What counts as agentic (and what doesn't)
An agentic example is one where the AI takes actions toward a goal, not just where it produces text. Drafting a summary is generative. Reading the data, deciding what to do, doing it, and checking the result is agentic. The examples below are all the second kind.
Example: reconciliation across tools
A finance agent pulls transactions from Stripe, the bank, and the accounting tool, matches them across sources, flags the anomalies, and drafts the entries, every night before anyone is in.
- Reads from several systems that do not agree with each other.
- Fuzzy-matches across sources and surfaces what is off.
- Produces a ready-to-review deliverable on its own.
Example: operating tools with no API
A huge part of real work happens on portals, dashboards and legacy software that have no usable API. An agentic agent logs in, navigates, fills forms and files documents like a human operator would, using a real browser. This is the reach that scripted, API-only automation simply does not have.
Example: ambient monitoring
An agent watches an inbox, a calendar or an external source and acts the moment something happens: a renewal is due, an invoice arrives, a threshold is crossed. The client never had to ask. Only an autonomous agent can do this, an ephemeral one only exists while a task runs.
Example: long-running workers
The agents people actually run for this are long-running runtimes like OpenClaw and Hermes: persistent workers that stay on, keep their context, and operate continuously. Their open ecosystem and community mean thousands of skills you can reuse instead of building from scratch.
Short-running: sandboxes and workflows (E2B, BrowserUse, Modal)
Stateless. Re-hydrates state, re-auths and reconnects every time. Great for code execution, scraping and batch tasks.
Long-running: persistent agents (OpenClaw, Hermes)
Persistent. An agent that lives, remembers and takes initiative. The only catch is idle cost, which per-agent pricing or your own always-on infrastructure removes.
What these examples have in common
Every one of them needs a runtime that stays alive, holds state, integrates with your tools, and recovers when it breaks, around the clock. That is the hard part, and it is what Molted provides: a managed runtime for autonomous agents (OpenClaw today) with 1,500+ integrations and self-healing.
One agent
Easy to babysit.
A fleet, by hand