What Is Inside a Custom AI Agent
An agent is more than a prompt. Here is how the task is planned, how it reaches your systems safely, and how approval and logging keep it accountable once it is live.
From a goal to a completed, logged task
The agent breaks the goal into steps itself
Given a goal, the agent works out the sequence of checks and actions needed, adjusting the plan as it learns what a step returned. This is different from a fixed script, because the agent can handle the variation that real business cases always contain.
- Goals broken into an ordered sequence of steps.
- Plan adjusted as each step's result comes back.
- Built and tested for the specific task, not generic.
Real access to your real systems
The agent calls your ERP, CRM, email or internal tools through defined connections, each scoped to exactly what the task needs. It does not get broader access than the job requires, and each connection is tested against your live data structure before the agent runs unattended.
- Connections scoped to the task, not open-ended.
- Built against your actual data structure.
- Tested before the agent is trusted with live data.
Every step accountable, every risk gated
Steps are classified during design as automatic or requiring sign-off, and that classification is enforced in the running agent, not left to chance. A complete log of what the agent did, when, and why is kept for every run, so behaviour can be reviewed at any time.
- Risk classification enforced at run time.
- Sign-off required before any flagged step proceeds.
- Full run log kept for every task the agent performs.
Everything the agent build includes
Task specification
A written definition of the goal, the steps and the approval points, agreed before building starts.
System integrations
Scoped connections built into the specific systems the task needs to read from and act on.
Approval workflow
A review step routed to the right person before any flagged action is carried out.
Run history
Every run of the agent recorded with its steps, inputs and outcomes, searchable later.
Testing against real cases
The agent proven on historical cases with known outcomes before it touches live data.
Model-agnostic build
Built so the underlying model can change without rebuilding the task logic around it.
See What an Agent for Your Task Would Look Like
Book a free consultation. Bring the task, and we will map the steps, the systems and the approval points it would need.
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