What are AI agents?
An AI agent is software that uses a model to select actions toward a goal, often by calling tools, inspecting results, and deciding what to do next. A tool might search a knowledge base, retrieve an order, calculate a value, or prepare a change in another application. The surrounding software determines which tools exist and what they are allowed to do.
Anthropic draws a useful architectural distinction: a workflow follows predefined paths, while an agent lets the model direct more of the process. Product names use ‘agent’ in different ways, so inspect the actual behavior. A system that classifies an email and runs one fixed action may be useful without needing an open-ended reasoning loop.
AI agents versus chatbots and fixed automation
A chatbot is an interaction format. It can answer questions without taking action, or it can expose an agent that calls tools. A fixed automation runs a known sequence, such as adding a submitted form to a queue. An agent is most relevant when the system must choose a next step based on new information.
Start by asking whether a simpler rule can finish the job. If every valid request follows the same three steps, explicit code or a workflow can be easier to debug. Consider an agent when requests vary enough that choosing among permitted tools is useful, and when success can still be checked. AWS’s agent overview describes this combination of goals, reasoning, actions, and feedback.
| Approach | Example | Main design question |
|---|---|---|
| Fixed automation | Copy each valid form response into a work queue | What happens if an action fails or repeats? |
| Question-answer assistant | Find a relevant help article and draft an answer | Is the answer supported by the source? |
| Tool-using agent | Inspect a request, choose permitted lookups, and prepare a resolution | Which decisions and actions may it make? |
Real products that illustrate different business uses
Fin is a customer-agent product connected to Intercom’s service offering. Its official site describes service channels and integrations with helpdesks. It is a concrete example to investigate for customer-facing support work. Before buying, verify the supported knowledge sources, handoff behavior, connected actions, and current billing definition for your intended channel.
Zapier Agents is an example of a platform for creating agents connected to business applications and company knowledge. It represents a broader automation route rather than a dedicated support desk. Check the exact actions and permissions for each connector you need, then inspect how activity and failures can be reviewed.
These are documentation-based examples, not a ranking or a hands-on performance comparison. We have not measured either product’s resolution rate, accuracy, or savings for this article. A vendor’s aggregate outcome claim does not establish how the system will perform on your requests.
Four practical workflow examples for a small business
The following scenarios are original design examples, not deployments we operated or measured. Each starts with a useful output a person can inspect. They show how to narrow the task before considering more autonomy. Choose a job with clear inputs and a manageable failure cost for the first pilot.
| Job | Permitted work in the pilot | Human decision |
|---|---|---|
| Support triage | Read approved help content and draft a reply with source links | Approve sending; decide refunds and account changes |
| Lead research | Summarize authorized company information against written criteria | Approve qualification and any outreach |
| Operations review | Compare approved records and list missing information | Approve corrections to the system of record |
| Code maintenance | Inspect a test repository and propose a reviewed diff | Approve merge and production release |
A worked support-triage pilot
Imagine a fictional scheduling SaaS receiving questions about calendar connections. The pilot accepts an authorized support request, searches an approved documentation collection, and produces a draft containing the relevant steps, source links, and any missing information. If the documentation does not establish an answer, the expected result is escalation rather than a confident invention.
Keep the pilot read-only. It does not send the reply, disconnect a calendar, change a subscription, or issue a refund. A reviewer compares the draft with the request and source material. The output should make that review easy: identify the issue, show the evidence, propose the next step, and state uncertainty.
Collect a small authorized evaluation set covering ordinary questions, ambiguous requests, outdated instructions, and attempts to redirect the system through text in a ticket. The task’s instructions and permissions come from the business configuration; text retrieved from a customer message or website is information to evaluate. It does not grant new authority.
Goal: prepare an accurate draft for a calendar-connection question. Inputs: an authorized ticket and the approved help collection. Allowed: search help content and produce a draft with sources. Required: state missing facts; escalate when the sources do not answer. Forbidden in this pilot: send messages, alter accounts, disconnect calendars, issue refunds. Stop: after the configured time, tool-call, or spending limit. Success: reviewer confirms relevance, source support, correct escalation, and no unauthorized action.
Control cost per completed job
Track completed, acceptable outcomes alongside model calls and tool activity. A cheap attempt that needs several retries or lengthy correction can be expensive to operate. Record the task, attempts, input and output usage where available, paid tool calls, elapsed time, and review time. Keep sensitive ticket content out of logs unless it is needed and permitted.
For an original fictional budget, suppose 100 requests average three model calls at an invented $0.01 per call and two lookups at an invented $0.005 per lookup. Direct usage cost is 100 × (($0.01 × 3) + ($0.005 × 2)) = $4. If review averages two minutes per request at an illustrative $30 per hour, review adds $100. These are invented planning inputs, not vendor prices or a savings claim.
Set maximum steps, execution time, retries, and spending in the application or platform controls. A prompt asking the model to be economical is not an enforced budget. Include idempotency for actions that must not repeat, a stop path when tools fail, and a human queue for unfinished work. Use the AI cost-controls guide for a deeper usage-cost worksheet.
Decide whether to expand the pilot
Define acceptance before reviewing results: the draft answers the actual question, each important instruction is supported, missing information is handled correctly, and no prohibited action occurs. Track escalation quality as well as answer quality. A system that refuses every request may be safe within its boundary but unhelpful for the job.
Compare the pilot with the existing process using a comparable request mix. Include setup, maintenance, review, and correction effort. Expand one permission at a time only when the evidence supports that change, with a clear owner and a way to stop execution. The coding tools guide applies the same evaluation logic to repository work.
Download the AI agent pilot template
The template includes a task brief, tool and permission inventory, evaluation cases, a cost log, and a rollout decision. Product and architecture sources were checked on October 26, 2026; verify current connector behavior and account eligibility before adopting a platform.