Automation Revamp for Tech Support Platform

@nicolasl63

I read everything — this isn’t about “making automations work”, it’s about making them predictable, traceable, and impossible to silently fail. That’s the right approach. 1. Why automations fail silently Because there’s no enforced state + no validation before/after each step. Triggers fire, but nothing checks if the system is in a valid state or if the action actually completed. No logging = no visibility. 2. Preventing duplicate SMS Assign a unique lead ID + event ID (idempotency key) Before sending SMS → check last action in the lead state (Google Sheets / DB) Lock execution (flag like sms_sent = true or timestamp check) Handle retries safely (only resend if previous attempt failed, not just retriggered) How I’d approach your system Audit all Make scenarios → remove duplicate triggers, fix race conditions Centralize everything around a canonical lead state (Google Sheets) Enforce rule: no action runs without validating state first Standardize Twilio flows (inbound/outbound + AI calls via Bland) Add logging layer: execution logs per scenario error logs + alerts (Slack/email) Add fail-safes for webhooks (retries, validation, fallback paths) Reduce unnecessary operations to cut Make costs I’ve worked on similar systems where the main fix was moving from “event-based chaos” to state-driven automation, which eliminated duplicates and blind spots completely. I can start with a test task and show logs + proof of execution before moving forward.

@ProfSoftStudio

AGOD SYSTEMS Hi, I specialize in Deterministic Automation Architecture. I don't just "link apps"; I build production-grade systems with state validation and error logging to eliminate silent failures. 1. Why automations fail silently: Lack of error-path routing. Most scenarios only account for the "happy path." I use global error handlers and "Dead Letter Queues" to ensure every failed operation is captured and alerted. 2. Preventing duplicate SMS: I implement a "Look-before-leap" logic. The scenario queries a Data Store/Cell for a locked state or last_sent timestamp before firing the Twilio module. If the state is active, the process terminates. My Approach for AGOD: Unique Lead IDs: Every row in Google Sheets acts as the "Source of Truth" for state checks. Modular Scenarios: Decoupling triggers from actions to prevent race conditions. Logging: Centralized execution logs for real-time monitoring. I’ve built complex Twilio/Make systems and am ready for the 4-hour test task to prove my reliability. Best regards

@TyeStewart

I already see a clean way to execute this. I specialize in building and fixing revenue-critical automation systems with Make, Twilio, and AI—specifically around support workflows, lead handling, and follow-up systems. I’ve worked on similar “must-not-fail” setups where every missed message or delay directly costs revenue, so reliability and proof of performance are always front and center. You want your tech support platform automation fully revamped so it’s stable, fast, and trackable—Make scenarios clean, Twilio messages delivered and logged, AI replies accurate, and clear proof that the system is working and driving revenue instead of leaking it. My focus would be on auditing your current Make+Twilio+AI flows, tightening the logic, adding proper monitoring and fail-safes, and setting up simple reporting so you can actually see what’s working and what’s not. Quick question: do you already have examples of “ideal” support or sales flows that we should use as the benchmark for this revamp? Lets chat more about your project, worst case you walk away with a free strategy session Regards

@sergiom17

Hi, AGOD SYSTEMS. The number one reason automations fail silently is lack of deterministic state validation and observability—flows execute without checking current state, and failures aren’t logged or surfaced, so errors go unnoticed. To prevent duplicate SMS sends, I implement idempotency at the system level: each action is tied to a unique lead ID plus an event key (e.g., lead_id + “sms_sent_stage_1”). Before sending, the system checks state in the source of truth (Google Sheets or DB). If already executed, it skips. I also add locking (timestamp/status flag) and webhook deduplication using request IDs. Example system: I built an event-driven automation pipeline using Make + Twilio + backend validation layer where: Every lead had a canonical state record All webhooks passed through a validation router (preventing race conditions) Twilio messaging was idempotent and logged per event Failures triggered Slack alerts + retry queues Execution logs were stored for every step (audit trail) For your system, I’d: Audit and simplify Make scenarios Introduce strict state-driven execution (no action without validation) Normalize Twilio flows (inbound/outbound + AI triggers) Implement logging, retries, and alerting Eliminate duplicate triggers and race conditions I’m comfortable starting with a test task and proving everything in production with logs and validation. Looking forward to speaking with you. Sergio

@manpreetkaur991

Hello Greetings, After reviewing your project description, I feel confident and excited to work on this project for you. But I have some crucial things and queries to clear out. Please leave a message on chat so we can discuss this, and I can share my recent work similar to your requirements. Thanks for your time! I look forward to hearing from you soon. Best Regards.

@teerthofficial

Hello there, I’ve carefully reviewed your project details and fully understand your requirements. I’m confident that I can deliver high-quality results that meet your expectations within the given timeframe. I’d be happy to discuss your project further and get started right away. Best regards, Thanks