ON CONFLICT DO NOTHING`, while the other splits and inserts each intelligence fact with defensive ENUM sanitization. This eliminates crashes and duplicate rows during retries. 10. The agent’s final output triggers a Telegram message summarizing the discovered leads, extracted facts, and drafted emails, sent directly to the operator’s phone. The result is a production-grade, self-healing AI outbound pipeline running with zero manual intervention. It maintains CRM data integrity, avoids duplicate outreach, and uses prompt caching to keep LLM costs low at scale.

An AI agent scrapes book marketplaces for recent releases, crawls the web for the corresponding authors’ email addresses, and generates customized marketing pitches based on the authors’ own book descriptions. Just kidding—I’m an author, not an agency. I receive about half a dozen such email pitches every day. I added a spam rule that looks for the text "your book" and sends those messages to spam because my email host obviously isn’t using a smart enough AI-based spam filter. Step-by-step: 1. An AI agent scrapes book marketplaces for recent releases. 2. It crawls the web for the corresponding authors’ email addresses. 3. It generates customized marketing pitches based on the authors’ own book descriptions. 4. I receive about half a dozen of these pitches every day. 5. I use a spam rule that looks for the text "your book" and sends the messages to spam.

It starts with one scheduled job that runs at 6 a.m., before I open anything. Every automated task in my business writes a single line to a shared table when it finishes. That log records which job ran, when it ran, and whether it succeeded. It is the foundation of the whole system because I am checking for silence instead of waiting for something to alert me. Step-by-step: 1. The 6 a.m. job reads the log and lists anything that was supposed to run in the last 24 hours but never checked in. 2. It pulls each client’s spend and traffic directly from the ad and analytics platforms. 3. It calculates where spend should be for that day of the month: monthly budget divided by the number of days in the month, multiplied by the number of days elapsed. Anything more than about 15 percent off pace is flagged. Accounts that return an authentication error are also flagged, which helps catch a dead access token before the client does. 4. It sends all of that to Claude along with a short brief on each client. Claude writes one email with broken items at the top in plain English, followed by a single line for each account that is fine. 5. The same table feeds the live dashboards clients use, so a monthly report is a link I already have rather than something I need to assemble. To rebuild this today without much code, I would start with the smallest version: Step-by-step: 1. Create a spreadsheet where each automation logs a row. 2. Add one scheduled script that emails me whatever is missing. 3. Use a Claude prompt to turn that raw list into the email. 4. Add the spend checks once the heartbeat is working. Alerting on silence is the part that does the real work.

I’ve been using the Awish app I built less as an automation builder and more as a way to solve specific business problems people bring me. One hotel client, Aurelia Bay Hotel, had an interesting marketing problem. They could see travel trends across search, Instagram, YouTube, website traffic, and ads, but connecting those signals early enough to spot a real campaign or guest-experience opportunity was difficult. I built an automation for them with Awish and gave it to them to test. Step-by-step: 1. I opened the Awish chat and wrote: “For Aurelia Bay Hotel, monitor Google Trends, YouTube, Instagram, Google Analytics, and Google Ads every day. Find rising interests around Dubai travel, staycations, family trips, wellness, dining, events, and hotel experiences. Cross-check those trends with social engagement, website behavior, and campaign performance. Rank the strongest opportunities, send them to the marketing team in Microsoft Teams for approval, and create a campaign or guest-experience opportunity brief in SharePoint for anything they approve.” 2. Awish understood what I wanted, planned the complete workflow, and selected the apps required for each step. 3. I reviewed the plan, connected the client’s accounts, and approved it. 4. Awish added Microsoft Teams as the human approval step and SharePoint for the final opportunity briefs. 5. Once everything was connected, Awish built the workflow and started running it daily. 6. It now cross-checks trend growth, social engagement, website behavior, and ad performance. When it finds a strong opportunity, the marketing team gets the evidence in Teams. If they approve it, Awish creates the full campaign or guest-experience brief in SharePoint. The useful part is that a trend isn’t treated as an opportunity just because it is growing on one platform. Awish checks whether the same signal is appearing across the business before recommending action. Do you have any time-consuming, repetitive tasks or problematic aspects in your job? I would be happy to develop an automation solution for you as well.

