Review and route
Classify an incoming request, gather the relevant record, and route it with a reason staff can inspect.
Custom AI agents
AI agents built for real work.
An AI agent is software that can interpret a request, choose from approved tools, and take a defined action. We design that behavior around the work, permissions, and review standards of the business using it.
Good use
01Give the agent a bounded job.
Agents are useful when work requires context, several system steps, and a judgment that can be checked. They are less useful when a fixed rule or ordinary application can solve the problem more clearly.
Prepare an action
Draft a response, report, proposal, or system update for human approval before anything consequential happens.
Coordinate tools
Move a task across approved applications while keeping each action inside a defined permission boundary.
Architecture
02One agent when one is enough.
A single agent is often easier to understand, evaluate, and operate. This use of models, tools, and actions is sometimes called agentic AI. Multi-agent systems make sense only when separate responsibilities, tools, or review steps create a real operational benefit. Agent orchestration is the logic that assigns those responsibilities and controls how work moves between them.
Control
03Designed for review, recovery, and evidence.
Approved actions, permission-aware access, structured outputs, audit trails, and staff override are part of the system design. Evaluations test whether the agent follows instructions and handles difficult cases. Guardrails restrict unsafe or unsupported actions, while error handling defines what happens when a tool, model, or source is unavailable.
Human approval
High-impact actions wait for the right person to review and approve them.
Evaluation
Representative cases test instruction following, output quality, tool choice, and refusal behavior.
Operations
Cost, latency, failures, and staff interventions remain visible so the system can be refined.
Common questions
Before you scope the work.
What is a custom AI agent?
Software that interprets a request, chooses from a set of approved tools, and takes or prepares a defined action. The useful part is the boundary: what it may read, what it may change, and which decisions wait for a person.
When is an agent the wrong choice?
When a fixed rule, a form, or an ordinary application solves the problem more clearly. Agents earn their complexity when work needs context, several system steps, and a judgment that can be checked.
How do you keep an agent from taking the wrong action?
Approved tools and actions, permission-aware access, human approval on consequential steps, structured outputs, evaluations against representative cases, and audit trails that record what happened.
Do you build multi-agent systems?
Only when separate responsibilities, tools, or review steps create a real operational benefit. A single agent is usually easier to understand, evaluate, and operate.