# What is AI Agent?

> Software that plans, uses tools, and acts toward a goal instead of answering one prompt.

Canonical: https://www.decimal.app/glossary/ai-agent · Last reviewed: 2026-09-23

An AI agent is a system built on a language model that can break a goal into steps, call tools such as search, code readers, or APIs, observe the results, and decide what to do next. A chatbot replies to a message; an agent works a problem until it's done or it needs a human.

## Why it matters

Most technical tickets can't be answered from a single document. Someone has to check logs, read the code path, and compare account config. Agents can do that legwork at machine speed, which is where support time actually goes.

## Best practices

1. Scope the tools. Give the agent read access to the systems a human investigator would open, and nothing it doesn't need.
2. Require citations. Every claim should link to the log line, commit, or doc it came from so a reviewer can check it in seconds.
3. Keep a human gate on actions. Let the agent investigate freely, but route anything that changes code or customer data through approval.

## How Decimal helps

Decimal's agents investigate customer issues across your code, logs, tickets, and docs, then answer the customer, hand engineering a cited root cause, or open a fix for review.

## Related terms

- [Agentic Investigation](https://www.decimal.app/glossary/agentic-investigation): An AI agent working a ticket the way an engineer would: hypothesis, evidence, conclusion.
- [Human-in-the-Loop](https://www.decimal.app/glossary/human-in-the-loop): A design where people review or approve an AI's work at key points.
- [Model Context Protocol (MCP)](https://www.decimal.app/glossary/model-context-protocol): An open standard for connecting AI models to tools and data sources.
- [Retrieval-Augmented Generation (RAG)](https://www.decimal.app/glossary/retrieval-augmented-generation): Having an AI model look up relevant documents before generating an answer.

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