Automation type

AI agent examples

An AI agent is software that uses a language model to understand a request, decide what to do and act on it across connected systems, within rules the business sets.

examples
161
simple
41
complex
120

Every example

All AI agents examples

Simple = one job, one path. Complex = several moving parts working together.

161 examples shown

About

About AI agents

Unlike a fixed script, an agent can read an unstructured email, classify a document, draft a reply or choose the next step in a process. Businesses use AI agents to answer repetitive customer questions, extract data from documents, qualify inbound leads, summarize calls and prepare drafts for staff to approve. Typical results include faster first responses, a large drop in manual data entry and staff who spend their time on exceptions instead of routine work. Mirapps builds agents on models such as those from OpenAI and Anthropic, grounds them in the business's own documents and data, limits what each agent is allowed to do, logs every action and routes uncertain cases to a person for review.

FAQ

Common questions

How is an AI agent different from a chatbot?

A traditional chatbot follows a fixed decision tree and fails when a customer goes off script. An AI agent understands free text, looks up information in connected systems and can take actions, such as creating a record, booking a slot or drafting a document. It works inside limits the business defines and escalates anything outside them to a person.

How do you stop an AI agent from making mistakes?

We limit each agent to specific tasks and data, give it the business's own documents as its source of truth and set confidence thresholds. Low-confidence answers, sensitive topics and any action with financial or legal impact go to a person for approval. Every action is logged, so the team can review what the agent did and why.

Is our data safe when an AI agent processes it?

We use business-tier model APIs, where under the providers' terms the data sent for processing is not used for training, and we pass only the fields each task needs. Credentials stay in secured connections, access is scoped per system and sensitive fields can be masked before they reach the model. Logs can be kept in the business's own environment.

What do we need to prepare before building an AI agent?

A clear description of the task, a set of real examples such as past emails, documents or questions with the correct answers, and access to the systems the agent should read or update. We use the examples to test accuracy before launch, then start with a narrow scope and widen it as the results hold up.

Thinking about AI agents?

We'll map where it fits in your business, what it connects to and what it typically saves, in a free 30-minute call.