The Tool Desk
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AI agents are software systems that use a model to pursue a goal across multiple steps. They interpret an objective, decide what to do next, use approved tools to gather information or change systems, inspect the results, and continue, correct course, hand off, or stop. Human-written instructions, permissions, guardrails, and approval points bound the behavior.
The word agent is used inconsistently. In this article, it means a system in which the model controls and adapts workflow execution—not merely an application that calls a language model once. Agents can fit work involving context, exceptions, or unstructured information, but they can also make consequential mistakes. Their usefulness depends on the task, the tools they can access, and how well the system is evaluated and supervised.
What is an AI agent?
OpenAI’s A Practical Guide to Building AI Agents describes agents as systems that independently accomplish tasks on a user’s behalf. Its business guide defines an AI agent as a system that can “plan, decide, and act independently to achieve a goal while operating within guardrails set by humans.” These are practical definitions rather than a universal industry standard; products may use the label more broadly.
An agent normally combines three essential parts:
- A model: usually a large language model that interprets the request, reasons about the next step, and selects among available actions.
- Tools: connections that retrieve data or perform actions in other software, such as search, databases, ticketing systems, calculators, or business APIs.
- Instructions and guardrails: rules that define the objective, permitted behavior, data boundaries, escalation conditions, and approval requirements.
The defining capability is workflow control. The model decides whether to answer immediately, obtain more context, call a tool, ask a person, retry, or finish. Simply adding an LLM to an application does not make that application an agent.
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How agents differ from chatbots and automations
| System type | How it operates | Typical limitation |
|---|---|---|
| Single-turn LLM or chatbot | Generates a response to a prompt, sometimes with retrieved context. | It generally does not control a multi-step workflow or take actions beyond the configured response. |
| Fixed-rule automation | Runs predefined triggers and steps, such as “when a form arrives, create a ticket.” | It follows the same path unless a developer changes the rules; unusual cases can break the flow or require a separate branch. |
| AI agent | Uses a model to choose and sequence steps, call tools, inspect outputs, and adapt within defined limits. | Its flexibility introduces uncertainty, permission risks, and the possibility of compounded errors. |
The boundaries are not absolute. A product may call a scripted workflow an “agent,” while an agent may contain deterministic steps. For a meaningful comparison, examine who controls the workflow, whether the system can choose among tools, and how it responds to intermediate results.
How an AI agent works, step by step
A useful mental model is a controlled loop. The exact implementation varies, but the following sequence explains most agent systems.
- Interpret the goal. The system receives the user’s objective plus relevant context, policies, identity information, and task-specific instructions. It may need to clarify an ambiguous request before acting.
- Choose the next step. The model determines whether it has enough information to respond, should retrieve data, should perform an action, should ask for approval, or should transfer the work to a person or specialist.
- Call a tool. A data tool can search documents, query a database, or read a record. An action tool can create, update, send, purchase, schedule, or otherwise change an external system. The tool interface should specify accepted inputs and return structured results.
- Inspect the result. The agent reads the tool output, checks whether it answers the current question, and notices errors, missing fields, or conflicting information. The result becomes new context for the next decision.
- Adapt or finish. The agent may call another tool, revise its plan, retry within limits, request human input, provide an answer, or end with a declared failure. A maximum step count and timeout prevent an endless loop.
- Enforce boundaries throughout. Instructions, input and output checks, identity and access controls, tool permissions, data filters, and approval gates constrain every proposed action—not just the final response.
Some systems keep this loop inside one focused agent. Others use handoffs: a triage agent routes a case to a billing, technical, or compliance specialist with different tools and instructions. OpenAI’s agent guidance recommends starting with one focused agent and splitting only when a specialist genuinely needs different capabilities, policies, or approval rules.
What tools give an agent its capabilities?
Information and retrieval tools
Retrieval tools let an agent obtain current or private context instead of relying only on what the model learned during training. Examples include a document search index, a customer-record query, an inventory database, a web search service, or a calculator. Read-only access is usually safer than write access, and returned data should be limited to what the task requires.
Action tools
Action tools connect the agent to systems where it can change state: opening or updating a support case, drafting an email, modifying a calendar entry, issuing a refund, or changing a configuration. Every action should have a narrowly scoped interface, explicit authorization, validation of arguments, and an audit record.
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Human handoffs and specialist agents
A handoff is a deliberate transfer when the current agent lacks the right expertise, reaches a risk threshold, or needs a person’s decision. The receiving party should get the relevant context, the reason for transfer, and any tool results so the task does not restart blindly.
Structured outputs and protocol connections
Structured output schemas constrain responses to fields that downstream software can validate. Protocol connections such as MCP servers can expose approved tools and resources through a consistent interface. These mechanisms improve integration and checking; they do not make the model infallible.
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Where AI agents may be useful
Agents are candidates for workflows in which the path to completion changes with context or intermediate findings. OpenAI’s practical guide highlights three signals:
- Complex contextual decisions: for example, deciding whether a refund fits a policy when order history, product condition, timing, and previous exceptions all matter.
- Rule sets that are difficult to maintain: an agent can interpret a policy and weigh several facts where a large collection of brittle if/then branches would be costly to update. The policy still needs explicit constraints and testing.
- Heavy use of unstructured data: a workflow may need to read contracts, correspondence, images, or free-form reports before selecting a next step.
Examples of potential applications
- Vendor security review: gather evidence from questionnaires and documents, identify missing controls, compare findings with a review policy, and route uncertain cases to a security professional.
- Insurance-claim processing: collect claim details, check required documents, compare facts with coverage rules, and escalate suspicious or ambiguous cases rather than approving automatically.
- Research and planning: break a broad question into searches, collect sources, reconcile findings, and present a plan for a person to review.
