Agents & AI applications
Give AI the context.
Define what it can do.
Build an assistant inside a useful interface, or investigate an agent that can select permitted tools. Start with the task and the authority the software should have.
Design pattern / Proposed project scope
The system
around the capability.
Discovery turns this pattern into a specification for your systems, users and operating constraints. It is not a claim of a completed deployment.
- Context
Permitted records and instructions
- Interpret
Understand the task and available information
- Act or review
Use a permitted tool or request approval
- Evaluate
Check completion, errors and cost
Illustrative commission
A record-review assistant, with a route to tool use.
An internal user needs a summary across selected records and a proposed next action. The first scope can prepare a sourced draft; tool execution is a separate decision with explicit permissions.
- Application
- An agreed web, mobile or embedded interface, with source context and review states.
- Tool boundary
- Allowed tools, input checks, approval requirements and limits on time, steps and cost.
- Evaluation
- Representative tasks, unsupported requests, tool failures and unauthorised actions.
- Handover
- Agreed code, configuration, evaluation results, known limitations and operating notes.
Example scope to assess and tailor. Deliverables, fees, dependencies and acceptance criteria are agreed before implementation.
What supports this work
Experience to inspect.
New work to evaluate.
IMFIT supports AI-assisted conversations and surrounding application controls. It does not establish a production autonomous agent. Agent execution begins with feasibility and evaluation before a deployment commitment.
Inspect the IMFIT case studySee how we scope and deliverScoped AI engineering
Have a task in mind?
Let’s assess the fit.
Start with the work, the information it needs and the question you want to answer.