Automation that can handle context.

AI agent or conventional automation?

A fixed automation follows a prewritten path. It is fast, predictable and ideal for stable structured inputs. An agent can decide among permitted next steps based on context, making it useful when the input is an email, document, conversation or unusual combination of facts.

These approaches complement each other. An agent might understand a request and prepare structured data; conventional code then validates required fields and writes to the system of record. Keeping hard rules outside the model improves reliability and makes the workflow easier to audit.

What can be connected

An AI automation can sit across the tools a business already uses, provided those tools offer a safe integration path. We prefer supported APIs and scoped service accounts over fragile screen automation whenever possible.

• Shared inboxes and service queues
• CRM, ticketing and work-management systems
• Approved document libraries and internal knowledge
• Forms, spreadsheets and structured databases
• Notifications, approval steps and reporting channels

Design for change

Business processes and AI models both change. A maintainable automation keeps prompts, policies, integrations and evaluation examples separate enough to update safely. It also has observable checkpoints so a failure can be located without reading an entire conversation trace.

Before launch, the workflow should be tested with normal cases, edge cases and intentionally bad input. After launch, changes to knowledge, model behaviour or connected systems need the same disciplined review as any other production software.