🎮 The Next Input — Issue #160

In partnership with

⚡ The Briefing — 60 sec

• Why the chatbot is not your AI strategy This is the right take without a doubt. If your “AI strategy” starts and ends with a chatbot, you don’t have a strategy — you have a demo.

• Canva buys two AI companies in major push 👀 Canva isn’t playing small here. This is how platforms move — quietly stacking capability until the product shifts under your feet.

• Anthropic Managed Agents Overview Anthropic is not playing. Steady cooking with gas. 🔥🔥🔥 This is the shift from “chat” to actual agent infrastructure.

🛠️ The Playbook — The Post-Chatbot Engine

Mission
Move beyond chatbot thinking and build AI systems that actually do work, not just talk about it.

Difficulty
Intermediate

Build time
3–5 hours

ROI
Real automation, real leverage, and a system that produces outcomes instead of conversations.

0) Why This Matters

The chatbot era is already getting old.

Not because chat is useless — but because it’s incomplete.

A chatbot:

• answers questions

• generates text

• helps you think

But it doesn’t:

• complete workflows

• take action across systems

• own outcomes

Meanwhile:

• Canva is stacking AI capabilities into its platform

• Anthropic is shipping managed agents

• the “chatbot = AI strategy” take is getting called out publicly

So the move is:

• stop thinking in prompts

• start thinking in workflows

• move from answers → actions

1) Architecture

2) Workflow

1. Identify a workflow currently handled manually (e.g. reporting, outreach, analysis).

2. Break it into steps that can be automated or assisted.

3. Replace chat-based interaction with a structured agent flow.

4. Connect tools and systems so the agent can take action.

5. Add approval steps for sensitive actions.

6. Deliver outputs directly into the system where work happens.

3) Example Prompts

Workflow Builder Prompt

You are converting a chatbot interaction into a full workflow.

For the task below:
- break it into steps
- identify which steps require AI
- identify which steps require tools or integrations
- identify where human approval is needed

Task:
[insert task]

Agent Instruction Prompt

You are an AI agent executing a workflow.

Your job is to:
- complete the task step-by-step
- use available tools when needed
- avoid unnecessary steps
- flag uncertainty

Task:
[insert task]

Action Validation Prompt

Before executing an action:
- confirm the action is correct
- identify risks
- determine if approval is required

Return:
approve / review / reject

Output Delivery Prompt

You are preparing the final output.

Ensure:
- clarity
- completeness
- usability
- direct integration into workflow

Return a ready-to-use result.

4) Guardrails

• Don’t stop at chat — build execution.

• Keep humans in the loop for critical steps.

• Ensure outputs land where work happens.

• Avoid over-complicating early workflows.

• Validate actions before execution.

• Build reliability before scale.

5) Pilot Rollout — 3 hours

1. Pick one workflow currently handled via chat or manually.

2. Map the steps and required systems.

3. Build a simple agent flow for that workflow.

4. Connect one or two key tools.

5. Add a human approval step.

6. Test with real tasks and refine.

6) Metrics

• Tasks completed end-to-end

• Time saved per workflow

• Reduction in manual steps

• Output usability

• Error rate

• Human intervention rate

• Workflow adoption

Pro Tip: If your AI still needs you to copy-paste its output into another system, you’re not done yet.

🎯 The Arsenal — Tools & Platforms

• Claude Managed Agents · real agent infrastructure beyond chat · https://platform.claude.com/docs/en/managed-agents/overview

• LangGraph · orchestration for multi-step workflows · https://www.langchain.com/langgraph

• Pinecone · retrieval for context-aware decisions · https://www.pinecone.io

• Zapier / Make · connect workflows across tools · https://www.make.com

• Canva AI stack · example of platform-level AI integration · https://www.canva.com

Copy-paste prompt block:

You are helping me build a Post-Chatbot AI Engine.

For the workflow below:
1. break it into steps
2. identify which steps AI handles
3. identify which steps require tools
4. identify where human approval is needed
5. design a simple agent workflow
6. propose a pilot rollout
7. define success metrics

Workflow:
[insert workflow here]

Return the answer in markdown with sections for:
- Workflow summary
- AI steps
- Tool integrations
- Human approval points
- Agent design
- Pilot rollout
- Metrics

💡 Free Office Hours

If you’re still stuck in chatbot mode and want to move into real AI workflows, I run free office hours to help you design systems that actually execute.

Book here: https://calendly.com

88% resolved. 22% stayed loyal. What went wrong?

That's the AI paradox hiding in your CX stack. Tickets close. Customers leave. And most teams don't see it coming because they're measuring the wrong things.

Efficiency metrics look great on paper. Handle time down. Containment rate up. But customer loyalty? That's a different story — and it's one your current dashboards probably aren't telling you.

Gladly's 2026 Customer Expectations Report surveyed thousands of real consumers to find out exactly where AI-powered service breaks trust, and what separates the platforms that drive retention from the ones that quietly erode it.

If you're architecting the CX stack, this is the data you need to build it right. Not just fast. Not just cheap. Built to last.

See the data

🕹️ Game Over

Chatbots talk. Systems execute. Choose wisely.

— Aaron Automating the boring. Amplifying the brilliant.

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