See what each agent reads, decides, does and hands back to a person. Then use the selection checklist to choose a sensible first workflow.
Key takeaways
- Choose a complete workflow with a clear owner, input, output and success measure.
- Use deterministic automation for fixed rules; use an agent only where interpretation or tool choice adds value.
- Keep consequential actions behind explicit approval and record every tool call.
- Start read-only, test failure cases, then add narrow write permissions.
- A smaller reliable agent is more useful than an impressive demo with unclear boundaries.
What makes an example a real AI agent?
An AI agent is not simply a chatbot that writes an answer. It uses a model to direct parts of a workflow and chooses tools that retrieve information or take action. OpenAI describes the basic components as a model, tools and instructions. The system also needs permissions, state, checks and a clear finish condition when used for real work.
The important test is ownership of a task. “Summarise this email” is a model request. “Read a support email, identify the account, check the order, draft a policy-grounded answer, request approval when a refund is involved, update the ticket and stop” is an agent workflow.
Figure 1. Anatomy of a bounded agent use case
Text equivalent: a request triggers the agent; the agent reads trusted context; it selects approved tools; sensitive actions pause for human approval; the final result and tool history are logged. Missing data, tool errors and policy exceptions follow an escalation path instead of being hidden.
Which AI agent use cases are practical?
1. Inbound lead qualification
Input: form, email or chat enquiry.
Agent work: extract needs, check the qualification rubric, ask for missing details and prepare a CRM entry.
Tools: product knowledge, CRM search and CRM draft/update.
Approval: a person approves pricing, unusual promises and outbound messages until the workflow is proven.
Output: evidence-backed qualification, owner and next step. Reject prompt instructions found inside copied lead content.
2. Customer support triage
Input: new ticket plus account identifier.
Agent work: classify the issue, retrieve policy and order context, draft an answer and route exceptions.
Tools: help centre, order system and ticketing platform.
Approval: refunds, credits, account restrictions and policy exceptions.
Output: grounded reply, category, priority and audit trail. On missing records, the agent asks for help rather than guessing.
3. IT request reviewer
Input: employee request for software or access.
Agent work: compare the request with approved tools, security requirements and role policy; collect missing evidence.
Tools: software catalogue, identity directory and ticket system.
Approval: every permission change and unapproved application.
Output: documented recommendation or routed approval request. The agent never grants itself broader access.
4. Meeting preparation agent
Input: calendar event and attendee list.
Agent work: find recent approved records, compile open questions and draft a briefing.
Tools: calendar, CRM, project tracker and document search.
Approval: none for a private draft; required before sending externally.
Output: a cited briefing with unresolved conflicts flagged. This is often a safe first agent because the primary output is reversible.
5. Document intake agent
Input: uploaded application, invoice or client packet.
Agent work: identify document type, extract defined fields, validate format and list missing items.
Tools: file store, extraction service and case database.
Approval: low-confidence extraction and any legal, financial or eligibility decision.
Output: structured record, source references and exception queue. Retention and access follow the organisation's privacy rules.
6. Research and evidence agent
Input: research question, source criteria and deadline.
Agent work: search approved sources, extract claims, compare evidence and produce a cited draft.
Tools: web search, internal search and document editor.
Approval: a subject expert reviews high-stakes claims and publication.
Output: claim ledger, sources, uncertainty and report. Conflicting evidence remains visible.
7. Metrics reporting agent
Input: schedule and agreed metric definitions.
Agent work: retrieve data, validate completeness, calculate predefined measures and explain notable changes.
Tools: analytics, spreadsheet or warehouse, plus reporting destination.
Approval: changes to metric definitions and any external distribution.
Output: dated report with data period, formula notes and missing-data warnings. The model should not invent causes from correlation.
8. Feedback-to-product agent
Input: support tickets, surveys and approved community feedback.
Agent work: group related signals, extract examples, count evidence and draft candidate product issues.
Tools: feedback sources and issue tracker.
Approval: product staff decide priority and roadmap.
Output: themes linked to source evidence, not an autonomous product decision.
9. Content production agent
Input: approved brief, claims and brand rules.
Agent work: research, draft, check claims, adapt formats and prepare a review package.
Tools: source search, content repository and QA checks.
Approval: publication, claims, regulated topics and brand-sensitive messages.
Output: reviewable drafts with sources and change log. It must not fabricate customer stories or performance numbers.
10. Operations exception agent
Input: failed workflow, overdue task or service-level alert.
Agent work: inspect logs, identify the known failure class, gather context and suggest the permitted recovery step.
Tools: workflow logs, runbooks and task system.
Approval: destructive retries, customer-impacting actions and policy overrides.
Output: recovered run or a concise escalation packet. It stops after the retry limit instead of looping indefinitely.
How do these examples compare?
| Use case | Why AI helps | Risk | Good first version | Primary measure |
|---|---|---|---|---|
| Lead qualification | Interprets varied enquiries | Medium | Draft and route only | Correct routing rate |
| Support triage | Combines language and account context | Medium | Draft replies; no refunds | Resolved or correctly escalated |
| IT requests | Reads policies and missing evidence | High | Recommendation only | Policy-compliant routing |
| Meeting preparation | Searches scattered context | Low | Private briefing | Source coverage |
| Document intake | Extracts from varied files | Medium–high | Extract and flag | Field accuracy and exception recall |
| Research | Searches and synthesises evidence | Varies | Cited draft | Claim support rate |
| Metrics report | Explains structured results | Medium | Internal draft | Data and formula accuracy |
| Feedback analysis | Groups unstructured signals | Low | Evidence-linked themes | Traceable source coverage |
| Content production | Coordinates research and formats | Medium | Draft package | Factual and editorial pass rate |
| Operations exceptions | Interprets logs and runbooks | High | Diagnose and suggest | Safe recovery or escalation |
How do you choose your first AI agent?
