As an AI automation agency we build the unglamorous part: agents that run every day against real customer conversations, with guardrails, memory, escalation paths and someone monitoring them. The demo is easy. Keeping an AI agent useful for six months — when the model drifts, the API changes and a customer asks something nobody anticipated — is the actual work.
Most AI automation fails in the same two ways. Either it never leaves the demo, because nobody solved what happens when the model is wrong; or it ships and quietly damages the business, because it confidently invented pricing, promised availability that does not exist, or kept messaging a lead a human was already talking to. Both failures come from treating the model as the product rather than one component in a system.
Agents that qualify inbound leads over SMS or chat, ask one question at a time, and hand off to a human the moment intent is high or the conversation goes sideways.
System prompts that explicitly forbid inventing pricing, guarantees or availability, plus automatic handoff when a lead asks for a person or sounds frustrated.
Per-contact memory so each lead keeps their own thread and the agent does not reintroduce itself on message four.
The moment a real team member replies, every automated sequence for that contact stops. Without this, AI follow-up talks over your salespeople.
We read the transcripts. Agents get worse at the edges over time and the only way to catch it is to look at what they actually said.
What the agent is allowed to say, what it must never claim, and exactly when it should stop and fetch a human.
We build the handoff path before the happy path, so the failure mode is a human conversation rather than a confident wrong answer.
We run the agent against your actual past conversations, including the awkward ones, before it touches a live lead.
The agent starts on one channel or one lead source. Scope widens once the transcripts justify it.
Working versions of the patterns above, ready to import into your own n8n instance.
ChatGPT answers a question you type. An AI automation agent runs unattended: it receives a webhook, loads the right conversation history, decides what to do, calls your CRM, and knows when to stop and escalate. The model is maybe a fifth of that system. The rest is routing, memory, guardrails, error handling and monitoring — and that is the part that determines whether it still works in three months.
Three layers. The system prompt explicitly forbids inventing pricing, availability, guarantees and business hours, and instructs the agent to escalate anything it does not know. The output is structured, so the workflow can act on a handoff flag rather than trusting free text. And a human-takeover rule stops all automation the instant a real person replies. No layer is sufficient alone.
Mostly OpenAI models through n8n, chosen per workflow — a short SMS reply does not need the same model as a multi-step reasoning task, and paying for the larger one everywhere is just waste. Because the orchestration lives in n8n rather than in a vendor's closed platform, swapping the underlying model later is a configuration change, not a rebuild.
We map your biggest manual bottleneck and show you what we would automate. No pitch — and if automation is not the answer, we will say so.
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