Prepare the action; a person executes it
The agent gathers evidence, drafts a response or plan and hands it to the user for review.
AI AGENTS / GOVERNED AUTOMATION
AI agents for business processes that act only within an approved scope.
A useful agent is more than a chatbot. It understands the goal, works with approved tools, prepares actions and follows explicit rules for when a person must confirm the next step.
01 / PRINCIPLE
Autonomy should be graduated.
Deploy agents from suggestion mode through approved actions to limited automation. Each level needs its own permissions, limits and tests.
An action waits for confirmation
The agent prepares a system change, message or order. Execution happens only after explicit approval.
Automate low-risk routine work
Repeatable steps with bounded impact can run automatically inside defined limits and monitoring.
02 / IMPLEMENTATION
Where agents make sense
Typical scenarios include processing incoming requests, preparing proposal inputs, synchronising tasks, checking documentation completeness, internal reporting or using company systems through APIs. GUI agents are a fallback where a stable API is unavailable.
- 01Tool registry
List of tools, inputs, outputs and owners.
- 02Policy engine
Roles, limits, prohibited actions and approval rules.
- 03Evaluation scenarios
Successful, edge and adversarial cases before production.
- 04Incident and rollback
Ability to stop the agent, reverse an action where possible and reconstruct the full execution path.
03 / DECISIONS
Questions to resolve before deployment.
Scope, architecture and automation level depend on data sensitivity, the workflow and accountability for the outcome.
Does an agent need access to every system?+
No. Each agent gets only the tools and data required for its role. Permissions should be separated just as they are for human users.
Is an API better than controlling a screen?+
Usually yes. APIs are more stable, faster and more auditable. GUI automation is a governed fallback for legacy software without an interface.
How do you measure agent success?+
Track task completion, correctness, human interventions, latency, cost, failure states and audit quality.
Start in suggestion or approval mode.
We prove task success and policy compliance before increasing autonomy.