A chat assistant answers. An agent acts: it chooses which permitted tools to use (look up an order, read a policy, draft an email, update a record) and works through several steps toward a defined goal.
That flexibility helps with tasks too varied for fixed rules, and it is also why agents need firm limits. Actions involving money, customer commitments, sensitive data or irreversible changes go to a person for approval.
What we deliver
- Task definition and tool design
- Agent implementation with tool calling
- Permission boundaries for each tool
- Human approval checkpoints
- Step, time and cost limits
- Execution traces and logging
- An evaluation set of representative tasks
- Failure handling and escalation
Technology & implementation
Technically, an agent is a language model working in a loop with tool calling. It receives the task and a list of tools, each described by its inputs and outputs, chooses one, reads the result and decides the next step, until it finishes or reaches a limit. Tools are ordinary code with their own permission checks, so the model can only do what a tool allows, whatever it is asked to do.
We keep agents narrow: one job, a short list of tools, read access by default and write access only where it is needed. Each run has a step and cost limit, a full trace of what was called and why, and approval checkpoints before consequential actions. Where the steps are predictable, a fixed workflow with a single AI step is the better design.
Models are accessed through provider APIs, for example Anthropic’s or OpenAI’s, chosen per project for quality on your tasks, cost, data handling terms and hosting region. Before launch the agent is tested against a set of representative tasks, including ones it should refuse or hand over.
Example use
An illustrative scenario, not a description of a completed project.
An operations assistant receives a customer request, retrieves relevant order information, checks the applicable company policy and prepares a recommended action for an employee to approve.