- Operations support: diagnose a service issue from logs and tickets, run permitted checks, and propose or perform a remediation after approval.
These are possible fits, not guarantees of accuracy, speed, lower cost, or productivity. A deterministic checklist or conventional automation may be the better choice when inputs, decisions, and outputs are stable and predictable.
When an agent is the wrong tool
Do not add an agent merely because a model is available. A fixed workflow is often preferable when:
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- The same inputs always produce the same sequence of steps.
- The decision can be expressed and maintained as clear rules.
- Errors would be unacceptable and there is no practical human approval point.
- The task has little unstructured context and no need to select among tools.
- A conventional integration already meets the reliability, latency, and audit requirements.
Agents introduce orchestration, monitoring, evaluation, access-control, and recovery work. Their flexibility is valuable only when it solves a real variation in the task.
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Guardrails should be designed around the impact of each possible action. A read-only research agent can have broader retrieval access than an agent allowed to send messages or alter financial records.
Practical controls
- Give each tool the minimum permissions and data scope it needs.
- Separate read operations from write operations and expose only the required fields.
- Require a human approval before irreversible, external, financial, or high-impact actions.
- Validate tool arguments and returned data with schemas, type checks, and policy checks.
- Set limits for steps, retries, spending, time, and the number of records changed.
- Log prompts, tool calls, results, approvals, handoffs, and final outcomes for audit and recovery.
- Provide a clear stop, cancel, and rollback path where the connected system supports one.
OpenAI’s safety guidance warns that guardrails reduce risk but do not guarantee perfect behavior. A system should fail closed when identity, authorization, or required information cannot be verified.
Security risks: prompt injection and data leakage
Prompt injection occurs when untrusted text—such as a web page, uploaded document, email, or database field—contains instructions intended to redirect the agent. The model may mistake those instructions for trusted policy and reveal data or call a tool improperly.
Reduce this risk by treating retrieved content as data, not authority; keeping system instructions and tool policies separate; limiting what untrusted text can influence; filtering sensitive fields; and requiring approval for consequential actions. Input guardrails can reject suspicious requests, while structured outputs make it harder for arbitrary text to flow directly into an action.
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Privacy leakage is another concern. A tool may expose more customer, employee, or confidential information than the task requires. Use identity-aware retrieval, field-level filtering, retention controls, and redaction. Test whether the agent can be induced to disclose information across users or tasks.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate an AI agent
Agent testing is more demanding than checking a single answer because actions can change state, adapt across turns, and compound mistakes. Anthropic’s January 9, 2026, engineering article defines an evaluation as a test that supplies an input and applies grading logic to measure success.
Build representative task cases
Include ordinary requests, ambiguous inputs, missing documents, conflicting records, tool failures, adversarial instructions, and cases that must be escalated. Define the desired outcome and unacceptable actions before running the tests.
Repeat trials
Because model behavior can vary, run each case multiple times. Measure not only whether the final answer is correct, but also whether the agent selected authorized tools, respected limits, requested approval, and stopped when it should.
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Deterministic checks work for fields, permissions, totals, and state changes. Human review or a carefully designed model grader may be needed for judgment, completeness, tone, or policy interpretation. Review disagreements rather than treating a grader as unquestionable.
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Inspect full traces
Keep the transcript and tool trace: the initial request, instructions in force, decisions, tool arguments, returned data, approvals, retries, handoffs, and final state. A correct-looking final answer can conceal an unsafe or wasteful path, while a failed answer may reveal a fixable integration problem.
How to compare agent offerings
There is no single standardized score that establishes which agent is best. Compare a system against your own representative tasks and risk requirements.
| Comparison question | What to verify |
|---|---|
| What can it complete? | Document the exact start condition, end state, supported exceptions, and tasks that require a person. |
| What can it access? | List connected systems, data scopes, regional restrictions, and whether access is read-only or write-enabled. |
| How are actions approved? | Check per-action confirmation, role-based approval, escalation, cancellation, and rollback options. |
| Can you see what happened? | Look for searchable traces, tool-call logs, error details, retention controls, and export options. |
| How does it perform on your cases? | Run repeated, representative evaluations that include failures and adversarial inputs; do not rely on a vendor’s general demonstration. |
| How do users recover? | Confirm handoff behavior, partial-failure handling, retries, state repair, and a way to stop the agent. |
A practical adoption path
- Choose one bounded workflow. Pick a task with a measurable outcome and a manageable consequence if it fails.
- Map the current process. Identify inputs, decisions, systems, permissions, exceptions, and points where a person must approve.
- Start with read-only tools. Prove retrieval and reasoning before granting write access.
- Write explicit instructions. State the goal, non-goals, source priority, escalation rules, output schema, and stopping conditions.
- Add controls before autonomy. Implement identity checks, least-privilege permissions, input and output validation, approval gates, limits, and logs.
- Create an evaluation set. Include normal, edge, missing-data, tool-error, and prompt-injection cases, then run repeated trials.
- Release gradually. Begin in a sandbox or suggestion-only mode, review traces, and expand permissions only when measured behavior supports it.
- Monitor and revise. Track failures, near misses, escalations, tool errors, and recovery time. Update instructions, tools, and tests as the workflow changes.
What to remember about AI agents
An AI agent is best understood as a model-driven workflow controller: it interprets a goal, selects and uses tools, learns from intermediate results, and acts within human-defined boundaries. Its flexibility makes it a potential fit for contextual, exception-heavy, and unstructured work. It is not automatically more accurate, faster, cheaper, or safer than a chatbot or deterministic automation.
The decisive questions are practical: what task must be completed, what systems and data may be touched, which actions need approval, how failures are contained, and whether repeated evaluations show acceptable behavior. Start narrowly, grant only necessary permissions, expose traces, and keep a person in control of consequential decisions.
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