AI Agent Examples and When to Use Them

An AI agent is a system that can use a model to decide among permitted steps, such as looking up information or calling a tool, while working toward a task. It differs from a fixed workflow, where the application chooses the sequence in advance. The distinction is useful because more autonomy also means more ways for a task to go wrong. Anthropic’s guide to agent patterns distinguishes workflows from agents and recommends matching complexity to the problem.

Four examples with different levels of autonomy

Task	Model’s job	Application’s control	Typical risk to test
Support triage	Suggest a category and draft a reply	A fixed rule routes the result to a queue	Wrong category or invented answer
Document question	Form an answer from retrieved passages	Retrieval is limited to documents the user may access	Missing or irrelevant source
Engineering investigation	Decide which approved read-only checks to run	A tool allowlist and time limit constrain the investigation	Misleading diagnosis or excess tool calls
Account change	Explain the proposed change	A separate authorization and approval step controls the write	Wrong account or unapproved action

These are design patterns, not claims about Venaio customers or completed projects. A system that merely produces a suggestion may be useful without any autonomous write access.

Worked example: routing an incoming request

Suppose a small team receives requests about billing, technical support and general information. First build a fixed process: the model classifies the message, a rule checks that the label is one of three permitted values, and the application sends it to the matching queue. If the model cannot choose confidently, route the message to a human review queue. This is a workflow because the application controls the steps.

Only consider an agent if a later task genuinely requires choosing among actions, such as deciding whether a read-only knowledge-base lookup is needed before drafting a reply. Give it access only to the relevant lookup, limit its number of calls, and require a human to approve any message sent externally. Compare it against the fixed workflow using the same set of sample requests.

What should you measure?

Define the task before choosing a score. For the routing example, check correct queue selection, missed urgent requests, unsupported claims in draft replies and time to human handoff. Include requests with missing information, conflicting instructions and text that tries to override the rules. Keep a small set of cases unchanged between versions so that a change in results is meaningful.

Agent failures often come from the surrounding permissions, not just the model’s wording. OWASP’s excessive agency guidance discusses the risks of giving tools broad functionality, permissions or autonomy. Make consequential actions independently authorized and auditable.

Start with a fixed workflow when the steps are predictable. Add agentic decisions only where the added flexibility has a clear benefit and a safe failure path. An example is not a promise of performance: test the particular data, tools and users your system will encounter.