The evolution of AI interaction
Phase 1 — Chat: You ask a question, the model answers. Useful for research and drafting but requires you to do the work of executing on the answers.
Phase 2 — Tools: The model can call external functions — search the web, query a database, send a message. You still direct each step, but the model handles execution.
Phase 3 — Agents: You describe a goal, and the AI plans and executes the steps autonomously. "Process this week's invoices" becomes a complete workflow: download attachments, extract data, reconcile against orders, flag exceptions, update the accounting system.
Agentic AI in practice
Platforms like OpenClaw represent the agentic approach — persistent AI agents that run in the background, monitor events, and take action when needed. The Model Context Protocol (MCP) provides the standardised tool access that makes this practical.
For SMBs, agentic AI means automating entire workflows rather than individual tasks. Instead of connecting three separate automations (receive email → extract data → update CRM), a single agent handles the complete process and adapts when it encounters exceptions.
When agentic AI makes sense
Agentic AI works best for workflows with multiple steps, variable inputs, and decision points. Simple, predictable tasks are better served by traditional automation. The complexity of agent systems — and the guardrails they require — means they should be reserved for workflows where the flexibility genuinely pays off.