Agents with boundaries, tools and supervision.
Single and multi-agent systems with defined tools, permissions, escalation paths and evaluation.
- Custom systems engineeringfrom $15k
- Productized systems$5k–$40k+
- Arvanord Ops coveragemonthly
An agent is not a prompt with ambition. It is a system with tools, boundaries, permissions and a defined relationship to human judgement. We engineer agents that operate inside those definitions.
Typical agents handle research, document processing, operational coordination and preparation work — with approvals where decisions carry consequence.
- Agent architecture & tool design
- Multi-agent coordination where justified
- Permission and data boundaries
- Human-in-the-loop checkpoints
- Evaluation and monitoring hooks
How this is engineered.
Established patterns, chosen for reliability over novelty — adapted to the environment rather than invented for it.
Single agent, narrow scope
One well-instrumented agent with a small toolset and a clear job — often the highest-reliability choice.
Research → prepare → approve
Agents gather and prepare; humans decide and send. The default pattern for consequential workflows.
Agent chains
Specialised agents passing structured output down a pipeline, each evaluated independently.
Common questions.
Multi-agent or single agent?
Whichever survives evaluation. Multi-agent architectures are used only where coordination between specialised roles demonstrably improves results.
How are agents controlled?
Through tool boundaries, permission scopes, approval checkpoints and evaluation datasets that run against them continuously.
Often engineered together.
Systems are rarely one discipline. These capabilities most often share an architecture with ai agents.
Engineer ai agents into your operations.
Every system starts with the same first step: understanding the work.