- Name one result. “Handle support” is too broad. “Classify new tickets and draft policy-grounded replies” is testable.
- Map the current process. Record the trigger, inputs, systems, decisions, exceptions, owner and final output before choosing tools.
- Check whether AI is necessary. If stable rules can solve every case, build a normal workflow. Add a model where language, documents or changing context require interpretation.
- Start with reversible actions. Reading, drafting, categorising and preparing records are safer than sending, deleting, paying or changing permissions.
- Define source authority. State which database, policy or document wins when sources conflict, and how freshness is checked.
- Set approval and stopping rules. List the actions that need a person, the retry limit and the conditions that end the run.
- Create the test set first. Include ordinary cases, ambiguity, missing data, conflicts, tool outages, duplicate requests and prompt injection.
- Measure the workflow outcome. Track correct completion and escalation, not the fluency of the agent's prose.
When should you not build an agent?
Do not use an agent merely because the label sounds modern. Anthropic recommends adding agentic complexity only when simpler solutions fall short. A fixed workflow is easier to predict, test and operate when the steps and rules are stable.
Keep these decisions with people
An agent may collect evidence or prepare a recommendation, but it should not autonomously diagnose a patient, give personalised legal advice, make a hiring decision, approve credit, sign a contract, transfer money or change critical access. These actions affect rights, health, employment, security or major financial outcomes. Use qualified human review and the policies that apply to your organisation and jurisdiction.
Also reject a use case when the data source is unreliable, no one owns the outcome, success cannot be measured, permissions cannot be limited or the process itself is not understood. Automation will reproduce unclear operations at greater speed.
How should an agent fail?
A safe failure is visible and bounded. The agent records what it attempted, preserves the last verified state, stops after a defined retry limit and gives a person enough context to continue. It does not quietly replace missing facts, ignore a denied permission or claim that an action succeeded without a tool result.
| Failure | Agent response | Human receives |
|---|---|---|
| Required field missing | Ask one focused question | Case only if the user cannot answer |
| Sources conflict | Show the conflict; do not choose silently | Both sources and timestamps |
| Tool permission denied | Stop the write action | Requested action and denied scope |
| Tool timeout or uncertain write | Check state before retrying | Attempt ID and current state |
| Prompt injection in content | Treat it as untrusted data | Flagged content and intended task |
| Approval rejected | Keep the action unexecuted | Draft, evidence and rejection reason |
| Retry limit reached | Stop the run | Logs, errors and recommended next check |
How do you test an agent example before deployment?
Turn each use-case card into an evaluation set. Test correct tool selection, grounded data, permissions, state changes and the final business result. Include failures deliberately. An agent that works only on the demonstration request is not ready for a real workflow.
Criteria for success
- The agent completes the defined normal case using only approved sources and tools.
- Every factual field can be traced to a source or tool result.
- No sensitive action occurs without the required approval.
- Duplicate input does not create a duplicate external action.
- Missing data and conflicts produce a useful question or escalation.
- Tool failures preserve state and stop after the configured limit.
- Prompt injection inside documents, emails or web pages cannot change the agent's permissions.
- The owner can review logs and reproduce why the agent acted.
Learn to turn an example into a working system
Barcelona Code School's AI Agent & Automation Bootcamp focuses on connected business workflows: data, tool-using agents, approvals, RAG, QA, reliability and deployment. It fits people who want to build complete automations with n8n and test them against real failure cases.
If you only need one fixed form-to-spreadsheet automation, a short tutorial may be enough. Use the live course page for the current format, dates and tuition.
Explore the AI Agent & Automation BootcampFrequently asked questions
What is a practical example of an AI agent?
A practical AI agent can qualify an inbound lead by reading a form, checking approved company criteria, gathering missing details, preparing a CRM record and routing the lead to the right person. A human should approve consequential outreach or pricing decisions.
What is the best first AI agent for a small business?
The best first agent owns one frequent, bounded and reversible workflow with clear source data. Good candidates include request triage, meeting preparation, document intake or draft reporting. Avoid starting with payments, hiring decisions, medical advice or unrestricted outbound messages.
When should a business use automation instead of an AI agent?
Use deterministic automation when inputs are structured, rules are stable and every path can be defined in advance. Add an AI agent when the work requires interpreting unstructured information, choosing among tools or adapting the next step to context.
Which AI agent actions should require human approval?
Require approval for external messages, payments, refunds, contracts, access changes, destructive edits and decisions that affect employment, health, legal rights or significant financial outcomes. The exact boundary should follow the organisation's policy and risk level.
How do you test an AI agent use case?
Create a test set with normal requests, missing data, conflicting instructions, tool failures, permission failures, prompt injection attempts and repeated requests. Check task success, factual grounding, correct tool use, approval compliance, recovery and safe stopping.
Sources
- OpenAI: A practical guide to building agents — agent definition, use-case criteria, tools, instructions, guardrails and human intervention.
- OpenAI: Solutions for agentic workflows — first-party examples across lead qualification, IT, content and feedback.
- Anthropic: Building Effective Agents — architecture selection and the case for starting simply.
- Anthropic: Trustworthy agents in practice — human control, security, transparency and privacy risks.
- Barcelona Code School: How to Build an AI Agent — the canonical step-by-step build guide.
- Barcelona Code School: AI Agent & Automation Bootcamp — current course information.