Give the agent a job it can explain
An agent is software that can interpret a request, choose from a set of permitted steps, and use connected tools to prepare an outcome. It can still misunderstand a question, miss context, or return an answer that sounds certain but is wrong. For that reason, a useful agent has a narrow role, a trusted source of information, and rules for asking for help instead of guessing.
We start by separating decisions that need judgment from actions that have fixed rules. The agent may gather context, summarize options, or prepare a draft; your team defines what it can read, what it can change, and what must wait for approval. This keeps the system helpful without treating an AI response as a guarantee.
Three useful starting points
An agent should solve a repeated task with a clear owner and a result someone can verify. These are illustrative patterns, not claims about completed client projects.
- Team knowledge assistant: search approved operating guides, answer a teammate’s question with links to the relevant passages, and say when its sources do not support an answer.
- Support triage assistant: read a new case, identify the likely topic, collect relevant order or policy context, and prepare a reply for an agent to approve.
- Research brief assistant: gather facts from a defined set of sources, organize them around a question, and present the supporting links for a person to check.
What comes with an agent
We shape the agent around a written task definition and the material it is allowed to use. A project may include a conversational interface, retrieval from selected documents, connections to approved business tools, and a structured way to return its findings. We also define the cases where it should stop, ask for more information, or hand the work back to a person.
- A scoped agent behavior and the rules that limit its access and actions.
- A reviewed source collection or agreed path to your approved data.
- Representative test questions, including cases where the right answer is “I don’t know.”
- Approval steps, error handling, usage notes, and a handover for your team.
People set the boundaries and check the result
Your team provides the task examples, source material, and business decisions that the agent cannot infer safely. You also arrange access through approved accounts and tell us which information must not be sent to an AI provider. We use that input to agree on the agent’s role, its allowed tools, and the situations that require a person.
We then build and test the agent with you. You review answers against the source material and try questions that are vague, incomplete, or outside the role. Any action that could affect a customer, financial record, public content, or account setting can be held for human approval. We document what the agent can do, what it cannot do, and how to report a poor result.
What shapes cost and timing
The work depends on how narrow the agent’s job is, how trustworthy its source material is, and how many tools it must use. Connecting read-only documents is different from allowing the agent to update records. Multiple languages, changing policies, access controls, evaluation requirements, and the need for a review interface can add design and build work.
We agree on a contained first release after reviewing the task, example inputs, and the required safety boundaries. Provider access, subscriptions, and model usage may have separate costs. We explain the assumptions behind the estimate and revisit them if discovery reveals that the agent needs a different data source or approval path.
Questions about this service
How is an agent different from a workflow?
A workflow follows steps you define in advance. An agent can choose among permitted steps based on the request and the information it finds. Some projects use both: a workflow controls the process while an agent handles a language-heavy task.
Will the agent act without a person?
Only within boundaries we agree together. We can keep it read-only, let it prepare drafts, or require approval before an action changes a record or reaches a customer.
Can the agent use our documents?
Often, if the documents can be accessed in an agreed way and your organization permits their use. We review where information is stored, how it is retrieved, and which provider services receive it before choosing an approach.
Can you promise every answer will be correct?
No AI system can promise that. We test the agent against representative examples, show its sources where possible, and give it a safe way to pause when it lacks enough information.