> ## Documentation Index
> Fetch the complete documentation index at: https://benchgen.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Agent Concepts

> Understand what an agent is, how it works, and when to use one.

# What Is an Agent?

An **Agent** is an autonomous, goal‑driven AI assistant that can understand a request, decide the next best step, run one or more Actions, and return a grounded, useful result. Unlike a single prompt (which just predicts text), an Agent performs a mini workflow: classify intent into a Topic, plan or ask clarifying questions, invoke Actions securely, iterate, validate, and respond.

Mental model: **Message → Topic → Reason / Act Loop → Validate → Response**.

***

## Core Capabilities

* Interprets free‑form natural language (not just buttons / rigid menus).
* Chains tasks: gather data, transform, update, summarize, draft content.
* Maintains short recent conversation context for coherent follow‑ups.
* Applies guardrails: scope limits, confirmation steps, identity checks.
* Improves over time as you refine Topics, Actions, and instructions.

***

## Building Blocks

1. **Agent** – Container (persona, collection of Topics, config).
2. **Topics** – Job definitions: when to engage, boundaries, rules, allowed Actions.
3. **Actions** – Executable units (API call, function, workflow, data lookup, generation) the Agent can run inside a Topic.
4. **Reasoning Engine** – Orchestrates classification, tool choice, multi‑step loops.
5. **LLM** – Language reasoning core used for understanding, decision steps, and drafting responses.

If any piece is vague (especially Topic scope), reliability drops—tighten definitions first instead of adding more features.

***

## How a Message Flows

1. **Input** – User (or system trigger) sends a question / command.
2. **Topic Selection** – Recent turns + each Topic's name & classification description are compared; best fit chosen (or off‑topic fallback if none match).
3. **Reasoning Loop** (repeat up to a small cap):
   * Decide: run an Action, ask for missing info, clarify ambiguity, or answer directly.
   * If an Action runs: execute → map outputs → append to context.
   * Check if goal satisfied; else iterate.
4. **Validation** – (Optional) ensure response is in‑scope, grounded, policy‑safe. Regenerate or escalate if it fails.
5. **Response** – Stream or send the final answer and wait for the next turn.

Lever: precise Topic instructions strongly shape step 3 (what to ask first, which Action to prioritize, when to stop).

***

## Agents vs Scripted Bots

| Aspect             | Agent                             | Scripted Bot                   |
| ------------------ | --------------------------------- | ------------------------------ |
| Conversation style | Adaptive, free‑form               | Predefined flows / branches    |
| Intent handling    | Semantic (LLM classification)     | Rules, keywords, narrow model  |
| Tool use           | Dynamic, multi‑Action plans       | Usually fixed single flow      |
| Adaptation         | Adjust by refining Topics/Actions | Manual flow editing            |
| Best for           | Varied, messy tasks               | Deterministic, regulated steps |

Choose a scripted bot when strict, auditable, unchanging flows dominate. Choose an agent when variability, blended context, and multi‑step reasoning matter.

***

## Common Use Cases

* Resolve support inquiries end‑to‑end (lookup → policy check → answer).
* Summarize records (case, ticket, lead) + propose next actions.
* Draft outbound messages (email, notification, follow‑up).
* Orchestrate data workflows (fetch → validate → update → summarize result).
* Internal analyst: explain KPIs, highlight anomalies, surface risks.

Start narrow (one high‑value task). Broad first scopes cause misclassification noise.

***

## New Topic or New Action?

Create a **new Topic** when: intent category or decision rules differ meaningfully, or a current Topic would need many conditional branches.

Create a **new Action** when: same intent category but you need a different external operation / side effect.

Avoid: catch‑all Topics, mega Actions doing unrelated jobs—they reduce transparency & testability.

***

## Narrative Example

A "Support Assistant" has Topics: Password Reset, Order Status, Return Eligibility.

**Step-by-step flow:**

1. User: "Where's my package?"
2. Classifier picks Order Status Topic
3. Instructions require `order_number` or identifying email fragment
4. Agent asks if missing information needed
5. Runs `LookupOrderStatus` Action
6. Normalizes carrier + ETA data
7. Replies with concise status
8. Offers follow-up ("Need to place a hold or start a return?")

***

## Next Steps

* Build or configure → [How to Create an Agent](/agentspace/how-to-create-agent)
* Define job units → [What is a Topic](/agentspace/topic-concepts)
* Add capabilities → [What is an Action](/agentspace/what-is-an-action)

Ship a small, well‑bounded agent; observe interactions; iteratively tighten definitions. Precision wins over breadth early on.