AI
AI Workflow Agents
Agents with permissions, boundaries and an audit trail
Agents embedded inside defined workflows: they read, analyse, prepare, route, follow up and execute a narrow set of permitted actions — under supervision.
The starting condition
What this usually looks like before
- General-purpose assistants have no access to the operating record and no accountability.
- Automation projects stall because nobody will let software act without oversight.
- The work worth automating is repetitive coordination, not conversation.
Capabilities
What we build
Scoped action sets
Each agent has an explicit, enumerated list of actions it may take. Anything else is refused, not improvised.
Human approval gates
Consequential actions — money, contracts, external commitments, data deletion — require a person.
Full observability
Every run is recorded: input, reasoning summary, action taken, outcome, and who approved it.
Deterministic where it matters
Calculations, rules and eligibility remain deterministic code. Language models handle language, not arithmetic or policy.
Graceful escalation
Low confidence routes to a human with the work already prepared, rather than guessing.
Outcomes
What you should expect to change
- Repetitive coordination work removed from skilled people
- Automation that risk and finance teams will actually approve
- Measurable agent performance and intervention rates
- A safe path to widening autonomy over time
Where AI fits
Agents are the product here — but the discipline is the point. Permissions, audit trails, deterministic boundaries and human approval are designed in from the first workflow.
Typical integrations
- Messaging
- ERP
- Accounting
- Storage
- Internal APIs
Common in
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ExploreAI Workflow Agents
Describe the version of this problem you have.
We will tell you what a system for it looks like, what it would take to build, and whether it is worth doing at all.