I have been testing Perplexity Computer, and it really makes you rethink a lot of workflows. It has been particularly useful for surfacing interesting and viral social content across X and Reddit, with internet search abilities that seem much more capable for quick canvassing of different platforms. I have also used it for clipping and captioning video content for social, with Computer able to find, transcribe, give recommendations on the most viral quotes/sections, and caption in just minutes — a wild process that would normally take me (with little video experience) much longer and have to leverage a ton of different apps to make happen. Step-by-step: 1. I used Perplexity Computer to canvass X and Reddit for interesting social content with signs of viral momentum. 2. I narrowed the results to the posts and videos worth developing further. 3. I had Computer find and transcribe the source video content. 4. I asked it to recommend the strongest quotes and sections for short-form clips. 5. I used the same workflow to clip and caption the selected sections in minutes.

I kept struggling to stay consistent with social media for my own projects. Writing captions, choosing hashtags, designing graphics, and scheduling content across platforms was taking hours every week—time I would rather spend building. So I built AutoKonnekt. You give it one sentence or a website URL, and it generates a full month of on-brand social posts, including captions, hashtags, and AI-generated images. It then schedules them across Instagram, Facebook, LinkedIn, TikTok, Pinterest, X, and Threads. The hardest part was getting the AI to sound like a specific brand’s voice instead of producing generic marketing copy. That took a lot of iteration on the prompting side. It’s live now with a free plan if anyone wants to try it: https://autokonnekt.com I’d love to hear what the community thinks. For those of you managing social media manually, which part takes the most time? I’m trying to figure out what to build next. Step-by-step: 1. I enter one sentence describing the project or provide a website URL. 2. AutoKonnekt generates a month of on-brand social posts with captions, hashtags, and AI-generated images. 3. I use the generated content across Instagram, Facebook, LinkedIn, TikTok, Pinterest, X, and Threads. 4. AutoKonnekt schedules the posts across those platforms. 5. I iterate on the prompting to make the content sound like the specific brand instead of generic marketing copy.

Claude Cowork has been a huge help in creating growth forecasts for our paid media channels. I just plug in our metrics, tell it what goal I'm trying to achieve, and it's able to create an Excel sheet for me with exactly what I need. This saves me a bunch of time because I can create multiple scenarios just by chatting with Cowork. Step-by-step: 1. I gathered the paid-media metrics and the business goal the forecast needed to model. 2. I gave the inputs to Claude Cowork and asked it to build the forecast directly in Excel. 3. I reviewed the structure, assumptions, and outputs of the generated sheet. 4. I changed the assumptions conversationally to create additional scenarios. 5. I compared the scenarios without rebuilding the spreadsheet model by hand each time.

We're always looking for A/B test ideas for our landing pages and new ways to improve conversion rates while creating a better user experience. Recently, I tried an interesting AI workflow after coming across a public CRO skill on Twitter. I installed it in Claude Code and then fed Claude screenshots of our landing page along with behavioral data from Microsoft Clarity, including scroll depth, heatmaps, and which buttons people were clicking most. Using the CRO scorecard framework and the Clarity data, Claude generated a detailed analysis of the page and recommended five A/B tests we should prioritize, along with the reasoning behind each one. The insights were genuinely useful, and we have already taken action by launching one of the recommended tests. Step-by-step: 1. I installed a public CRO skill in Claude Code and used its scorecard as the evaluation framework. 2. I supplied screenshots of the landing page along with Microsoft Clarity scroll depth, heatmaps, and click data. 3. I asked Claude to analyze the design and behavior together rather than treating screenshots and analytics separately. 4. I had it recommend five A/B tests and explain the evidence and reasoning behind each one. 5. I prioritized the list and launched one of the recommended tests.