Start with a task, not a model
A production AI agent is part of a business workflow. Before selecting a model or orchestration tool, describe the task, the person responsible for the result and the point at which the system should stop. A support-response assistant, for example, may draft a reply using approved knowledge while leaving sending to a person.
Write an acceptance statement a business owner can review: which inputs are supported, which sources may be used and which outputs are useful. If the task can be completed reliably by a deterministic rule or ordinary integration, that may be a simpler starting point than an agent.
Separate reading from actions
List every system the agent needs to access. Distinguish reading a customer record from changing one, and preparing a recommendation from issuing a transaction. Give each tool a narrow purpose and agree which operations need human approval.
Consider what happens when a document contains misleading instructions, a user asks for information they cannot access or a tool returns an unexpected response. Permissions must be enforced by the application and connected systems; a natural-language instruction alone is not an access control.
Build an evaluation set before launch
Collect representative examples, including incomplete requests, ambiguous wording and cases the system should refuse or escalate. Define what the reviewer will check: correctness, supporting evidence, permitted tool use, response time and the cost of running the task.
Keep evaluation examples separate from the examples used to shape the implementation where possible. Re-run the relevant checks when prompts, models, tools or source content change. A production decision should use evidence from the intended workflow rather than a single successful demonstration.
Design for failure and human review
External tools can time out, source information can be missing and a model can produce an unhelpful result. Set limits on tool calls and running time, record the state needed to understand a failure and provide a useful path back to a person.
For operations that create or change records, consider retries and duplicate actions explicitly. Approval steps should show the proposed action and the information supporting it. A reviewer needs enough context to decide, rather than a button that approves an unexplained result.
Plan operating ownership and cost
Identify who owns the knowledge sources, model accounts, deployment, incident response and review queue. Estimate the expected workload and examine how input size, retrieval, repeated tool calls and evaluation affect cost. A fixed demonstration cost does not establish the cost of a live service.
For a US, Gulf or Indian deployment, discuss where the data is processed, access expectations and the customer review process. Applicable requirements must be assessed for the specific organization and project. The same technical blueprint should not be assumed suitable for every jurisdiction or type of data.
A discovery checklist to bring to your team
Prepare one workflow description, a set of representative examples, the systems involved and a list of actions that need approval. Include the people who own those systems and who will review the result. This is enough to begin a focused discovery conversation without pretending every implementation choice is already known.
Accenous can discuss use-case discovery, retrieval, tool integration, application interfaces, evaluation and operations as a connected delivery scope. Our AI Division page describes the capabilities; the project agreement defines the actual deliverables.
Questions before you begin
Is an AI agent the same as a chatbot?
A chatbot describes an interaction format. An agent may also use tools or perform workflow steps. What matters for planning is its permitted task, data access, actions and review process.
Can a prototype go directly into production?
A prototype can inform discovery, but production still needs relevant evaluation, permissions, failure handling, deployment and operational ownership.
What should we send to request an AI project discussion?
Send the workflow, example inputs, connected systems, approval requirements and intended users. Avoid sending sensitive production data through an initial inquiry form.
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