Term: Agent / agentic AI
~7 min read
Estimated time: ~7 min read — for the in-app brief plus opening the primary source.
What this is
An agent is AI that can plan and take multi-step actions across tools (email, tickets, CRM) — not only draft text in a chat box.
Everyday example
Not just 'draft a reply,' but: read the inbox, open the CRM, update the opportunity, schedule a meeting, and email the customer — with or without asking you at each step.
An agent takes multi-step actions toward a goal — browse, call tools, update systems — not just draft in a box.
- Higher upside and higher blast radius than a copilot.
- Needs permissions, logs, spend limits, and a kill switch.
- “Agentic” on a slide often means a scripted workflow with an LLM in the loop — ask what it can actually touch.
- Customer-facing agents inherit your brand and your liability.
Next action: Do not approve an agent that can write to a system of record without a named owner and a rollback.
What changes in how you lead
How decision rights, process, and ownership should change.
- Treat agents as operators with credentials, not as chat windows.
- Risk, Legal, and the process owner sign the permission set together.
Compare related ideas
Agent / agentic AI vs Copilot
If a human must approve each external action, you are closer to copilot. If the system executes a chain of tools autonomously, treat it as an agent and govern it like automation, not like a spellchecker.
Open CopilotDeep dive
Opportunity: automate routine multi-step workflows.
Risk: wrong action at speed; over-permissioned access; hard-to-audit chains.
Start with reversible internal tasks; require step-up approval for money, external messages, or deletes.
Labs and vendors increasingly brand features as 'agents' — demand a clear action list and permission model, not a buzzword.