An assistant answers. An agent acts: it reads a request, plans the steps, calls systems, updates records and hands back the result. That is where the larger gains are, and where the risks are larger too, because a wrong action costs more than a wrong answer.
We design and build AI agents for real business processes, with scoped permissions, action limits, human approval and a full audit trail.
Each agent has a defined job, tools and limits.
Approval required for actions above agreed thresholds.
Inputs, reasoning, tool calls and results recorded.
Agentforce, Copilot Studio, Oracle AI agents and custom frameworks.
Agentic AI is mostly engineering discipline: what the agent may do, what it may not, how it is tested, and how a person stays in control.
Processes assessed for agent suitability: volume, rules, exceptions, systems involved and the cost of a mistake.
The best first agents handle repetitive work with clear outcomes.
Agents where they pay back safely.
Goals, tools, data access, decision boundaries, escalation paths and approval thresholds defined before build.
Narrow agents that do one job well beat general ones.
A job description for every agent.
Agents connected to ERP, CRM, ticketing, email and document systems through APIs with least-privilege service identities.
An agent can never do more than its role allows.
Actions inside your access model.
Several specialised agents coordinated across a process, such as intake, validation, update and notification.
Each hand-off is logged and testable.
End-to-end processes, step by step.
Scenario tests, adversarial tests, limits on actions and spend, and simulation before agents touch production.
Tested on what should not happen as well as what should.
Confidence before autonomy.
Dashboards of agent activity, approval queues, exception handling and regular review of agent decisions.
Autonomy expanded only as evidence accumulates.
Control that grows with trust.
How an agent goes live
Typical agent use cases and the controls around them.
| Process | Agent actions | Systems | Control |
|---|---|---|---|
| Invoice exceptions | Match, query supplier, propose correction | ERP, email | Approval above threshold |
| Service requests | Classify, gather data, resolve or route | ITSM, CRM | Auto for standard, approval for others |
| Sales operations | Update records, prepare quotes, follow up | CRM, ERP | Approval before sending |
| Order management | Check status, update customer, raise issues | ERP, TMS, email | Limited actions |
| Employee requests | Answer, update records, start workflows | HR, ITSM | Policy-based approvals |
Agents usually fail at the edges: the unusual invoice, the angry customer, the record that does not match. Giving an agent full autonomy on day one means finding those edges in production.
Our agents start in shadow mode, proposing actions people approve. Autonomy is extended step by step as the record shows it is safe, and every action stays logged and reviewable.
Discuss an agent use caseScope follows process selection.
An assistant answers questions or drafts content. An agent takes actions in systems, such as updating records, sending messages or starting workflows, and needs stronger controls.
High-volume processes with clear rules, defined systems and measurable outcomes, where most cases are routine and exceptions can be escalated.
With scoped permissions, action and spend limits, approval for higher-risk actions, testing on adverse scenarios, and shadow running before autonomy.
Yes. We build on vendor agent platforms where they fit, and on open frameworks where they do not.
A named process owner on your side, supported by logs that show exactly what the agent did and why.
Tell us which repetitive process costs your team the most time. We will tell you whether an agent fits.