AI Solutions
Agentic Workflows
A defined sequence of actions an LLM can carry out on its own — within limits you set explicitly, not a black box you hope behaves.
LLM Agents · Orchestration · Bounded Autonomy
What it is
An agentic workflow gives a model a scoped set of tools and a defined goal, then lets it decide the sequence of steps rather than following a fixed script. That's useful exactly where the steps vary by case but the goal doesn't — and dangerous exactly where the boundaries aren't stated up front, which is why every boundary is explicit before anything ships.
How it works
- 01Define the goal and the tools
What the agent is actually trying to accomplish, and the specific, scoped set of actions it's allowed to take to get there.
- 02Set the hard boundaries
What it can never do unsupervised — an irreversible action, a spend limit, a customer-facing send — stated explicitly, not left implicit.
- 03Build and test against edge cases
Adversarial and edge-case testing before production, not just the happy path a demo is built to survive.
- 04Ship with a kill switch and logs
Every action taken is logged and attributable, and the workflow can be paused or reverted without a deploy.
Benefits
- Multi-step work handled end-to-end instead of requiring a person at every step
- Explicit, auditable boundaries on what the agent can and can't do unsupervised
- A kill switch and action log, not a system you have to trust blindly
Frequently asked
What stops the agent from doing something irreversible?
Hard boundaries defined at build time — certain actions simply aren't in its available tool set, and anything with real consequence routes through human approval.
Can we see what it decided and why?
Yes — every action is logged with the reasoning that led to it, so a wrong outcome is traceable back to a specific decision, not a mystery.
Also under AI Solutions
Not sure this is the right fit yet?
A scope call is a lower-commitment way to find out before anything gets built.