While testing AI agents, I’m noticing a recurring issue: sometimes the agent produces incorrect responses, and in some cases, it fails to access tools that are explicitly required at the beginning of the workflow—even when the instructions are clearly defined in the prompt. I’ve been tightening the prompts and making changes regularly, but the same issues still occur intermittently. My question: How do you handle these kinds of reliability issues when moving AI agents from testing to production? Do you rely on prompt engineering alone, or do you use guardrails, validation layers, tool-call enforcement, retries, logging/monitoring, or other techniques to make agentic workflows production-ready? Would appreciate insights from anyone who has deployed AI agents in real-world business environments.

My client wants to sell chatbots + voice agents to clinics/doctors under one single brand (theirs) — not white-labeled per clinic. It's B2B, multiple paying customers, not a one-off bot for one business. I've only built single-client bots before using no-code tools (n8n). I don't know if that approach even holds up once you're selling to many clients at once, or if this needs to be built in actual code from the start. I don't have a plan yet — genuinely asking. If you've built something like this before: - Did you use no-code, custom code, or a mix? - What broke or didn't scale once you had more than one client? - What would you tell someone starting from scratch? Would really appreciate hearing how people actually did this in practice.

Is this normal, or am I just bad at this? 😅 Building my first production-level AI agent — a WhatsApp booking bot for a dermat clinic (n8n + AI Agent + Postgres/Redis + EkaCare integration). Booking, reschedule, cancel, the whole flow. The last couple weeks have been a real lesson in how many ways things can quietly break: - Agent confidently told a patient "you're booked!" — except the booking had actually failed, it just never found out - Session memory that worked fine... until a stray character in the key made it forget everyone was ever there - A field I *knew* was an array turned out to be a stringified array three nodes downstream, and everything silently broke - A workflow crash mid-booking left the system in this weird limbo state where the agent kept insisting it was "still checking" — forever None of these showed up until real (test) conversations hit them. Everything looked fine on paper. So — genuine question for anyone who's shipped agentic workflows to production: is this just what building these systems looks like? And if you've been through this — what's actually helped you catch this stuff earlier? How do you test for the weird edge cases (crashes mid-flow, race conditions, data type mismatches) before a real user finds them for you? Would love to hear how others approach testing here — feels like there's no "just write unit tests" playbook for this yet, or maybe I just haven't found it. 🙂

Hey everyone, running into a wall and could really use input from anyone who's dealt with this. I'm setting up WhatsApp Business API for a clinic client. Business verification has now failed twice — first attempt had a phone number cache issue (now resolved), but the identity confirmation step keeps rejecting the ID even after fixing lighting, framing, corners, dark background, and file size exactly per Meta's spec. I'm currently submitting a photographed printout of an e-Aadhaar PDF Has anyone here run into this specifically? - Did switching to a different ID type (Passport/PAN/DL) fix it? - Does it have to be the original physical card, not a printed e-Aadhaar? - Any luck getting Meta support to actually clarify the rejection reason beyond the generic message? This is time-sensitive — client onboarding is blocked until this clears, and I don't want to burn another attempt guessing. Really appreciate any first-hand experience here. 🙏

I’m building a WhatsApp chatbot in n8n for a clinic. The bot needs FAQ handling, appointment booking, rescheduling/cancellation, reminders, and personalization I’m deciding between using a Switch node based workflow or the AI Agent node. My goal is fast response time and a clean production design, but I also want the bot to feel personalized. For those who have built something similar: - Is the Switch/Rules-based approach better for speed and reliability? - Does the AI Agent node make personalization easier enough to justify the extra latency? - For clinic use cases, would you keep booking/rescheduling outside the agent and use it only for FAQ or fallback? - Any best practices for storing personalization data and routing intents in n8n? I’d like advice from people who have done this in production