An AI agent is a software workflow in which a model interprets a goal, chooses among permitted actions, observes the result, and decides what to do next. The model is only one component. Reliable operation also depends on the tools it may call, the state it can read, the rules that stop or escalate work, and the evidence recorded after each action. An agent therefore should be designed as a bounded decision loop, not treated as an independent digital employee.
The operating loop behind an agent
The loop begins with a defined task and a source of truth. A support agent might read an approved knowledge base and a ticket record; a finance assistant might read a reconciled ledger view. The agent receives only the fields needed for that task. It then proposes a response or invokes a narrow tool whose input and output are structured enough to validate. The result returns to the loop as an observation, not as proof that the business outcome is correct.
Tools turn language into consequences. A search tool can reveal information, while a write tool can change a customer record or trigger another system. These capabilities need separate permissions. Start with read access and draft generation, then require a person or deterministic rule to approve consequential writes. This is one reason the tests to run before integrating AI should cover the task definition, result checking, and a safe stop path rather than only a polished demonstration.
State also needs boundaries. Conversation history can help an agent continue a case, but it can contain stale assumptions. Durable business facts should come from an owned system, with timestamps and identifiers that let a reviewer reconstruct the decision. For knowledge-heavy work, the distinction between chat context and governed company memory prevents a fluent answer from silently becoming policy.
Principles versus strict instructions: run the same test
Teams often debate whether an agent needs a short set of principles or a long procedure. The useful answer comes from an evaluation, not preference. Choose one representative task and freeze its input records, tool definitions, permissions, and expected outcomes. Prepare two prompt versions. The principles-led version states the goal, evidence hierarchy, boundaries, and escalation rule. The strict version specifies the sequence, required fields, decision branches, and prohibited actions.
Run both versions against the same set of ordinary cases and deliberate failures. Include missing data, conflicting sources, an unavailable tool, an ambiguous request, and a request outside authority. Record whether the agent selected the correct source, stayed within permission, asked for review when required, and produced evidence that another person could inspect. Do not turn a model score into a business claim. The acceptance decision belongs to the process owner and should reflect the cost of each failure type.
The comparison may show that strict instructions work well for stable routing while principles cope better with variable language. A combined design is often more defensible: deterministic code enforces required fields and permissions; principles guide interpretation; a human resolves exceptions. When evaluating outputs, use the checks in the guide to detecting invented agent metrics so that a clean dashboard does not conceal an unsupported success label.
Controls that define the real limit
An agent’s practical limit is not what the model can describe. It is what the system is authorized to read, propose, and change. Write these limits into tool scopes and workflow states. Store the input reference, selected tool, proposed action, approval identity, tool response, and final status. Define what happens when evidence is incomplete: retry with a safe source, send the case to an owner, or stop without changing anything.
Frequently Asked Questions
It is a controlled software loop in which a model interprets a goal, may call permitted tools, observes results, and continues or stops under defined rules.
Test both on the same inputs, tools, permissions, and failure cases. Keep deterministic controls for required fields and risky actions even when principles guide interpretation.
A reviewer owns ambiguous exceptions and consequential decisions. The agent can prepare evidence and a proposal, but authority should remain explicit and auditable.
Record the source references, proposed action, tool call, approval identity, returned result, exception path, and final status needed to reconstruct the decision.
Release in shadow mode before enabling live actions. Compare proposals with completed historical decisions, protect sensitive fields, and review disagreements rather than hiding them in an average. A rollback should disable the write path and preserve the evidence needed to repair affected records. If your team needs to turn one bounded process into an agent with explicit permissions, eval cases, and human gates, scope an AI agent development engagement with AI4SALE.