AI Agents

An AI agent is a software system that selects actions to achieve a goal, observes the results, and decides what to do next. In foundation model applications, the model commonly interprets the request, proposes intermediate steps, and selects tools. An orchestration layer manages model calls, tool execution, state, and stopping conditions. The model does not inherently have access to business systems: the application supplies interfaces and enforces permissions.

A typical interaction follows a bounded action-and-observation cycle. A user asks a support agent to investigate a delayed order. The agent requests missing order details, retrieves shipment information through an approved API, checks the applicable policy, and offers an authorized resolution. If a refund exceeds a configured threshold, the workflow requests human approval instead of allowing the model to decide whether approval is necessary. The agent then reports the actual outcome rather than merely generating a plausible completion message.

Cloud implementations usually combine a hosted model, an agent runtime or orchestration component, API-backed tools, and storage for task state. Tools might query inventory, create support tickets, schedule appointments, or retrieve documents. Tool definitions describe supported operations and their inputs. Execution code must independently validate arguments, authenticate the caller, and check authorization. Retrieved documents provide evidence, but their contents should be treated as untrusted data rather than instructions that can override application policy.

Business applications include customer service agents that resolve account issues, IT operations agents that collect diagnostics and propose remediation, procurement agents that compare approved suppliers, and sales agents that update customer records after a conversation. Their value is greatest when work requires interpreting variable inputs and choosing among permitted actions. A fixed workflow is often preferable when the sequence is predictable, strict reproducibility is required, or errors have unacceptable consequences. Agents and deterministic workflows can also be combined, with flexible interpretation inside tightly controlled execution paths.

Agent autonomy creates trade-offs. Multiple model calls and tool requests increase latency and cost, and an incorrect early decision can affect later steps. Engineers should impose iteration limits, timeouts, spending limits, and clear escalation conditions. Retries need special care: repeating a read operation is usually less risky than repeating a payment or refund. Idempotency keys, transaction controls, and durable task state help prevent duplicate actions. Evaluate completed business outcomes, tool-selection accuracy, policy compliance, and behavior when dependencies fail, rather than judging only the final response.

Several misconceptions are important. A chatbot is not necessarily an agent; answering questions alone does not demonstrate goal-directed action. Retrieval-augmented generation can supply relevant information without providing an action loop. Agents do not necessarily learn from each interaction or require model fine-tuning, and they need not operate without supervision. Effective designs use least-privilege access, minimize sensitive data exposure, record execution events, and require approval for consequential actions. Safety instructions and output filters complement these controls but do not replace authorization or business-rule enforcement.