Start with one operational decision. Keep predictable steps in code and use AI where the input needs interpretation.
Start with the decision, not the interface
A chat window does not tell you whether a system needs an agent. Start by mapping the decisions inside the workflow. If the inputs are structured and the next action is predictable, ordinary automation is often the clearest implementation. An agent becomes useful when the task involves interpreting unstructured information and selecting among a bounded set of tools.
Consider a customer asking where an order is. Looking up an order number and returning its status can be a conventional integration. Interpreting an unusual delivery issue across messages and policies may justify an AI step. Authorizing a refund is a separate action with its own permissions. Combining these into one unrestricted assistant makes the scope harder to evaluate.
Write a pilot brief that can be tested
Describe the trigger, input sources, permitted actions, expected output and escalation owner. Choose one workflow with a person who can judge whether a result is correct. Collect representative examples, including incomplete requests and unavailable systems, before choosing the architecture.
An illustrative pilot could prepare support replies without sending them. Staff compare drafts against the current process and record corrections. Useful measures include review time, unsupported statements, escalation accuracy and cost per accepted result. These are proposed evaluation measures, not claims about a completed Koddox project.
Separate model judgment from business authority
Treat retrieved documents and customer messages as information, not instructions that can redefine the agent's permissions. Validate tool inputs outside the model. Limit accessible records to the requesting user's scope and separate reading data from changing it.
OpenAI's agent safety guidance discusses prompt injection, structured outputs and tool approvals. These controls are layers rather than a guarantee. A practical implementation still needs application authorization, review of consequential actions and tests that attempt to cross the agreed boundaries.
Plan for failure and operating cost
A useful agent must explain what happens when evidence is missing, an API times out or a task is interrupted. Decide whether to retry, ask for clarification or send the case to a person. Keep enough operational evidence to investigate problems without retaining unnecessary sensitive content.
Estimate cost using expected task volume, model usage, retrieval, external APIs and human review. A cheap model call does not establish a cheap workflow if staff repeatedly repair the output. Start with a baseline, release to a limited audience and compare the complete operating effort.
What to request from your development partner
Ask for a workflow diagram, tool inventory, evaluation examples, deployment responsibilities and an explicit list of excluded actions. For teams in North America and Europe, agree who handles escalations outside the shared working window.
Koddox can scope an agent engagement around your existing applications and acceptance criteria. Bring anonymized examples, integration documentation and a named process owner to discovery. The first deliverable should make the implementation decision clearer, including when an agent is unnecessary.