An agent is part of a process
A useful agent knows when to start, what information it may access, what result is expected and when a human must intervene. It may prepare a weekly performance review, qualify incoming requests, check contract completeness or monitor overdue actions. Its output enters an existing workflow rather than remaining in a chat window.
Context creates reliability
Generic models know language; they do not automatically know your definitions, priorities, customers or approval rules. A production agent needs curated business context and access to trusted sources. It should cite evidence, flag uncertainty and avoid inventing missing facts.
Control is designed, not assumed
Different tasks require different autonomy. An agent may analyze freely, draft for approval or execute within strict thresholds. Logs, permissions, exception handling and human review protect the company. The goal is not maximum autonomy; it is the right autonomy for the risk and value of the work.
Agents should improve with the process
Performance must be measured: accuracy, time saved, exceptions, adoption and business impact. Feedback from users and outcomes should update instructions, context and workflow rules. An agent without an improvement loop becomes another neglected tool.
Practical next steps
- Define one clear role and measurable output for each agent.
- Connect agents to trusted business context and explicit permissions.
- Review exceptions and outcomes regularly to improve both agent and process.